Tommy Huang Portfolio by Tommy Huang

The text is not merely a tool for judging the legitimacy of interpretation, but an object gradually established in the process of justifying itself -- Umberto Eco 2021 PORTFOLIO YE HUANG

CONTENT YE HUANG Tel: 15851628800 E-Mail: 392057135@qq.com

EDUCATION 2018.9-2020.5 : University of Pennsylvania, Stuart Weitzman School of Design MSD-AAD

ACADEMIC PROJECT

Architect/LEED AP BD+C

FUTURE AIRPORT | PHILADELPHIA TERMIANL T5 未来机场 | 费城航站楼 T5 设计

ELEGANT COMPOSITION | NYC JFK AMAZON TERMINAL 优雅之歌 | 纽约 JFK 亚马逊货运航站楼设计

ZHALANTUN NARRATIVE |INDSUTRIAL MEMORY MUSEUM 扎兰屯故事 | 扎兰屯工业记忆博物馆设计

THE FILM OF PINGYAO | HISTORICAL FACTORY RENOVATION 平遥之影 | 平遥老厂房改造公共电影宫设计

ACADEMY LINEAGE |TSINGHUA UNIVERSITY HIGH SCHOOL 学院传承 | 清华大学附属中学郑东分校规划设计

SILK ROAD | XINJIANG R&D URUMCHI CENTER DESIGN 丝路之上 | 新疆乌鲁木齐产业技术研究中心设计

TEXTILE MEMORY | HUAFANG WOOLEN MILL RENOVATION 纺织记忆 | 华芳集团毛纺厂改造施工项目

2D-3D FORM ENCODING WORKFLOW BASED ON STYLEGAN 基于深度学习 STYLEGAN 网络的建筑 2D-3D 形体编码及生成工作流

XHOME-BIM PREFABRICATED HOUSING SYSTEM XHOME-BIM 预制装配式住宅系统

POLYFRAME FORCE DIAGRAM DATASETS AUGMENTATION BY VAE 变分自编码器 (VAE) 在 POLYFRAME 力学图解原型数据集扩充的应用

2013.9-2018.6: North China University of Science and Technology Bachelor’s Degree,Architectural School, GPA: 3.78/4.0, ranked 1 of 42 students

2020.5-Present PM

Suzhou Jiaxie Construction Engineering Co. Ltd

2019.5-2020.5 RA

UPenn PSL Lab

2017.5-2018.6 Architect

Tsinghua Architectural Design and Research Institute

2016.7-2017.5 Intern

Stone Design Consultant Inc., Beijing Headquarter

HONORS Xinjiang Innovation and Entrepreneurship Competition, Finalist(2017) Xinjiang [future ARQUITECTURAS] Architecture Competition Honorable Mention(2015) NCST Outstanding Graduate(2018)

PROFESSIONAL PROJECT

PROFESSIONAL EXPERIENCE

National Scholarship (2015 Fall Semester, 2016 Fall Semester) The First Prize in University Scholarship (7 times); The Second Prize in University Scholarship (2 times)

Software: Rhino, GH, Maya, Revit, ArchiCAD, Adobe Series, C#, Python, Html/ CSS/ JS Fluent English Speaker: TOEFL 106; GRE: 321+3.5

RESEARCH PROJECT

PROFESSIONAL SKILLS

FUTURE AIRPORT | PHILADELPHIA TERMIANL T5 未来机场 | 费城机场 T5 航站楼设计

Site: Philadelphia, PA, USA Studio(MSDAAD) Project | 2019 Spring Instructor: Masoud Akbarzadeh Partner:Yuchen Liu

项目位置 : 费城 , 宾州 , USA Studio 项目 (MSDAAD) | 2019 春指导老师 : Masoud Akbarzadeh 搭档 : 刘禹辰

Airport Design Based on 3D Graphic Statics One of the most important cities in the United States, Philadelphia has a long history and important political and economic status.The city's gateway, however, has long been criticized for its poor design.The chaotic streamlines and outdated image do not match Philadelphia's ridership needs and city status.As a result, rebuilding/updating Philadelphia Airport has become a hot topic of discussion. Dr.Masoud Akbarzadeh is a professor with a background in architecture and architecture. He has a unique and in-depth understanding of the combination of structure and architecture.He developed a set of plug-in Polyframe, which can be used for structure generation design. It can reverse the static structure by designing 3D static graphic prototype, which has important guiding significance for guiding reasonable and new spatial static structure.The project is based on the plugin to explore the possibility of using it to lead the skeleton design of large span buildings, such as airports.After designing the mechanical prototype according to the requirements and obtaining the structural prototype, we further developed the results and designed the final large-span airport.

未来机场 | 费城航站楼 T5 作为美国最重要的城市之一 , 费城拥有悠久的历史和重要的政治与经济地位。然而，作为城市大门的费城机场长久以来因其糟糕的设计被人诟病。混乱的流线和过时的形象无法与费城的客流需求和城市地位相匹配。也正因此 , 重建 / 更新费城机场已经成为了一个热门讨论话题。 Dr.Masoud Akbarzadeh 是一位有着结构和建筑背景的教授，对于结构和建筑的结合有着独到而深入的理解。他开发了一套可以用于结构生成设计的插件 PolyFrame, 可以通过设计 3D 静力学图解原型 , 反向推倒静力学结构 , 这对于指导合理而又新型的空间静力结构有着重要的指导意义。该项目基于此插件，探索在大跨度建筑，例如机场设计中应用此插件主导建筑骨架形体设计的可能性。在根据需求设计力学原型并获得结构原型后 , 我们对成果进行深化并设计了最后的大跨度机场。

01_ Flat Curve

02_ Triangle Structure

03_ Hexagon

04_ Center Node

05_ Triangle with Center

06_ No Member

07_ Bridge

08_ Rectangle

09_ Tetrahedron _ Center Hole

10_ Rectangle with Center

11_ Cross

12_ Circle

Subdividing the global force polygon in two-dimensional graphic statics is another technique to derive various funicular forms for given boundary conditions.The “external” polygon, representing global equilibrium, is subdivided into smaller polygons using various subdivision schemes.

By laying out the most basic feature the plugin can generate, we can look up features in this 'dictionary'. The future form development is highly based on these simple and efficient basic structure. Combining different subidivision techniques, a simple and clean prototype can be transfrom into amazing forms. In 3D Grahpic Statics, any cell decomposition of the global force polyhedron that is closed and has planar faces, can represent the equilibrium of a spatial funicular form. Recursive barycentric subdivision of the global force tetrahedron results in various structural forms preserving the same boundary conditions.

Spatial funicular forms can be derived by subdividing not only the external force polyhedron, but also the internal cells of the force diagram. Consider the form and force diagrams of the compression-only branching structure.

The exploded force diagram and corresponding branching form diagrams after subdivision. The areas of highlighted faces in the polyhedral force diagram represent the magnitude of forces in the highlighted edges of the form diagram.

_ Tetrahedron _ Center Hole

0_Triple Pyramid

_ Extruding Form

0_ Column with Enlarged Ends

_ Column Force _ Center Hole & Top Layer

0_ Column & Platform

_ Column Bridge _ Flat Combination

0_Triple Bridge

_ Continues Curvature _ Complex Interior

0_ Double Column

_ Extrude Form _ Too many side forces

0_ Flower

Forms shown above show the study in design process. We try to create some efficient and brand-new forms. Sometime it is hard to coordinating these two element. For instance, the last flower form which is asethically pleasing. However, the too much side forces make the form hard to realize

Detailed Compression-Only Values

At the end of the form, because of the inevitable sides forces, there will need specific constrain to eliminate the side forces. We successfully decreased the volume(value) of the polyhedral side force at the begin of the design process

As the base and the direct connection to the ground, the root members will have to take more pressures.

Our final prototype starts from a extremely simple force diagram. The first column force diagram shows some simple features such as extruding and pipe. What's challenging is the subdivision process. Based on the requirement of the new airport, we add new features to the force diagram and finally got the four sides form shown in the right.

Because of sufficient subdivision around the central circle space, the compressedonly force is share by many small members.

Slow Route-Pedestrain Circulation SITE PLAN

Slow Route-Drone Circulation

12 A340-200

A340-200

A340-200 A340-200

A321-100 A321-100

A340-200

9 2

A321-100 A321-100

7 A340-200

A340-200

A321-100 A321-100

4 A321-100 A321-100

A321-100 A321-100

A340-200

A321-100 A321-100

A340-200

A321-100 A321-100

A340-200

A321-100 A321-100

A340-200

A321-100 A321-100

A340-200

25 24

The existing Philadelphia has lots of problems such as the inconvenient circulation and terrible architectural form.

Through the plug-in, we can extend the ordinary two-dimensional Prototype

Site Plan

100m

Second Floor Plan

The final airport has an efficient and beautiful structure form system. In our design, we wrap the interior with a complete curved surface to create the feeling of 'heavy.' On the other hand, we can see the outermost layer of the building structure from the outside, which is 'light.' We hope to enhance the airport's design feel with this exciting contrast between inside and outside.

The way of boarding is also different. As the force diagram shows before, we design the force diagram based on the requirement. As we want to provide new boarding experience and combine the boarding with the existing structure, the passengers will get on the plan from the four hole-shape gates at the second. Other programs are mainly on the third floor.

The new mode of transportation gives new airports unlimited possibilities. The airport is no longer confined to a vast single building. Instead, due to the existence of drones and new boarding modes, each terminal becomes more independent. Streamlines are clearer. All of this provides a better airport experience and better traffic efficiency

The new airport will be connected with the underground directly. People can take off at different gates and go straight up to check-in. People who take the drone can land on the top of the terminal and take the central elevator to their gates. Passengers' luggage goes underground and will transport to the ground directly.

27.00m

21.00m

15.00m 9.00m

-0.00m

-6.00m

-10.00m

ELEGANT COMPOSITION | NYC JFK AMAZON TERMINAL

优雅之作 | 纽约 JFK 亚马逊货运航站楼设计

Site: JFK, New York, USA Studio(MSDAAD) Project | 2018 Fall Instructor: Ali Rahim Partner: Lingyun Yang, Yifei Wang

项目位置 : 美国 , 纽约 , JFK Studio 项目 (MSDAAD) | 2018 秋指导老师 : Ali Rahim 搭档 : 杨灵运 , 王艺斐

Advanced Logistics + Boarding Termianl Design A new airport typology can help sustain New York City's financial global leadership in the world. At the same time, the rise of technologically driven retail giants such as Amazon uses distribution networks that channel materials and goods to remote locations that are delivered through existing delivery networks including DHL, USPS, UPS, and FedEx. The airport terminal that deals with passengers and cargo will contribute towards reducing the disparity between cities vying for financial global leadership and New York City. The new terminal will increase the relevance of JFK as a business and logistics hub making a stronger connection to global centers and strengthening development locally. The project is located at JFK Airport.We deconstruct and study the streamline and aesthetic logic of the TWA terminal in New York designed by Ero Saarinen, the architect of the last century.Design a new portal hub.It will serve as the gateway to the airport, streamlining the passenger and cargo terminals within the terminal, providing a new architectural typology for the airport development in New York City.

先锋货运物流 + 人行登机航站楼设计新的机场类型有助于维持纽约市在全球金融领域的领导地位。与此同时，由技术驱动的零售巨头的崛起，如亚马逊，利用分销网络将材料和商品运送到遥远的地点，并通过现有的递送网络，包括 DHL, USPS, UPS 和联邦快递。负责客运和货运的机场航站楼将有助于缩小争夺全球金融领导地位的城市与纽约市之间的差距。新的航站楼将增加 JFK 作为一个商业和物流枢纽的相关性，使其与全球中心更紧密地联系在一起，并加强当地的发展。项目地点在 JFK 机场。我们以上世纪建筑大师 , 埃罗沙里宁设计的纽约 TWA 航站楼为研究对象 , 解构并学习该航站楼的流线及美学逻辑 . 从而设计一个新的门户枢纽。它将成为机场的入口门脸，在航站楼内部，乘客流线并将与货运终端有机结合，为纽约市的机场发展提供一个全新的建筑类型。

Prototype Research Regarding Eero Saarien's TWA terminal as our preference. From its openning in 1960s, TWA Flight Center was more than a functional terminal, it was a monument to the airline and a sensational space for passenges to experience.In the last decade, Amazon has grown to be the largest online retailer. A large part of Amazon's success is its very sophisticated and increasingly automaated logistic system which applies matrix sorting and conveying robts with great efficiency and accuracy.

Wing

TWA Exterior Space Feature

TWA Interior Space Feature

Shell

Column

Bifurcation

Dome

Spiral

Pipe

Smooth Curve

Amazon Logistic System Prototype (Overview)

Amazon Logistic (Interior)

SKIN SYSTEM

AMAZON Logistics

Shelves

Lift

Robotic Arm

Conveyor

Amazon Logistic System Prototype

FORM PROTOTYPE 1 FORM PROTOTYPE 2 By combining the main features of interior space in TWA with Amazon's new automated logistic system, we tried to created a new prototype for future airport which will serve both for people and cargos. It will also bring a different experience for passengers when they move in the airport seeing the motion of machines and cargos delivered by conveyor system.

LOGISTIC SYSTEM

TRUNK

CHUNK RENDERING

TRUNK

PEOPLE

AXONOMETRIC EXPLOSION

By applying the prototype in architecture ,we create a future airport for both human and cargo. Passenger flow and cargo flow are separated but weaved together. The diagram below show how these two systems organize in one space and interact with each other.

INTERIOR LOGISTIC SYSTEM

28.00m

20.00m

14.00m

7.00m

-0.00m

EAST ELEVATION

28.00m

20.00m

FIRST FLOOR 14.00m

7.00m

-0.00m

-8.00m

SECTION 1

ll ll

28.00m

20.00m

14.00m

7.00m

-0.00m

-8.00m

SECTION 2

SECOND FLOOR

INTERIOR STREET VIEW

STREET VIEW

ZHALANTUN NARRATIVE| INDUSTRIAL MEMORY MUSEUM

扎兰屯故事 | 扎兰屯工业记忆博物馆设计

Site: Zhalantun, Inner Mongolia, China Studio Project | 2017 Fall Instructor: Brian Wong Individual Work

项目位置 : 中国 , 内蒙古 , 扎兰屯 Studio 项目 | 2017 秋指导老师 : Brian Wong 个人作品

REGIONAL MULTIFUNCTIONAL MUSEUM As the best witness, the first task of museums is trying to keep the last glimpse of history. Ironically, because of some characteristics of the museum, it often becomes an eternal tombstone or the second ruin. Zhalantun once lived with the most booming Industrial period in China with the most advanced technology help from the Soviet Union. However, influenced by the political change in recent decades, the situation changed dramatically. Most of the railways merged into the wild grass, the all the warehouses and the train factories became ruins. Declining of permanent population, lower vitality also make the city suffers a lot. How to use the new museum to meet the necessary exhibition features and make it become the city's activation point is the key in this project.

地域性的多功能博物馆作为最好的见证人，博物馆的首要任务是保持对历史的最后一瞥。具有讽刺意味的是，由于博物馆的某些特点，它往往成为一个永恒的墓碑或第二个废墟。在苏联最先进技术的帮助下，扎兰屯一度成为是中国工业最繁荣的城市之一。然而，受近几十年来政治变化的影响，情况发生了巨大变化。大部分的铁路都消失在荒野中，所有的仓库和火车工厂都成了废墟。常住人口的减少、活力的降低也使城市遭受很大的损失。如何利用新博物馆满足必要的展览特色，使之成为城市的活化点，是这个项目的关键。

SITE ANALYSIS &CITY HISTORY

Mid-East Railway Museum

Railway residence

Dinning Hall

Existing Office

Local Residence

Russian Church

This museum is located at the south-west side of the train station in the city of Zhalantun. The north of the site is the centre plaza of this city. And on the southeast hand, a small church and The priest lounge stand at the site area. There are many preserved historic buildings surrounding. These traditional architectures in Russian style were brought here by Russian immigrants who had settled down since the rail construction. The famous rail line at the northeast side is the Middle East Line, which belongs to the huge Siberia Line from Russia to Northeast China . This rail line was built in 1890s with the total length of 2500km. This rail construction not only brought the large population of immigrants but also gave a hand to the rise of cities and towns along the rail line. Those historic architectures were used as the infrastructure including the bus station, working area, locomotive sheds, churches, clubs, hospitals, schools, barracks and a mess of railway residences. The main design purpose of the museum is to show the city development for the decade without disturbing the existing cultural atmosphere. By visiting this museum, the citizens could learn the industrial and cultural stories about this city.

First Floor Plan

Second Floor Plan

The space feature of the museum is in the transformation of each exhibition hall. Vertically, the visitors circulation are separated into three levers but connected by the sloped city viewing corridor and the indoor carriage gallery, which make the different and individual exhibition hall stands in line and become a visiting loop. 1: CINEMA 2: CHURCH 3: GALLARY 4: CITY VIEWING CORRIDOR 5: HALLWAY 6: TICKET BOX & SHOP 7: STOREROOM 8: OFFICE 9: ROOM GARDEN 10: TOILET 11: SUNKEN SLOPE 12: TRAIN GALLARY

VIEWING PLAFORM

SUNKEN PLAZA

SECOND FLOOR PLAN

VIEWING PORT

fvasdfsfsfasdf FIRST FLOOR PLAN

FRONT SQUARE

The museum totally changed the centre part of the city, from a closed narrow block to a multifunctional city culture plaza. The museum is no longer just a showcase full of the historical treasure of the past but also moving the first step of leading the city to a bright future through speeding up the transfering process. The museum plaza itself is like a documentary film directed specifically for the city and played on its own facade, watched by the people and the city in the open air.

NEW VIEWING This high raised museum does not block out the sight for people at the center plaza watching the church and other historic buildings, but strengthen the centripetal force pulling the street to this area. The shape of the museum seems to a huge roof which protect visitors from rains and sunshine. It is also like a giant sculpture assembled by steel frame and train carriages which expresses the urban feature of cultural history from the aspect of view and space.

THE FILM OF PINGYAO | HISTORICAL FACTORY RENOVATION

平遥之影 | 平遥老厂房改造公共电影宫设计

Site: Zhengzhou, Henan, China Design Commission | 2018 Spring Instructor: Yirui Lian Team Member: Brian Wong, Yu Zhou etc.

项目位置 : 中国 , 河南 , 郑州设计委托 | 2018 春主创设计师 : 廉毅锐搭档 : Brian Wong, 周宇等

RENOVATION OF THE MAIN VENUE OF INTERNATIONAL FILM EXHIBITION IN DIESEL ENGINE FACTORY OF PINGYAO This is a project that has won the 2016-18WA China Architecture Award "City Contribution Award" and the 2020 UNESCO Asia Pacific Cultural Heritage Conservation Award.With a history of more than 2700 years, Pingyao is a famous national historical city and a world heritage site. Although Pingyao has long been famous, its tourism features are monotonous, the space and places in the ancient city lack diversity, and the lifestyle of the residents in the ancient city is gradually stagnating.With the renovation of an old diesel engine plant, the construction of a new film palace and the launch of the Pingyao International Film Festival (one of five international film festivals officially approved by the state), the ancient city has received an unprecedented upgrade. During the festival, the Film Palace provides a high-quality venue for the pre-release of domestic and overseas films, awards, and various cultural exchange projects.While satisfying the inherent functions of the film festival, the Film Palace and the ancient city of Pingyao complement each other, gradually pushing the atmosphere of the ancient city to a climax.After the film festival, the film palace becomes a permanent operation of the cinema, and supplemented by supporting entertainment and cultural content into the film culture square open operation.The factory that used to serve the development of the city, with new culture, new ways and public significance, with the appearance of old neighbors, returns to the mission of the city again, which is far more meaningful and more challenging than the aesthetic tribute. In this project, I participated in the conceptual discussion of the overall scheme, and was mainly responsible for the design of the outdoor facade transformation design of multiple entrances of the venue, the design of multiple foyers, and the external site and landscape design.

平遥柴油机厂国际电影展主场馆改造这是一个斩获了 2016-18WA 中国建筑奖“城市贡献奖佳作奖以及 2020 联合国教科文组织亚太地区文化遗产保护大奖的项目。拥有 2700 多年历史的平遥是国家历史名城也是世界遗产，平遥虽早已声名赫赫，但旅游风貌单一，古城内的空间场所缺乏多样性，古城居民生活方式也渐行渐滞。随着对老旧柴油机厂的改造 , 新电影宫的建成以及而平遥国际电影展的开展 ( 国家正式批准的五项国际影展之一 )，平遥古城焕发了前所未有的升级。电影节期间，电影宫为海内外电影的预上映，颁奖，各类文化交流项目的开展提供了高品质的场地。在满足电影节固有功能的同时，电影宫与平遥古城交相辉映，逐渐将古城氛围推向高潮。电影节后，电影宫成为常设运营的电影院，并辅以配套娱乐文化内容成为电影文化广场开放运营。昔日服务于城市发展的工厂，带着新文化新方式和公共意义，用老邻居的样子，重新回归城市使命的方式，远比作为审美的凭吊更有意义，也更有挑战。在该项目中，我参与了整体方案的概念研讨，主要负责设计了场馆多处入口的室外立面改造设计，多处门厅的设计，外部场地及景观设计。

Palace Main Elevation

Steps

1500 -People Outdoor Cinema

Elevation Renovation

VIP Pavilion

Recreation Square

No-Barrier Design

Bird View

Exhibition Hall Elevation

Exhibition Hall Interior

Elevation

Section

Small Path

Cinema Palace Interior

Cinema Palace Main Entrance

Outdoor Theatre Steps Night Scene

Outdoor Theatre Steps

ACADEMY LINEAGE | TSINGHUA UNIVERSITY HIGH SCHOOL

学院传承 | 清华大学附属中学郑东分校规划设计

Site: Zhengzhou, Henan, China Design Commission | 2018 Spring Instructor: Yirui Lian Team Member: Brian Wong, Yu Zhou etc.

项目位置 : 中国 , 河南 , 郑州设计委托 | 2018 春主创设计师 : 廉毅锐搭档 : Brian Wong, 周宇等

Tsinghua Spirit-Students All Over the World As a top institution of higher learning in China, Tsinghua University represents the highest level of culture and education in China.In order to spread the spirit of Tsinghua spirit to all parts of China, in recent years, Tsinghua University has started to establish secondary schools affiliated to Tsinghua University selectively.As one of the important new districts in central China, Zhengdong New District of Zhengzhou City, Henan Province, the district government plans to invest 1.4 billion yuan to introduce the high school attached to Tsinghua University. The school is affiliated to the Bureau of Education, Culture and Sports of Zhengdong New District. The school implements integrated and unified management and is a full-time public school covering kindergarten, primary school, middle school and high school. The campus is located in the north of the inner ring of Longhu, east of the 8th Street of Longyuan and west of the 10th Street of Longyuan in Zhengdong New District. The campus covers an area of 307.41 mu, with a building area of 321,000 square meters.It is the largest branch school of the high school attached to tsinghua university and the first public branch school of the high school attached to tsinghua university in central China.In this project, I participated in the conceptual discussion at the early stage, and was mainly responsible for the scheme design and preliminary enlarged drawing of the outdoor playground stands of the A2 block (high school teaching area), the south entrance library and the gymnasium scheme design and preliminary enlarged drawing of the A3 block (sports living area).

清华精神 - 桃李天下清华大学作为中国顶级高等学府 , 代表了中国最高文化教育水平。为了让清华精神能够更好的开枝散叶到全中国各地，近年来，清华大学开始在全国有选择性地创办清华大学附属中学。河南省郑州市郑东新区作为中国中部重要的新区之一，区政府计划全额投资 14 个亿引进清华附中，学校隶属于郑东新区教育文化体育局，学校实施一体化统一管理，是一所涵盖幼儿园、小学、初中、高中的全日制公办学校。校区地址位于郑东新区龙湖内环北、龙源八街东、龙源十街西，学校整体占地 307.41 亩，建筑面积约 32.1 万平方米。是清华附中体系中最大的一所分校，也是清华附中在华中地区开办的第一所公办附中分校。在该项目中 , 我参与了前期的概念研讨 , 总图确定后 , 主要负责 A2 地块 ( 高中教学区 ) 的室外操场看台方案设计及扩初图纸绘制 , 南入口图书馆扩初图纸绘制 , A3 地块 ( 体育生活区 ) 体育馆方案设计及扩初图纸绘制。

Campus Square Street View

Administrative Building Street VIew

Bird View

中小学部分效果图

Rendering of primary and medium school districts

1st Floor Plan

2st Floor Plan

中小学部分二层平面图

3st Floor Plan

4st Floor Plan

Roof Interlayer Plan

Roof Plan

Library Street VIew

小学教学区透视图 Classroom Building Street VIew

Stadium Street View

Basement Plan 1st Floor Plan

1-1 Section

2st Floor Plan

2-2 Section

South Elevation

Stand Plan(Level 10.25m)

East Elevation

Stand Plan

Stand 1st Floor Plan

Roof Plan

HighOutdoor School Playground Stand StreetStand View

Main Entrance Rendering

Main Entrance Completion

Outdoor Stand Rendering

Outdoor Stand Completion Photo

Air Corridor Rendering

Air Corridor Completion Photo

117述 |1 概案方念概心中究研术技业产 YRAMMUS NALP

SILK ROAD | XINJIANG R&D URUMCHI CENTER DESIGN

丝路之上 | 新疆乌鲁木齐产业技术研究中心设计

Site: Urumchi, Xinjiang, China Design Competition | 2017 Spring Instructor: Brian Wong Team Member: Yu Zhou

项目位置 : 中国 , 河南 , 郑州设计投标 | 2017 春主创设计师 : Brian Wong 搭档 : 周宇

Tianshan Silk Road-Innovation Polar Region In the discussion of China's five thousand years of development culture and history, the Silk Road is a topic that no one can avoid.It represents the earliest trade route between China and the world. At the western end of this significant ancient trade route, Xinjiang also ranks among the first echelon of China's development in the next 50 years or even longer. Xinjiang's development will undoubtedly become the benchmark for China's progress in the next era.As the capital of Xinjiang Uygur Autonomous Region, Urumqi is one of the most important cities in Northwest China. Today, Urumqi has become one of the most vibrant and promising cities in Central Asia.Exploring how to achieve the natural environment, urban development, industrial upgrading, jointly upgrading, and how to achieve the great change of people's lifestyle and ideology through the sight line has become an important topic in architecture and even planning and urban design. The project design of Xinjiang Industrial Technology Research Center, we try to find the answer in the design, and try to make a meeting.In response to the national strategy of the Belt and Road, adapt to the current industrial development situation, improve the comprehensive market competitiveness, industrial technology research center strive to introduce talents, vigorously carry out innovation work.The industrial innovation research center will develop into an important ecological base, a regional landmark logistics and exhibition center, a regional service center for finance, trade, technology, information and education, and a model of research center. In this project, I participated in the concept discussion, the building Rhino model production, the ground landscape design, the report text production and participated in the final project report.

天山帛锦 - 创新极地在中国 5 千年的发展文化历史的探讨中，丝绸之路是一个任何人都无法绕开的话题。他代表着中国与世界最早期的贸易往来路径，在这条意义非凡的古代商道的西端 - 新疆，同样跻身中国后 50 年乃至更长时间的发展第一梯队，新疆的发展无疑会成为下一时代中国式前进的标杆。作为新疆维吾尔自治区的省会乌鲁木齐是中国西北最为重要的城市之一如今，乌鲁木齐已经是中亚地区最有生机和希望的城市之一。探讨如何做到自然环境，城市发展，产业升级，共进升级，如何通过反正视线人民群众生活方式及思想观念的大转变，已经成为了一个建筑学乃至规划及城市设计的重要课题。该新疆产业技术研究中心的方案设计，我们试图在设计中寻找答案，并尝试做出回应。响应一带一路的国家战略 , 是当今高速的产业发展形势，提高综合市场竞争力，产业技术研究中心力求引进人才，大力开展创新争先工作。产业创新研究中心将会发展成重要生态基地 , 地区标志性物流和会展中心 , 金融、贸易、技术、信息与教育的区域服务中心 , 研究中心的典范。在该项目中 , 我参与了前期概念研讨 , 办公楼 Rhino 模型制作 , 完成了地面景观设计 , 汇报文本制作并参与了最终项目汇报。

) 册注邦联国美 ( cnI，tnatlusnoC ngiseD enotS 司公限有计设筑建划规统思海上

Bird View

Architecture Scale Calcultaion

Area Calculation

Box Adjustment

View Analysis

BEAUTIFUL BROCADE

Silk Road

Program Design

Supprting Hotel

Trading Center

Research&Entrepreneurship park

Supporting Apartment

Conference Center

Landscape Analysis

Security Design

Air Corridor View

Interior Courtyard View 建筑主体 Architecture subject

空中封闭连廊 Air CLosed Corridor

连续慢行网络及活动用地 PATH/LOOP-ACTIVITIES

空中开放连廊 Air Open Corridor

下沉公共广场 Sink Public Square

灌木/乔木 UPLAND SPECIES

地面草地 GRAND GRASSLAND

Landscape Explosion diagram

Open Square View

Due to the existence of "Science and Technology Corridor", we can completely abandon the common fence, the Urumqi special security needs combined with the integration of the entire site landscape design, the wrong use, in the realization of security requirements at the same time, the maximum degree The landscape corridor is open to the public,combined with a large area of ecological leisure area and rich water system, the technology research and development center to build a park into a city innovation living room.

TEXTILE MEMORY | HUAFANG WOOLEN MILL RENOVATION

纺织印象 | 华芳集团毛纺厂改造施工项目

Site: Zhangjiagang, Suzhou, Jiangsu, China Project Cost: 50,000,000 RMB Construction Company: Suzhou Jiaxie Construction Engineering Co. Ltd

项目位置 : 中国 , 江苏 , 苏州 , 张家港项目造价 : 5000 万施工单位 : 苏州嘉劦建筑工程有限公司

As one of China's top 500 enterprises, Huafang Group has to face the trend of the times and reduce production in the era of rapid development in China.This project is located in the suzhou in jiangsu zhangjiagang HuaFang Xumian woolen mill, which has a north part and a south part. Due to the need of government, the north factory needs to be demolished. Usable equipment are mvoed and merged to north factory. New efficient Italian imported equipment are procured, so the north plant upgrading. North of the original factory only keep the main structure, internal space are all redesigned,Including the redivision of internal space, new deep pit cold and hot sewage pool, dye VAT platform foundation, steel structure dye VAT platform, indoor concrete storage platform, 1200 meters underground air duct, outdoor pressure PE sewage official website system.The hydraulic and electric power systems were re-laid, and the sewage treatment plant, refrigeration station and pumping station were rebuilt. As one of the two project managers of this transformation project, I was mainly responsible for on-site construction management and construction process coordination in the southern section of the factory (25000 square meters), in-depth docking with architects and structural designers of Jiangsu Textile Industry Design Institute, and accelerating the deepening and completion of construction drawings of semi-finished products.At the same time, according to the requirements of the owners, independently designed the new sewage plant, hot and cold sewage pool, outdoor pressure sewage pipe network, office building indoor transformation and other new projects, and the corresponding construction drawings were drawn.At the same time, I was responsible for other docking work with the owner, changing the data and preparing the visa data.Completed the check with each subordinate subcontract project amount, project payment settlement, a full set of as-built drawings used for audition.(The attached drawings are all drawn by my team or independently)

华芳集团作为中国老牌的 500 强企业 , 在中国如今这个高速发展的年代 , 也不得不面对时代的潮流 , 并厂减产。本项目位于中国江苏苏州张家港市华芳旭勉毛纺厂，原先设有南厂北厂，后因政府拆迁需要，将南厂拆除，可用设备搬迁合并至北厂，并采购新型高效意大利进口设备，因此对北厂进行升级改造，北区原有厂房只保留主体结构，内部空间全部重新设计，包括内部空间重新划分，新建深基坑冷热污水池，染缸平台基础，钢结构染缸平台，室内混凝土仓储平台，1200 米地下风道，室外压力 PE 污水官网系统。水力电力系统均重新敷设，同时配套新建污水厂，改建冷冻站，泵房等。作为该改造工程两位项目经理之一 , 我主要负责厂内南部条整车间 (25000 ㎡ ) 现场施工管理 , 施工过程协调 , 与江苏省纺织工业设计院建筑师 , 结构设计师深度对接，在组织开展现场情况精准测绘的同时 , 加速推进半成品施工图深化完整。同时应业主要求，独立设计了新增污水厂房，冷热污水池 , 室外压力污水管网 , 办公楼室内改造等新增工程 , 及对应施工图绘制。同时负责与业主的其他对接工作 , 变更资料 , 签证资料编制。完成了与各下属分包工程量核对，工程款清算，审计用全套竣工图纸绘制。( 所附图纸均为自己参与绘制或独立绘制 )

Original Structure Demolition

Reinforced Concrete Sewage

Steel Structure Rust Removal and Reinforce

Ceiling

03 04

江苏省纺织工业设计研究院有限公司江苏省纺织工业设计研究院有限公司江苏省纺织工业设计研究院有限公司 DESIGN. No.

APPROVED BY

TITLE

APPROVED BY TITLE

DESIGN. No.

STEP

ITEM

ITEM DATE

DIRECTED BY

CHECKED BY

CHIEF BY CHECKED BY NAME CHECKED BY DESIGNED BY

DRAWN BY DRAWN BY

APPROVED BY

STEP

AUDITED BY

DATE

DIRECTED BY

DATE

SCALE

CHIEF BY

DESIGN. No.

JOB NUMBER STEP

AUDITED BY DIRECTED BY ITEM

CHIEF BY

DESIGN. No.

JOB NUMBER TITLE JOB NUMBER

APPROVED BY AUDITED BY

AUDITED BY

DIRECTED BY

SCALE

NAME NAME

DWG. No. DWG. No.

DRAWN BY

SCALE

DWG. No.

TITLE

JOB NUMBER

STEP ITEM

一层平面图

1:250

DATE

SCALE

CHIEF BY

CHECKED BY

DRAWN BY

NAME

DWG. No.

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

AUDITED BY

DIRECTED BY

CHECKED BY

DRAWN BY

APPROVED BY

STEP

AUDITED BY

DATE

DIRECTED BY

SCALE

CHIEF BY

江苏省纺织工业设计研究院有限公司

DESIGN. No.

JOB NUMBER

ITEM

NAME

DRAWN BY

DESIGN. No.

JOB NUMBER

STEP ITEM DATE

SCALE

CHIEF BY

CHECKED BY DWG. No.

TITLE

NAME

DWG. No.

Workshop Completion

North-Light Roof Hoisting Compltion

一层防火分区示意图

Casting VATs Platform Base

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

DESIGN. No.

JOB NUMBER

STEP

AUDITED BY ITEM DIRECTED BY

DATE

SCALE

CHIEF BY

CHECKED BY

VATs Platform Completion

DRAWN BY

NAME

DWG. No.

污水池平面图

1:100

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

污水池结构图

STEP ITEM

CHIEF BY

CHECKED BY

DESIGN. No.

1:100

江苏省纺织工业设计研究院有限公司

JOB NUMBER

AUDITED BY

DIRECTED BY

AUDITED BY

DIRECTED BY

DESIGN. No.

TITLE

APPROVED BY

DATE

SCALE

JOB NUMBER

STEP ITEM DATE

NAME

SCALE

CHIEF BY DWG. No.

NAME

CHECKED BY

DRAWN BY

DWG. No.

DRAWN BY

染料仓排水槽平面图 1:250

染料仓地坪示意图 1:250

注:排水槽与墙边距离均为5cm

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

ITEM

江苏省纺织工业设计研究院有限公司 APPROVED BY

STEP

AUDITED BY

DATE

DIRECTED BY

SCALE

CHIEF BY

CHECKED BY

DESIGN. No.

JOB NUMBER

AUDITED BY

DIRECTED BY

CHECKED BY DWG. No.

DESIGN. No.

JOB NUMBER

STEP ITEM

DATE

SCALE NAME

DWG. No.

DRAWN BY

染料仓基础平面图

TITLE

CHIEF BY

NAME

DRAWN BY

1:250

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

染料仓建筑平面图1:250

STEP ITEM

DATE

SCALE

CHIEF BY

CHECKED BY

DESIGN. No.

JOB NUMBER

AUDITED BY

DIRECTED BY

NAME

DWG. No.

DRAWN BY

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

ITEM DATE

CHECKED BY

染料仓构造柱圈梁平面图 1:250

SCALE

CHIEF BY

DESIGN. No.

JOB NUMBER

STEP

AUDITED BY

DIRECTED BY

NAME

江苏省纺织工业设计研究院有限公司

DWG. No. APPROVED BY

TITLE

江苏省纺织工业设计研究院有限公司 APPROVED BY

TITLE

DIRECTED BY

ITEM DATE

DATE

SCALE

CHIEF BY SCALE

CHECKED BY

NAME

DWG. No.

DRAWN BY DRAWN BY

JOB NUMBER

STEP

AUDITED BY

ITEM

CHIEF BY

CHECKED BY

DESIGN. No.

JOB NUMBER

STEP

AUDITED BY

DIRECTED BY

DRAWN BY

NAME

DWG. No.

2D-3D Form Encoding WorkFlow Based on StyleGan Ye Huang, Hang Zhang University of Pennsylvania, Philadelphia, PA 19104 {tommyhy,kv333q}@upenn.edu

Abstract

2D-3D FORM ENCODING WORKFLOW BASED ON STYLEGAN 基于深度学习 STYLEGAN 网络的建筑 2D-3D 形体编码及生成工作流

Ye Huang, Hang Zhang Univerisity of Pennsylvania Philadelphia, PA 19104 tommyhy@upenn.edu, kv333q@upenn.edu

黄晔 , 张航宾夕法尼亚大学费城 , PA, 19104 tommyhy@upenn.edu, kv333q@upenn.edu

Based on the state-of-the-art 2D image generation and style transfer algorithm, StyleGan, we propose brand-new strategies of encoding, decoding and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. There are already some 3D model generation algorith. However, their resolution are too low to provide enough information needed in architecture design. We hope this can provide some new ideas and help generate 3D architectural forms in new ways. Keywords: StyleGan, machine learning, form, 3D, generative design.

Abstract Artificial intelligence is not a new concept, but its development has reached a climax in recent years with the huge increase in computing power brought by high-performance GPUs.The architecture industry is also trying to apply artificial intelligence to design.There has been some research on the combination of two-dimensional drawings with artificial intelligence algorithms. However, there are few studies on 3D, and even if there are, their pixels are too low to provide sufficient information for architectural design.Based on StyleGan, the most advanced algorithm for 2D image generation and form conversion, we propose a novel strategy for encoding, decoding and form generation between 2D drawings and 3D models, which is called 2D-3D form coding workflow.We hope that this will provide some new ideas and help architects generate completely new 3D architectural forms in new ways. Keywords: StyleGan, machine learning, form, 3D model, generative design.

摘要人工智能并不是一个全新的概念，而近年随着高性能 GPU 带来的算力的大幅提升，人工智能的发展迎来了高潮。建筑界也在尝试应用人工智能到设计中。目前已经有一些研究是关于二维图纸与人工智能算法的结合。而相关三维的研究却较少，即使有，他们的像素也都极低，无法提供建筑设计中所需的足够信息。基于目前最先进的二维图像生成和形式转换算法 StyleGan，我们提出了一种全新的二维图纸和三维模型之间的编码、解码和形体生成策略，我们称之为 2D-3D 形体编码工作流。我们希望这可以提供一些新的想法，并帮助建筑师用新的方式生成全新的 3D 建筑形式。关键字 : StyleGan, 机器学习 , 形式 , 3D 模型 , 生成设计 .

(a) Exampel Feature 1.

(b) Exampel Feature 1.

Figure 1. Detailed new features in genearted models

1. Introduction Generative design is an essential branch in computational design. In the last 20 years, we witness the tremendous development in parametric design, and there are already several outstanding attempts in pattern generation and form generation in the architecture field. However, most of them are based on pre-design logics, which are predictable. But the fantastic development in machine learning neural network algorithms in recent years brings us brand-new tools in design with the help of high-performance graphic cards. Many design issues can be solved 1

by those new machine learning algorithms. Some of them are working pretty well, such as CNN in facade classification. With so many exciting machine algorithms, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field, such as building facades generation or floor plans generation, etc. However, most of the relative works about machine learning applications in the architecture field are working in 2D. Some possible reasons for the lack of a 3D architecture machine learning algorithm are the comparatively lagging development of 3D machine learning algorithms. Compared with 2D images, the complexity of 3D form issues increases dramatically, not only because of the new z-dimension but also because of different methods of 3D form representation such as point cloud, voxel, and mesh. More people realize the importance of 3D machine learning topics. However, most existing algorithms about 3D issues are not satisfying enough. For example, 3DGAN proposed by Wu et al. (2016)[7] can only work on a tiny scale. On the other hand, after years of development in the 2D machine learning algorithm, generating high resolution and new images are entirely possible nowadays. When it comes to the architecture field, architects always face the issue of 2D and 3D. Every building in the world is 3D. However, when we are working on the construction, people turn to 2D drawings more often. They get all the building information from 2D drawings, a more straightforward and more clear form that includes accurate information about the building. People are finally building 3D buildings with 2D drawings. This tricky relationship between 2D drawing and 3D model inspires us. In terms of the 3D models generation, instead of using new 3D machine learning algorithms which has poor performance at present, we are thinking of using the more sophisticated 2D algorithm: StyleGan, which has astonishing performance in the 2D image generation and style transfer, to help us generate 2D architectural drawings and then, use them to ’construct’ corresponding 3D architectures. In Figure 1, we can see the high-resolution output result, the style transfer model between one typical high-rise building, and the Sydney Opera House.

2. Relative Work DCGAN Deep convolutional generative adversarial networks are the first GAN which was a major improvement on the GAN architecture, it introduced a more stable training process and higher samples of quality. CycleGan Compared to pix2pix, CycleGAN no longer needs paired images to train, it only requires an input dataset and an output dataset. In addition, It makes constraints on networks by giving another network to train the output dataset back to the input dataset. The whole process is trying to reach to the goal that the result-input should be the same as the original-input. CycleGAN successfully divided style and content, and it is easy to learn automatically maintaining contents when changing styles. CGAN and Pix2Pix Conditional GAN is the first attempt to add additional information on both generator and discriminator. This information could be labels tagged on original inputs data, resulting in images with better quality and the controllability of what the output images will look like. pix2pix no longer inputs random noise when training, instead the inputs of discriminator(D) become paired images from class A and B. After training is finished, the output results from class B could be controlled by inputting a new image.

Figure 3. GAN

Figure 4. CycleGan

Figure 5. Pix2Pix

Figure 6. Pix2Pix

3D GAN Wu et al.(2016)[7] propose the 3D Generative Adversarial Network (3D-GAN), which can generates 3D objects from a probabilistic space based on volumetric convolutional networks and generative adversarial nets. The resolution of the final model can reach 64*64*64.

Figure 7. The generator in 3D-GAN

3. StyleGan Overview

Figure 2. 2D-3D Form Encoding WorkFlow

As mentioned above, the existing 3D machine learning generative model do not have enough resolution for architectural models. On the other hand, StyleGan, as the state-of-the-art algorithm proposed by researchers from Nvidia can work on images with high resolution(up to 1024*2048). StyleGan is the improved version of ProGan. By introducing progressive training, the method that networks train from low resolution and gradually increase until reaching the max of the target, ProGAN solved the problem of GANs generating high quality and high-resolution images.However, essentially it is a method of generating unconditional data which is hard to control the property of output images. Based on ProGAN, StyleGAN mainly modified the input of each level separately to control the visual features represented by this level without affecting other levels. The lower the level, the lower the resolution, the coarser the visual features that can be controlled. This method helps to generate different levels or multi-layers 3

style-mixing outputs. In addition, instead of using traditional random vector input, StyleGAN introduced Mapping Network and Adaptive. Instance Normalization (AdaIN) to control visual features of the generated image, leading to the effect of truncation trick as a function of style scale. To translate 3D problems into 2D, we start from sections of 3D models because sections contain very rich information about the target building not only its outline but also its interior space. Instead of just cut the model to get just a few key sections, we cut the target models 64 times to get 64 section pieces. Then get the snapshot of each piece in the resolution of 128*128. After getting 64 drawings, we array them into an 8*8 grid one by one and finally get a 1024*1024 image, which contains the former target model information in the resolution 128*128*64.

Figure 9. Reconstruction Detail

(a)

(b)

(c)

Figure 8. StyleGan

4. Implementation

Figure 10. Input Training Data

4.1. Training Data Preparation In terms of train data, we pick a few buildings, all of which have either historical value or form value. What we want to do to find out the blending form of different pairs of models in them in the the context of 2D-Image encoding. The detailed pairing strategy and form generation of pairing will be talked later

to do further style transfer. In Figure 12, we find that at epoch 5000, the reconstruction starts to be clear. As the training goes on till epoch 8500, the result is quite satisfying: compared with the result as epoch 5000, results at epoch 8500 contain most details from the input models. And then we start our decoding process.

4.2. Training

4.4. 3D Decoding and Form Generation

With the help of a powerful Nvidia RTX Titan graphic card, we successful train all the input at the same time in StyleGan. Because of the sophisticated source written by Nvidia engineers, after some adjustment by ourselves, we can train several pairs of inputs at the same time as shown in Figure4.2. The left grid shows all the inputs to StyleGan(total of 28 images at the same time), the right part shows details about one single image in the left image grid

As for the output, we first check the reconstruct results. This step helps show the reconstruction result of input images. Generally, when the reconstruction results are good enough, we will use the training model at that step

With the results after 8500 epochs of training when the reconstruction result is quite satisfying, we start the style blending of 2D-Encoding images with continues changing the weight of 2 parent images. As mentioned before, we use several pairs of images to generate our final forms. The pairing strategy is showed in the table 4.4. We pick several images in the red wirefream from the StyleGan results as decoding source. Then decoding the images into 3D models. As shown in the final generated models(Figure 4.4), we can observe the continues change one model to another modle. These generated models between those 2 input models show exciting new architecture features. For example, in Figure 16, we can clearly see the column-liked form which is the fusion of the column from high-rise building and slab from Sydney Opera. The series decoding process of our 3D input is shown in

4.3. Reconstruction Result

Figure 11. Reconstruction Porcess Figure 11. Reconstruction Porcess

Figure 13. Series result images of style blending

5. Conclusion

Figure 12. Reconstruction Porcess

Model Pairs

Model 1

Model 2

Model 1

Model 2

Pair (a) PairPair (a) (b) PairPair (b) (c) PairPair (c) (d)

Sydney Opera SydneyMuseum(Wright) Opera Guggenheim Guggenheim GuggenheimMuseum(Wright) Museum(Gehry) GuggenheimMuseum(Gehry) Museum(Wright) Guggenheim

Home Insurance Building(Chicago) Home Insurance Building(Chicago) Guggenheim Museum(Gehry) Guggenheim Museum(Gehry) Typical Gothic Church Sydney OperaChurch Typical Gothic

Pair (d)

Guggenheim Museum(Wright)

Sydney Opera

Table 1. Pairing of Input Models

Table 1. Pairing of Input Models There are several other interesting new form in generated models which we have not seen before.

In this paper, by using StyleGan, we successfully reconstruct existing building models through the 2D-3D encoding strategy and realize style blending and generation of new architectural forms based on existing forms. This style blending strategy provides a new idea of architecture design for an architect which expands the boundary of form-finding. Compared with other relative machine works such as Deep Form Finding proposed by Jaime et al. which use VAE to generate continues changing structure form between two input buildings, our result has way much higher resolutions(128*128*64) compared with theirs(14*14*11) In SG2018, there is a group work call HouseGAN, which is kind of the first application that uses GAN in 3D model design(the paper is still in the writing process). They use the multi-view system to get relative views of the target architecture and then train the image to for form generation. It is clear that in Figure19, we can find the final result is not satisfying enough to recognize the form. The resolution is only about 32*32*32.

There are several other interesting new form in generated models which we have not seen before.

6 66

Figure 14. Series Decoding Process

(a) Exampel Feature 1.

(b) Exampel Feature 1.

Figure 16. Detailed new features in genearted models

Figure 14. Series Decoding Process

Figure 18. Resutl from Deep Form Finding(Miguel et al.) Figure 18. Resutl from Deep Form Finding(Miguel et al.) Figure 18. ResutlFigure from Deep FormFigure Finding(Miguel et al.)Deep FormetFinding(Miguel 18. Resutl from Deep 18. Resutl Form Finding(Miguel from al.) et al.) Figure 18. Resutl from Deep Form Finding(Miguel et al.)

Figure 17. Other outstanding generated models by our workflow Figure 15. Form generation with the different outputs from StyleGan

(a) Exampel Feature 1.

6. Future Work

(b) Exampel Feature 1.

Figure 16. Detailed new features in genearted models

blending model will be increased a lot. 18. Resutl from Deep 18. Resutl FormetFinding(Miguel from al.) Figure 18. Resutl et al.) from Deep Form Finding(Miguel et al.) Figure 18. Resutl Figure from Deep Form Figure Finding(Miguel al.) Deep FormetFinding(Miguel

Figure 18. Resutl from Deep Form Finding(Miguel et al.)

For future work,some predictable improvements are still needed for this encoding strategy. First, the running time issue is still a difficult problem we have to deal with. As mentioned before, the training process of those input Figure 18. Resutl from Deep Form Finding(Miguel et al.) lasts for 10 days in total with 1 single Nvidia RTX Titan. Even though the reconstruct result is satisfying enough for 9 model decoding after 8000 epochs(5days), the running time is unacceptable for the most designers. Sometimes, the running time is also out of control. Figure 19. Result from Fresh Eye(Steinfeld et al.) Figure 19. Result from Fresh Eye(Steinfeld et al.) Besides, the resolution of this encoding is indeed way much higher than the existing 3D machine learning algoFigure 19. Result Figure from Fresh 19.etResult Eye(Steinfeld et al.) Eye(Steinfeld Figure et 19.al.) Result from Fresh Eye(Steinfeld et al.) Figure 19. Result from Fresh Eye(Steinfeld al.) from Fresh rithm most of which are about 32*32*32 or 64*64*64. But our 2D-3D encoding strategy which has the resolution References References of 128*128*64 is still needed improvement to get a better representation of a building system. References References References[1]References Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Last, in most situations, 64 sections in one direction is a little bit redundant for a simple building. The traditional [1] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron [1]Courville, Ian J.Jean Goodfellow, [1] Ian J.Jean Goodfellow, Pouget-Abadie, Jean Pouget-Abadie, Mehdi Mirza, Mehdi Bing Xu, Mirza, David Bing Warde-Farley, Xu, David Warde-Farley, Sherjil Ozair,Bing Sherjil Aaron [1] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Xu,Ozair, David Warde-Farley and Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, JunAaron way of representing a simple building such as a three-layer house may only have 10-20 sections and many other [1] Ian J. Goodfellow, Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Jun Courville, and Courville, Yoshua Bengio. and Yoshua Generative Bengio. Adversarial Generative Networks. Adversarial Networks. e-prints, arXiv page e-prints, arXiv:1406.2661, arXiv:1406.2661, Jun arXiv e-prints, Jun page Courville, and YoshuaarXiv Bengio. Generative Adversarial Networks. 2014. plans or detail. Courville, and Yoshua Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Junpage Figure 19. Result Figure from Fresh 19. Eye(Steinfeld from Fresh et al.) Eye(Steinfeld Figure et 19. al.) Result from Fresh Eye(Steinfeld et al.) FigureBengio. 19. ResultGenerative from Fresh Eye(Steinfeld et Result al.) 2014. 2014. 2014. 2014. 2014. So based on the conclusion above, our first next move about our strategy is to develop a better framework [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional Phillip Zhu,References Tinghui Zhou, and Alexei A.Eye(Steinfeld Efros. Image-to-image translation conditional for building information representation. Based on the new framework, we can extract the crucial section or planReferences References[2][2] Figure 19. Result from Fresh et al.) Image-to-image PhillipIsola, Isola, [2]Jun-Yan Jun-Yan Phillip Isola, Zhu, Jun-Yan Tinghui Zhou, Zhu, Tinghui and Alexei Zhou, A. and Efros. Alexei Image-to-image A. Efros. translation withtranslation conditional with conditional trans [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A.with Efros. Image-to-image References [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks. CoRR, abs/1611.07004, 2016. adversarial networks. CoRR, abs/1611.07004, 2016. networks. information from input building models in a more efficient way, we can reduce the number of section. As long adversarial networks. adversarial CoRR, networks. abs/1611.07004, CoRR, abs/1611.07004, 2016. 2016.CoRR, abs/1611.07004, 2016. adversarial adversarial networks. CoRR, abs/1611.07004, 2016. [1] Ian J.Jean Goodfellow, [1] Ian J.Jean Goodfellow, Pouget-Abadie, Pouget-Abadie, [1] Mehdi IanXu, J. Mirza, Goodfellow, Bing Mehdi Xu, Mirza, Jean David Pouget-Abadie, Bing Warde-Farley, Xu, David Warde-Farley, Sherjil Ozair,Bing Aaron Sherjil Xu,Ozair, David Aaron Warde-Farley, Sherjil Ozair [1] Ian J. Goodfellow, Pouget-Abadie, MehdiJean Mirza, Bing David Warde-Farley, Sherjil Ozair,Mehdi AaronMirza, [3] Tero Karras, Samuli Laine, and Figure Timo 19. Aila. A from style-based generator architecture for generative adversarial as the number of sections is decreased, we can increase the size of every section drawing, for example, from Result Fresh Eye(Steinfeld etAdversarial al.) References [3]Tero Tero Karras, [3]Samuli Samuli Tero Karras, Laine, Samuli and Timo Laine, Aila. and A style-based Timo Aila. Agenerator style-based architecture generator for architecture generative for adversarial generative adversarial [3] Tero Samuli Laine, and Timopage Aila. A style-based generator architecture for Courville, and Courville, Yoshua Bengio. and Yoshua Generative Bengio. Adversarial Courville, and Networks. Adversarial Yoshua arXiv Bengio. Networks. e-prints, Generative arXiv page arXiv:1406.2661, Networks. arXiv:1406.2661, Jun arXiv e-prints, Jun page arXiv:1406.26 Courville, [3] andTero Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Jun [3] Karras, Laine, and Timo Aila. A Karras, style-based generator architecture for generative adversarial Karras, Samuli Laine, and Timo Aila. AGenerative style-based generator architecture fore-prints, generative adversarial Figure 17. Other models by our workflow In this case, the running time networks. CoRR, abs/1812.04948, 2018. networks. 128*128(what we have now) to 256*256 willoutstanding increase generated the resolution dramatically. networks. CoRR, networks. abs/1812.04948, CoRR, abs/1812.04948, 2018. 2018. CoRR, abs/1812.04948, 2018. 2014. 2014. 2014. 2014. networks. CoRR, abs/1812.04948, 2018. Mehdi Mirza, Bing Xu, David Warde-Farley, networks. CoRR, abs/1812.04948, 2018.Pouget-Abadie, [1] Ian J. Goodfellow, Jean Sherjil Ozair, Aaron will be the same but the building still conveyed clearly and the detail level of the final reconstruct model and style References [4][4] Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Diederik P [4] Kingma Diederik and PMax Kingma Welling. and Auto-Encoding Max Welling. Auto-Encoding Variational Bayes. Variational arXiv e-prints, Bayes. page e-prints, arXiv:1312.6114, page arXiv:1312.6114, [4]and Diederik PA.Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, p [2] Jun-Yan Phillip Isola, [2] Jun-Yan Phillip Isola, Zhu, Jun-Yan Tinghui Zhou, Zhu, [2] Tinghui and Phillip Alexei Zhou, Isola, A. Efros. Jun-Yan Alexei Image-to-image Zhu, Efros. Tinghui Image-to-image Zhou, translation and Alexei withtranslation conditional A.arXiv Efros. with Image-to-image conditional translation with con [2] Phillip Isola, Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional Courville, and Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Jun blending model will be increased a lot. Diederik Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, [4] Diederik [4] P Kingma andPMax Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Dec 2013. Dec 2013. Dec 2013. Dec 2013. adversarial networks. adversarial CoRR, networks. abs/1611.07004, CoRR, abs/1611.07004, adversarial 2016. networks. 2016. CoRR, abs/1611.07004, 2016. adversarial networks. CoRR, abs/1611.07004, 2016. 2014. [1] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Dec 2013. Dec 2013. 8 Courville, and Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Jun Mehdi Mirza [5]and and Mehdi Simon Mirza Osindero. and Simon Conditional Osindero. generative Conditional adversarial generative nets. adversarial CoRR, abs/1411.1784, nets. CoRR, abs/1411.1784, 2014. 2014. [5] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. CoRR, abs [3] Samuli Tero Karras, [3] Samuli TeroTimo Karras, Laine, Samuli and Timo Laine, Aila. [3] and Tero Agenerator style-based Timo Karras, Aila. Samuli A generator style-based Laine, and generator Timo for Aila. architecture generative A style-based for adversarial generative generator adversarial architecture generative adv [3] Tero Karras, Laine, and Aila. ASimon style-based architecture forarchitecture generative adversarial [5][5] Mehdi Mirza Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014.for [2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. translation with conditional [5] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. CoRR, Image-to-image abs/1411.1784, 2014. [5]CoRR, Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014. 2014. networks. networks. abs/1812.04948, CoRR, abs/1812.04948, 2018. networks. 2018.CoRR, abs/1812.04948, 2018. networks. CoRR, abs/1812.04948, 2018. adversarial networks. CoRR, abs/1611.07004, 2016. [6] Alec Radford, [6] Luke Alec Radford, Metz, and Luke Soumith Metz, Chintala. and Soumith Unsupervised Chintala. Unsupervised Representation Representation Learning with Deep Learning Convoluwith Deep Convolu[6] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learnin [6] Alec Radford, Luke Metz,Chintala. and Soumith Chintala.Representation UnsupervisedLearning Representation Deep Convolu[6] Alec Radford, Luke Metz, and Soumith Unsupervised with DeepLearning Convolu-with [6] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative tional Adversarial Generative Networks. Adversarial Networks. e-prints, arXiv page e-prints, arXiv:1511.06434, page arXiv:1511.06434, NovVariational 2015. Nov 2015. tional Generative Adversarial Networks. arXiv e-prints, page arXiv:1511.06434, Nov 20 Diederik PMax [4] Kingma Diederik and P Max Kingma Welling. and Auto-Encoding Max [4]Tinghui Welling. Diederik Auto-Encoding Variational ParXiv Kingma Bayes. and Variational Max arXiv Welling. e-prints, Bayes. Auto-Encoding arXiv page e-prints, arXiv:1312.6114, page arXiv:1312.6114, Bayes. arXiv e-prints, page arXiv:131 [4] Diederik P [4] Kingma and[2] Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Phillip Isola, Jun-Yan Zhu, Zhou, and Alexei A. Efros. Image-to-image translation with conditional tional Generative Adversarial Networks. arXiv e-prints, page arXiv:1511.06434, Nov 2015. tional Generative Adversarial Networks. arXiv e-prints, page arXiv:1511.06434, Nov 2015. [3] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial 9 tional Adversarial Networks. page arXiv:1511.06434, Nov 2015. Dec 2013. adversarial Dec Generative 2013. networks. Dec 2013. arXiv e-prints, Dec 2013. CoRR, abs/1611.07004, 2016. [7] Jiajun Wu, [7] Chengkai Jiajun Wu, Zhang, Chengkai TianfanZhang, Xue, Tianfan Xue, Freeman, William and TZhang, Freeman, JoshuaTianfan B and Tenenbaum. Joshua BLearning Tenenbaum. a ProbLearning a Prob[7]William Jiajun TWu, Chengkai Xue, William T Freeman, and Joshua B Tenenba networks. CoRR, abs/1812.04948, 2018. [7] Chengkai Jiajun Wu, Chengkai Tianfan William Freeman, and Joshua B Tenenbaum. [7] Jiajun Wu, Zhang, TianfanZhang, Xue, William T Xue, Freeman, and TJoshua B Tenenbaum. Learning a Prob- Learning a Probabilistic Latent abilistic Space Latent ofOsindero. Object Space Shapes of Xue, Object via 3D Shapes Generative-Adversarial via 3D Generative-Adversarial Modeling. Invia Modeling. Advances In In Neural Advances In Neural abilistic Latent Space of Object Shapes 3D Generative-Adversarial Modeling. 20 In [5]and Mehdi Mirza [5] and Mehdi Simon Mirza Osindero. and Simon Conditional [5]and Mehdi generative Conditional Mirza and adversarial generative Simon Osindero. nets. adversarial CoRR, Conditional abs/1411.1784, nets. CoRR, generative 2014. adversarial 2014. nets. CoRR, abs/1411.1784, [5] Mehdi Mirza Simon Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014. [7]Osindero. Jiajun Wu, Chengkai Zhang, Tianfan William T Freeman, and Joshua Babs/1411.1784, Tenenbaum. Learning a Prob[3] Tero Karras, Samuli Laine, Aila. A style-based generator generative adversarial abilistic Latent Space ofKingma Object Shapes via 3DTimo Generative-Adversarial Modeling. In architecture Advances In for Neural abilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances In Neural [4] Diederik P and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Information Processing Information Systems, Processing pages Systems, 82–90, pages 2016. 82–90, 2016. Information Processing Systems, pages 82–90, 2016. abilistic Latent Space ofChintala. Object Shapes via 3D Generative-Adversarial Modeling. In Advances In Learning Neural with Deep C (a) Exampel Feature 1. (b) Exampel Feature 1. networks. CoRR, abs/1812.04948, 2018. [6] Information Alec Luke Alec Radford, Metz, and Luke Soumith Metz, and [6] Alec Soumith Unsupervised Radford, Chintala. Luke Unsupervised Representation Metz, and Soumith Representation Learning Chintala. with Deep Unsupervised Learning Convoluwith Deep Representation Convolu[6] Alec Radford, Luke Radford, Metz,[6] and Soumith Chintala. Unsupervised Representation Learning with Deep ConvoluProcessing Systems, pages 82–90, 2016. Information Processing Systems, pages 82–90, 2016. Dec 2013. Figure 16. Detailed new features in genearted models Processing Systems, pages 2016. (a) Exampel Feature 1. (b) Exampel Feature 1. [8]Information Jun-Yan Zhu, [8] Jun-Yan Taesung Zhu, Park, Taesung Phillip Isola, Park, and Phillip Alexei Isola, A2015. Efros. and Alexei Unpaired A2015. Efros. image-to-image Unpaired image-to-image translation using translation using [8]82–90, Jun-Yan Zhu, Taesung Phillip Isola, and Alexei A Efros. Unpaired image-to-im tional Generative tional Adversarial Generative Networks. Adversarial arXiv Networks. tional e-prints, Generative arXiv page e-prints, arXiv:1511.06434, Adversarial page Networks. arXiv:1511.06434, NovPark, arXiv e-prints, Nov 2015. page arXiv:1511.06434, Nov 2015. tional Generative Adversarial Networks. arXiv e-prints, page arXiv:1511.06434, Nov (a) Exampel Feature 1. (b) Exampel Feature 1. Figure 16. Detailed new features[8] in genearted models [4] Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei Efros. Unpaired image-to-image translation using [8] Mehdi Jun-Yan Zhu,and Taesung Park, Phillip Isola, andgenerative Alexei A(ICCV), Efros. Unpaired image-to-image translation using cycle-consistent cycle-consistent adversarial networkss. adversarial InAnetworkss. Computer Vision In Computer Vision 2017 IEEE (ICCV), 2017 IEEE Conference International on, Conference on, Interna cycle-consistent adversarial networkss. In Computer Vision (ICCV), 2017 IEEE [5] Mirza Simon Osindero. Conditional adversarial nets.International CoRR, abs/1411.1784, 2014. Figure 16. Detailed new features in genearted models [8][7] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei ATianfan Efros. Unpaired image-to-image translation using Jiajun Wu, Chengkai Jiajun Wu, Zhang, Chengkai TianfanZhang, [7]William Tianfan Jiajun TWu, Xue, Freeman, Chengkai William T Zhang, Freeman, Joshua B and Tenenbaum. Joshua Xue, BLearning Tenenbaum. T Freeman, a ProbLearning and Joshua a ProbB Tenenbaum. Learning [7] Jiajun Wu,[7] Chengkai Zhang, Tianfan Xue, William TXue, Freeman, and Joshua B and Tenenbaum. Learning aWilliam Prob8 9 cycle-consistent adversarial networkss. In Computer Vision (ICCV), 2017 IEEE International Conference on, Dec 2013. 2017. 2017. cycle-consistent adversarial networkss. In 2017. Computer Vision (ICCV), 2017 IEEE International Conference on, abilistic Latent abilistic Space Latent of Object Space Shapes of Object via abilistic 3DShapes Generative-Adversarial Latent via 3D Space Generative-Adversarial of Object Modeling. Shapes Invia Modeling. Advances 3D IEEE Generative-Adversarial In In Neural Advances InConference Neural Modeling. In Advances In abilistic Latent2017. Space [6] of Object Shapes via 3D Generative-Adversarial In Advances In Neural cycle-consistent adversarial networkss. In Modeling. Computer Vision (ICCV), 2017 International on, Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learning with Deep Convolu2017. [5] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014. Information Processing Information Systems, Processing pages Systems, 82–90, Information 2016. pages 82–90, Processing Systems, 82–90, 2016. Information Processing Systems, pages 82–90, 2016. 2017. tional Generative Adversarial Networks. arXiv2016. e-prints, pagepages arXiv:1511.06434, Nov 2015.

10 10 10 [8] Jun-Yan Zhu, [8] Jun-Yan Taesung Zhu, Park, Taesung Phillip Isola, Park, [8]Efros. and Phillip Jun-Yan Alexei Isola, Zhu, AChintala. Efros. and Taesung Alexei Unpaired Park, A Efros. Phillip image-to-image Unpaired Isola, and image-to-image translation Alexei A Efros. using translation Unpaired using image-to-image translatio [8] Jun-Yan Zhu, Taesung[6] Park, Phillip Isola, and Alexei A Unpaired image-to-image translation using Alec Radford, Luke Metz, and Soumith Unsupervised Representation Learning with Deep Convolu[7] Jiajun Wu, Chengkai Zhang, Tianfan10Xue, William T Freeman, and Joshua B Tenenbaum. Learning a Probcycle-consistent cycle-consistent adversarial adversarial In Computer networkss. cycle-consistent In arXiv Computer (ICCV), adversarial Vision 2017 networkss. IEEE (ICCV), International 2017 In Computer IEEE Conference International Vision on,Conference 2017 IEEE on, International Confere cycle-consistent adversarial networkss. In networkss. Computer Vision (ICCV),Vision 2017 IEEE International Conference on, 10 page tional Generative Adversarial Networks. e-prints, arXiv:1511.06434, Nov (ICCV), 2015. 10 abilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances In Neural 2017. 2017. 2017. 2017.

(a) Exampel Feature 1.(a) Exampel Feature 1.

(b) Exampel Feature 1.(b) Exampel Feature 1.

Figure 16. Detailed Figure new features 16. Detailed in genearted new features modelsin genearted models

Information Processing Systems, pages 82–90, 2016. [7] Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances In Neural

XHome-BIM Prefabricated House Design Ye Huang1 , Brian Wong2 , and Yu Zhou3

XHOME-BIM PREFABRICATED HOUSING SYSTEM XHOME-BIM 预制装配式住宅系统

Ye Huang, University of Pennsylvania Brian Wong,Columbia University, New York, NY 10027 Yu Zhou, THAD, Beijing 100084

Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104 2 GSAPP, Columbia University, New York, NY 10027 3 Architectural Design & Research Institute of Tsinghua University, Beijing 100084

黄晔 , 宾夕法尼亚大学 , 费城 , PA, 19104 Brian Wong, 哥伦比亚大学 , 纽约 , NY 10027 周宇 , 清华大学建筑设计研究院 , 北京 , 100084

Abstract This paper is a summary of the results of an entrepreneurial project.Combined with the rapid improvement of BIM technology and prefabricated component technology at that time, we put forward a new residential construction strategy based on BIM system.After the preliminary investigation, we preliminarily decided to choose Canada as the pilot country. Through the in-depth study of the real estate market in Canada, we found the possibility of unique business opportunities in the design and construction of residential buildings.Based on parameterized software and programming, we designed Rhino model and corresponding Grasshopper file that can generate scheme design drawings with one click according to customer requirements. In the later stage, we cooperated with professional programmers and connected the Demo algorithm to Archicad to generate modular construction drawings.Combined with prefabricated elements, we proposed a new housing construction scheme called X-Home.Customers can design their new home, pay for it, and place an order with a simple operation on a mobile APP.After drawing is generated in the background, all information is directly delivered to the factory to generate prefabricated components, which are shipped to Canada and assembled in the homestead.After the project is delivered, the APP becomes the intelligent control end of the house.The pilot project was selected in Canada.It is expected that within 120 days, a brand new home will be delivered to the client in the selected location.

Abstract This paper presents a new BIM-based housing construction strategy. Based on the detailed researches of the Canadian real estate market, we find the possibility of unique business opportunities in house design and construction. With the help of new construction techniques such as prefabrication and BIM, we propose a brand-new housing construction solution named XHme, which can be applied in Canda. Within 120 days, an all-round new house can be delivered to clients in the selected site. Keyword: BIM, Prefabrication, Parametric Design, Residence, Real Estate.

Keywords: BIM, Prefabrication, Parametric Design, Residence, Real Estate, Operation & Maintenance

摘要本文是对一个创业项目成果的总结。结合彼时 BIM 技术及预制构件技术的飞速提升，我们提出了一种新的基于 BIM 系统的住宅建造策略。在通过前期简单调研后，我们初步确定选在加拿大作为试点国家，通过对加拿大房地产市场的深入研究，我们发现了住宅设计和建造中存在独特商机的可能性。基于参数化软件及编程，我们设计了可以根据客户需求一键生成方案设计图纸的 Rhino 模型及对应 Grasshopper 文件，并在后期与专业程序员合作，将该 Demo 算法接入 Archicad，用于生成模块化施工图纸。结合预制构件，我们提出了一种全新的住宅施工方案，名为 X-Home。客户通过在手机 APP 程序上简单的操作即可设计自己的新家，并付款下单。后台生成图纸后，所有信息直接交付工厂生成预制构件，通过海运运至加拿大，并在宅基地进行组装。项目交付后，APP 变成住宅的智能控制端。项目试点选在加拿大。按照预期，在 120 天内，一个全新的住宅就可以交付给选定地点的客户。关键字 : BIM, 预制 , 参数化设计 , 住宅 , 房地产 , 运维

Figure 1: Drawing outputs with one click in Grasshopper; (a) basement plan; (b) 1st floor plan; (c) 2nd floor paln; (d) east elevation; (e) south elevation; (f) west elevation; and (g) north elevation

1. Introduction BIM is a terminology has exists everywhere in architectural field in the last 20 years. It went through healthy development among most developed countries over the world espeically in Europe and North 1

America. On the other hand, China, as a developing country, lags behind relatively in technology such as BIM, but the cheap labor is competitive among the world. BIM is a terminology that has exists everywhere in the architectural field in the last 20 years. It went through healthy development among most developed countries over the world, especially in Europe and North America. On the other hand, China, as a developing country, lags relatively in technology such as BIM, but cheap labor is competitive in the world. As one of the few developed countries in the world, Canada has a very high per capita GNP, high-level industrialization, and extremely high-quality life. On the other hand, the relatively slow development of real estate in recent years and the long-lasting gentle pace of life lead to the unsatisfying people’s poor residential quality, especially houses. Some of them are in bad condition, and the long-time lack of maintenance make them hard to be repaired anymore. Therefore, there is a potential requirement for new houses for some Canadians. Based on systematic research, we develope the product: XHome, a complete housing construction workflow that includes every step from design to delivery. In summary, this paper makes several contributions that can be helpful in contemporary house design and construction: (1) Developed countries house real estate research; (2) Contemporary house design; and (3) New approach to prefabricated construction.

2. Background Research

gain after training (percentage). We can do a deformation to the formula: divide both the numerator and denominator by B, and we get the formula 1 (1 − ( 1+E )) ∗ (12 − C) (2) A B + (C ∗ D)

When we focus on the parameter B, we conclude that when B(monthly labor cost(dollars)) is bigger, the ROI is bigger. When the invested labor in the project is bigger, which means a project with bigger size and higher complexity and bigger size of industry companies, the ROI increase. When we focus on the parameter E, we can conclude that when E(productivity gain after training(percentage)) is more prominent, the ROI is more significant. Higger E means that with a lower unit price of the project and higher reusable rate, the project benefit is higher. The conclusion derived from the formula matches the conclusion from the current situation investigation. Therefore, it can be concluded that under the circumstances where BIM industry benefits ROI is generally low, the breakthrough point to improve ROI is to improve B(monthly labor cost(dollars)) and E(productivity gain after training(percentage)). Accordingly, we should first, focus on the production of building products and product operation to, and second, focus on project integration and turnkey job management.

3. Market Research To determine the production and target audience, we did large-scale researches worldwide. The final result is the Single Detached House in Canada for the middle class.

2.1. BIM and Prefabricated Building Prospect Building Information Modelling(BIM) is a broad term that describes the process of creating and managing digital information about a built asset. (BIM wiki, 2019). Based on the definition and past researches, we find that the core value of BIM is the dramatic improvement of productivity led by the building information, which has not been fully realized. Relative survey shows due to the application of BIM; half respondents report the 5% acceleration of the project completion, over a quarter respondents reports the improvement of labor productivity by 25%, and a third of respondents report the 25% reduction of in site labor because of more offsite fabrication. Meanwhile, the lack of interest and support for BIM and the lack of collaboration on project teams are two main obstacles to promoting BIM. BIM-BAM-BOOM According to the research from an industry authority, Patrick MacLeamy, if we define the benefit of pure BIM market as x, the benefit generated by BIM and BAM can reach to 20x, and the BIM-linkage capital market scale(BAM + BOOM + assess management) can reach to 60x.(cite) However, compared with the market scale expectation of 60x, the real benefit at nowadays building industry is only about 0.3x that has a considerable gap between 60x. Return of Investment(ROI) When eliminating human’s subjective factors and uncontrollable environmental factors, we can conclude that the project size and the industry size are the main factors of ROI. Meanwhile, we can turn to the equation proposed by Autodesk. In the Autodesk BIM Report(cite), they propose the formula of ROI1. B )) ∗ (12 − C) (B − ( 1+E (1) A + (B ∗ C ∗ D)

where A is the cost of hardware and software(dollars), B is the monthly labor cost(dollars), C is the training time(months), D is the productivity lost during training (percentage), and E is the productivity 2

3.1. Building Types There are several types of buildings in the markets based on the primary classification method. primary classification method, such as • Single-family detached • Large multi-family (apartments/flats) • Office • Retail • Hotels • Special-purpose buildings • Industrial buildings, • Other In the BIM context, compared with other building types, single-family house has two obvious advantages: lower investment and high reusable rate.

3.2. Market Selection 1. Living Habit North American has the biggest single-family detached house market. Most people tend to choose a single house as the first residential choice.

2. Government Review and Approval The land used for housing in North America is private land and can be used for private residential construction. The administrative approval process is complex but procedural. 3. Digital Information North American electronic land information is complete and convenient for data management and automatic plotting.

3.3. Existing Competitors 1. Zillow and Other Second-hand Trading Agencies. The online data can basically represent the contemporary second-hand house market. Zillow owns about 110 million American household data, which constitutes one-third of the second-hand house market. The market sharing of other big agencies such as Trulia is 15.89%, and Realtor is 19.35% 2. Real Estate Developer The development of real estate is stable in recent years. 3. Existing Prefab-Housing Company Prefabricated house companies are led by Homeowever, followed by many other prefabricated house factory(Clayton, Express Modular, Champion, etc), all of which share the market together. We get browsing rank results of the leading agency in second-hand markets, Zillow.com and prefabricated house companies, Homeowners.org from Alexa. From the chart, we find the browsing rank of Zillow.com is way much higher than that of Homeowners.org() results(see Figure xxx) show that compared with second-hand house market, the prefabricated house gets way much less attention. On the other hand, it also means immense potential in this field.

(a)

(b)

Figure 3: (a) ; and (b) . new house from the developers or purchase a relatively better second-hand house. It will be excellent for them if they can get a new house in their original house site without worry about the complicated and expensive process.

3.6. Demand Survey According to the incoming and house price analysis from CMHC DATA, the average price of a single house(2017) in Canada is 713579 dollars, which is 20-years incoming for 63.13% of the population.(see Table 4(a) As mentioned before, there are 1833055 families who need house quality improvement. The number of the completed house every year is 13227 ∗ 4 = 52905. So it will take 34 years to meet all the needs without regard to the new demand in this process.

3.4. Market size estimization The total market trading value in Q1, 2017, is about 823 billion CAD. So the estimated trading volume is about 3992 billion CAD.

(a)

(b)

Figure 4: (a) Average income in Canada; and (b) average price in Canada. (a)

(b)

(c)

Figure 2: (a) Canada 2017 housing Q1 transaction price ; (b)the number of housing completed in Canada 2017, Q1 ; and (c) Canada 2017, Q1, residential transaction ratio.

3.5. User Requirements Analysis In the Canadian and American markets, the contradiction between the high design and construction costs of single-family homes and the time it takes to build them and the high demand for existing homes. According to the data from the Canadian residential requirement survey(Figure 3(a)), there are 1392190 families below the line and totally 1392190+233925+206940 = 1833055 families(2011) need improvement in single house quality. In the house purchasing process, some people in Canda have already taken the high price of architect and construction into consideration. The better choice for them is to sell the old house and purchase a 4

As regards the quality of the existing house, according to the data from , it shows that 84.5% of the single-detached house is built 17 years ago and 55.8% was built 27 years ago. In the real scenario, according to the demographic data(see Table 5(a) ) and house age data(see Table 5(b)), a large number of people are living in houses that much old than themselves. In brief, the high price of second-hand houses and the number of the new-built apartment with reasonable price can not meet current people’s demand for improving the living condition. The main reason for this problem is (1) long construction time; and (2) high construction price, which beyond expectation.

4. Prodcut Design Based on the research above and combined with our advantages, we proposes the home, a product that can complete all the design process((1) site, house plan, and material selection; (2) building model

(a)

(b) (a)

Figure 5: (a) Canada Demograpahic Data ; and (b) Canada House Age Data

(b)

Figure 6: (a) House bubble diagram; and (b) house programs. generation; (3) drawings generation for approval; (4) tables for approval; and (5) production drawings for factories) within 2 hours. In this process, with extremely low learning cost, the user can get the final design of the single-detached house. At the same time, related drawings and tables for other approval procedures are already sent to the related government departments and factories. The fast process not only improve user’s purchase experience but also save the money spent on house design and the unpredictable communication cost. The whole-chain full-digital production process optimizes the production efficiency and slashes the unit cost. The full-digital model makes it possible for the factory to produce construction components((1) structure system and envelope system; (2) Equipment; (3) Electricity and HAVC) directly also greatly simplify the construction process. With the advantage of lower labor costs and lower raw material prices in China and a simple approval procedure of shipping, the prefabrication process can be down in China, and the prefabrication product can be sent to Canada by shipping. Chinese worker can take part in the on-site construction and assembly directly through a Chinese service contract, which further lowers the cost.

(a)

(b)

Figure 7: (a) Deployable direction; and (b) outputs floor plans with same program in different sizes. of the plan. We set all pieces in the model as dynamic components and the Ruby Console.(Figure 8(a))

4.1. Plan Design Based on the researches of existing traditional Canadia detached house floor plans, and combined with small changes in modern lifestyle, we got the floor plan bubble diagram as shown in Figure. This diagram includes all the basic programs needed in a contemporary mid-size single-detached house. Then, based on a high-quality modern single detached house design, we design the prototype of the floor plans, as shown in Figure6(b). In this process, the deployable quality is given more attention because the plans have to be still functional when the size of the bounding box changes. We give different rooms different deployable dimension, as shown in Figure 7(a). General speaking, the deployable process follow rules that (1) keep the width of the main corridor, small toilet; (2) enlarge or reduce the area of living rooms and bedrooms; and (3) squeeze the area of the space that is hard to use.

4.2. BIM Model Generation In Figure7(b), we test several outputs that have different bounding box sizes but the same diagram manually. These outputs are quite satisfying. Nevertheless, when the size of the bounding size becomes too large or too small, the quality of the floor plan will decrease dramatically. So finally the size range of the bounding box width(w) is set to (10,18), and the depth range(d) is (9,15). And then, for the sake of super fast early-stage development and easier communication with Chinese local consulting companies, instead of using the popular parametric plugin, Grasshopper, we first design the 3D model

(a)

(b)

Figure 8: (a) Dynamic component console in SU; and (b) site analysis in Vancouver. Then, we set down the building prototype, as shown in Figure. Next, we turn to Grasshopper, Python, and Archicad as parametric tools. The reason for using Archicad instead of Revit is that Archicad has a more user-friendly interface and requires less amount of computational overhead. Besides, the plugin, GH-Archicad Live Connection, developed by Archicad, provides a straightforward connection between GH and Archicad. Based on the deployable strategy and some basic parameter(Figure 10(a)), we create 3D Archicad parametric model which can export drawings with one click(see Figure 4.2). As for the factory’s production part, it is still an issue under discussion. There are two may approach: (1) Divide the house by pieces; and (2) divide the house by rooms. The first experimental city for this project will Vancouver, Canada, for its great demand and feasibility and flexible policies. As shown in Figure 8(b), the red pard are site which has the size in the domain mentioned above(w:(10,18),d:(9,15)). About 60% of the house sites met our setting. 7

(a)

(b)

Figure 9: Dynamic Model In Su

Figure 11: Drawing outputs with one click in Grasshopper; (a) basement plan; (b) 1st floor plan; (c) 2nd floor paln; (d) east elevation; (e) south elevation; (f) west elevation; and (g) north elevation

(a)

(b)

Figure 10: (a) Selectable parameters for the house and parametric house plan in GHPython ; and (b) Grasshopper and Archicad interface.

(a)

(b)

Figure 12: Different assembling ways 4. Select Material Style material is available, such as Stone, Wood, and Metal.

5. UIUX The UIUX aims to provide a user-friendly interface for every customer. There is a total of 20 steps, as shown in Figure 13. 1. Select Site Select the site of your house(City-District-Street). The app will check the size of your site automatically, and the corresponding design code will be ready for future approval. 2. House Plan The current version only supports one certain house plan with different sizes, as mentioned before. In the future, more house types will be provided. Users can decide the number of rooms and the orientation. The basic program is the bubble diagram shown in Figure 6(a) 3. Select Style Several popular styles are available for different customers, such as Rocco, Modern, and American style. 8

5. Interior Material This step needs you to choose the material for every room in the house. With the interface from Archicad, customers can see the real-time rendering. 6. Select Electric Appliance Since people tend to prefer different household appliances due to lifestyle and purchasing power, this step helps decides the electric appliance. 7. Select Bathroom Material 8. Select Sanitary Appliance 9. Select Smart Home This house will be equipped with a Smart House system, which includes Smart Power Supply, Smart Light, Smart Security and Protection System, Smart TV and Smart Database, etc. 9

10. Home Confirmation Customers can preview the final house through real-time rendering or VR. 11. Comirm the Material Statement The price will be provided together with certain suppliers. 12. Pay 13. Drawing for Approval The client can download the detailed drawing right after the payment. This drawing is generated by pre-programmed codes in the back end. The drawing is only for confirmation. Other detailed drawings for approval will also be generated automatically and sent to related government departments. 14. Day 2: Delivering Drawings to Factories Clients can see all detailed drawings and get the progress information. 15. Day 5: Foundation Construction Clients can keep an eye on the project at any time. 16. Day 40: Factory Production 17. Day 80: Installation in Site 18. Day 120: All construction completed 19. User Acceptance Testing After the acceptance, the client can get the digital key number of the house. This app will also then used for after-sale management.

6. Conslusion This project is our team’s first attempt to combine BIM, parametric design, and Internet products at the same time. Even though the project is still in process, after so many researches and works, we have a clearer understanding of the next generation building product. There is no doubt that the advancement of technologies will liberate people from a complicated lifestyle and provide people with a higher-quality life at a lower cost. To provide the customer’s products within a short time, every chain in this process has to be prepared well. Therefore, the biggest challenge we are facing right now is to coordinate the factories, related governments, and shipping companies. In terms of the technical part, even though Archicad is relatively fast that Revit, but the running time still needs improvement because when the number of customers increases, the stability will be a predictable problem. Secondary development of Grasshopper and Archicad is necessary.

7. Image Credits Table 3.4, 3.5,3.6,3.6 comes CMHC data. All other drawings and images by the author.

References [1] alexa. Website traffic analysis of zillow.com. [2] Autodesk. Achieving strategic roi. [3] Designing Buildings. Bim wiki. [4] Cadalyst. Calculating bim’s return on investment. [5] Friederike Heckmann. Floor Plan Manual Housing. Birkhäuser, 5 edition. [6] Charlotte Business Jounal. Largest area commercial real estate developer.

PolyFrame Force Diagram Dataset Augmentation by VAE (In progress)

POLYFRAME FORCE DIAGRAM DATASETS AUGMENTATION BY VAE

变分自编码器在 (VAE)POLYFRAME 力学图解原型数据集扩充的应用

Ye Huang University of Pennsylvania Philadelphia, PA 19104 tommyhy@upenn.edu

Ye Huang University of Pennsylvania Philadelphia, PA 19104

黄晔宾夕法尼亚大学费城 , 宾州 , 19104 tommyhy@upenn.edu

tommyhy@upenn.edu

Abstract

Autoencoder is a very common algorithm in machine learning. It was first used to find the eigenvalues of image sets and the parameters of compressed images. In this way, the compressed images can be enlarged in reverse to get the images that are almost the same as the original images.This algorithm is then derived and applied to other deep learning algorithms.At the same time, after the development of autoencoders, there are also some better performance of the variant algorithms, such as VAE. In parametric design, a large amount of output is an important characteristic, which helps the architect to choose the best from a wider range of design outcomes.A mechanical graphic prototype of Polyframe (a 3D graphic and static design tool developed by Polyhedral Structures Laboratory) was used as a data sample.This article demonstrates how VAE(Variational Autoencoder) can be used to help enhance the mechanical prototype data set, which can help designers generate the appropriate form in the Polyframe plugin.The algorithm is based on a specific polyhedron diagram and can expand the data set without changing the diagram type.A series of continuously changing polyhedra generated by Vae and their corresponding forms can provide designers with more design choices, which will greatly improve the design efficiency based on three-dimensional static structures.

This paper demonstrates how VAE(Variational AutoEncoder) can be used to help augment PolyFrame force diagram dataset, which can help generate corresponding forms in Polyframe plugins(3D graphic static design tool developed by Polyhedral Structures Laboratory) for designers. Based on several particular polyhedron force diagram prototypes, VAE can help generate new force diagrame prototype dataset without changing the typology. A series of continues changing polyhedron generated by VAE and their corresponding forms can provide more design options for designers, which will profoundly improve the 3D graphic-static-based design efficiency. Keywords: Variational autoencoder,2 Machine Learning, Graphic Static, Polyhedral Structure. 1

Keywords: Variational autoencoder, Machine Learning, Graphic Static, Polyhedral Structure.

摘要自编码器（AutoEncoder）是机器学习中很常用的一种算法 , 最早用于寻找图片集的特征值 , 同时寻找压缩图片的参数 , 这样压缩的图片经过反向放大可以得到与初始图片几乎一样的图片。该算法后被衍生应用在其他深度学习算法中。同时经过发展，自编码器也了一些性能更高的变体算法，如变分自编码器（VAE）。在参数化设计中，大量的输出是一个重要的特征，这有助于建筑师从更多的设计结果中选择选优。以 PolyFrame（Polyhedral Structures Laboratory 开发的 3D 图形静态设计工具 ) 的力学图解原型为数据研究样本，本文演示了 VAE(Variational AutoEncoder) 如何用于帮助增强力学原型数据集，它可以帮助设计师在 Polyframe 插件中生成相应的形体。该算法基于一个特定的多面体力图，可以在不改变力图类型的情况下扩充数据集。由 VAE 生成的一系列连续变化的多面体及其对应的形式可以为设计师提供更多的设计选择，这将极大地提高基于三维静力结构的设计效率。关键字 : 变分自编码器，机器学习，图解静力学，多面体结构

Figure 1. Force and form diagram dataset augmented by VAE

1. Introduction 3D graphic static is a novel geometric method of structural design in three dimensions. Based on graphic statics theory, the generated structures have outstanding mechanical properties and novel forms. The first step of this design workflow is to design the polyhedral force diagram. The next step is to generate form diagrams based on the force diagram. The form diagram can become the structure member guidelines. The final step is to generate members with different thicknesses based on the form diagram. PSL(Polyhedral Structure Laboratory) has developed the plugins which can realize the manual design process. However, because of the strict requirement of the polyhedral force diagram and the long optimization runtime, the design process always takes a long time. The designer tends to spend much time on articulate the force diagram manually and focus on the rationality of the force diagram at the same time. Especially when the typology is settled down, the continuous modification of the current force diagram to the target one becomes a physical job. VAE(Variational AutoEncoder) is a generative machine learning algorithm, which was first used to generate continuous changing images according to the inputs(Figure 2(b)). This exceptional quality can be used to help generate continuously changing force diagrams with a specific encoding scheme. The designer can input several boundary force diagram prototypes as inputs and get multiple intermediate transition-state force diagram prototypes. Then, with further subdivisions, the corresponding forms are generated. Then, the designer can evaluate the mechanical property and aesthetic property of the results and pick up the satisfying one.

(a)

(b)

Figure 2. (a) The architecture of VAE; and (b) numbers generated from VAE.

form diagram results. After getting the satisfying force diagram prototypes, the next step is to add subdivisions to get different curvatures in the generated form diagrams. This step can help enhance the beauty of the form and improve its mechanical properties. With the help of the new Grasshopper plugin, 3D GRAPHIC STATICS, a grasshopper version of PF developed by ETH, the subdivision step can be programmed in different ways quickly, so it is not a big problem now. Therefore, the challenge lies in providing more force diagram prototypes. Even though there are already several 3D model generation algorithms, such as Point Cloud Gan(Li, et al, 2018)[5] that focuses on point cloud genertaion, or 3DGan(Wu, et al, 2016) [11] that can generate simple mesh models , however, none of them can help generate polyhedrons that can meet the force diagram requirement as mentioned above. Our strategy is to encode the force diagram into a series of digital codings. Then, input these digital codings into generative models to get new digital codings and decode them into new force diagrams. Take example.1(Eg.1), four simple triangular meshes as an exmaple to illustrate(Figure 3). These four meshes have different forms: different scales and orientation. However, they have the same topology, so when deconstructing them into vertices and faces information, they in fact have the same faces information. What are different are their vertice coordinates. So as long as two meshes have the same topology, they have the same faces information. It is the same with rectangular meshes or 3D meshes. Therefore, when change the mesh vertice coordinates and keeping the faces information, we can create different forms with the same topology. This is the core rule in the following force diagram prototypes generation.

Figure 3. Mesh deconstruction: (a) four different triangle meshes; and (b) vertices and face information of the four meshes

Because of these strict requirements, until now, the most popular way of generating force diagrams is still modeling polyhedral prototype models as force diagrams in Rhinoceros and articulate them based on the generated

In Eg.1, after the meshes are deconstructed, each mesh have three vertices and the coordinate of each vertices consists three numbers. So each mesh can be represented by nine numbers and the faces information(Figure 3). Since we want to keep the topology, the faces information will be kept unchanged. Only the vertices information will be extracted. As a generative network, VAE can generate new information based on inputs. So our strategy is using mesh vertices information as the input training data, and use the generated output data as new vertice coordinates. In this situation, the vertices are all new. Combining these new vertices and unchanged faces information, meshes with new forms and the same topology are created. We input four classes of meshes(Figures 2) to the VAE network. Only four meshes in Eg.1 are not enough for the training. To augment the training dataset, aftering encoding each mesh to the 9*1 array, each number in the array will add a small random number and the new array becomes a new input. In this process, the form of the original triangular mesh will change a little, but this change will not change its original classification. In Eg.1, the original 4 meshes are augmented into 2000 mesh input after this operation. After 400 epoches of training in VAE network, continues changing meshes array are generated as shown in Figure 2 . Different training parameters

2. Methodology In terms of the force diagrams design, one of the most challenging issues in augmenting the force diagram dataset is to change the form of the force diagram without changing its essential topology since it also decides the topology of the final form diagram. A force diagram tends to consist of several polyhedrons. There are several restrictions or requirements of the force diagram, such as (1) thes force diagram should be closed meshes or breps; (2) each surface should be planar; (3) all edges should be connected well; and (4) less side forces are excepted, etc. A minor error in the force diagram may lead to the failure of the form diagram generation.

Figure 6. Inputs simplification (a) a simple tetrahedron, and its vertices and faces information; (b) cull duplicated information; and (c) flatten point coordinates into a 12D array as the input.

Figure 4. Information extraction process of a simple traingle mesh: (a)

lead to different outputs. Compared with the four original input meshes, many brand-new triangular meshes are generated.

(a)

(b)

(c)

Figure 7. Example 1 force diagrams generation: (a) polyhedral inputs with same topology; (b) generated polyhedrons; and (c) clustering results.

(a)

(b)

(c)

Figure 5. (a) & (b) & (c): Triangle meshes generated from VAE with different training parameters

With some preprocessing, this strategy can be applying 3D meshes easily. Take example.2(Eg.2), a simple tetrahedron(Figure 7(a)) as an example to illustrate. A tetrahedron have four faces and for four vertices. However, because of some meshes are non-manifold or not closed, there will be total 12 vertices when deconstructing this tetrahedron. So the first step is to delect duplicated vertices. The final mesh information contains four vertices information and four faces information. Then the four vertices are used as one VAE training input. Eg.2 has four classes(Figure 2). Every two meshes in one class have different height. Different classes have different orientations. In the generation result in Figure 7(b), smooth changing process of 3D mesh orientations and heights showes up. A lot of intermediate-state meshes are generated. The clustering results in Figure 7(c)also shows the success of the training process. A generated mesh prototype with different subdivisions can generate totally different forms. Some normal subdivison types and examples are shown in Figure 2(a). For simplicity and clarity, Subidvison Type1 is used in this paper. The subdivied polyhedrons are shown in Figure 2(b) & ()c). Then the subdivided force diagram can generate the corresponding form diagram and finally, the pipe form. The thickness of each member of the final pipe form is based on the form diagram. The complete process final form generation is shown in Figure 2. Some 4

other examples are this process are shown in Figure 2. We also try this strategy in example.3(Eg.3), a simple polyhedron prototype consists triangular meshes and quadrangle meshes. There are three classes. Meshes in one class have the same bottom and top faces, what are different are the face scales. Meshes in different classes have different bottom and top faces. After training for 400 epoches, the generation results in Figures 12(b) show continues changing results. The great clusting result in Figures 12(c) prove the success of the training process. Similarly, the generated meshes can be used as force diagram prototypes to generate corresponding forms (Figure ?? & Figure 2). The closer look of the generated forms through VAE are shown in Figure 2.

3. Runtime In terms of the runtime, because of the small size of the input data, the runtime tend to be very small. As shown in the Table 3. training dataset in Eg.2 for 400 epoches in Keras Framework only takes 10 seconds. In this aspect, this strategy can be really helpful in future quick force diagrams augmentation process. Dataset Eg.1 Eg.2 Eg.3

Epoches 400 1000 1000

Table 1. Training Runtime

Dataset Size(np.array.shape) [2000,9] [2000,12] [2000,42]

Class 4 4 3

Runtime(s) 5 10 18

(a)

(b)

(c)

(d)

Figure 8. (a) Different subdivision types of a simple hexahedron (Image Courtesy:Effect of Subdivision of Force Diagrams on the Local Buckling, Load-Path and Material Use of Founded Forms(GHOMI, et al)[10] ); (b) a closer look at a hexahedron with subdivision type 1; and (c) a closer look at a tetrahedron with subdivison type 1. Figure 11. (a) Form diagrams of example.1 generation results ; and (b) pipe forms.

Figure 9. Generation process of a tetrahedron with the PolyFrame Plugin (a)

(b)

(c)

Figure 12. Eg.3 force diagrams generation: (a) polyhedral inputs with same topology; (b) generated polyhedrons; and (c) clustering results.

Figure 13. Generation process of a the example.2 polyhedron with the PolyFrame Plugin Figure 10. Generation process: (a) original breps ;(b) breps after type 1 subdivision; and (c) form diagrams and pipe forms.

4. Conclusion and Future Work This augmentation strategy can help generate more force diagrams and form diagrams based on certain inputs. The outputs also show close relationships with each other. The designer can have more options in a short

[6] Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas Guibas. Grass: Generative recursive autoencoders for shape structures. ACM Transactions on Graphics (Proc. of SIGGRAPH 2017), 36(4):to appear, 2017. [7] Akbarzadeh Masoud, Van Mele Tom, and Block Philippe. 3d graphic statics: Geometric construction of global equilibrium. In Future Visions. Proceedings of the International Association for Shell and Spatial Structures (IASS) Symposium 2015, 2015. [8] Akbarzadeh Masoud, Van Mele Tom, and Block Philippe. On the equilibrium of funicular polyhedral frames and convex polyhedral force diagrams. Computer-Aided Design, (63):118–128, 2015. (a)

(b)

Figure 14. (a) Form diagrams of Eg.3 generation results ; and (b) pipe forms.

time when articulating a force diagram. In addition, as metion above, the training time of the network can be in a extremely short time because of the small input dataset size. So this strategy can be applied in future plugin development easily. On the other hand, a force diagram will be first modeled in breps. These breps will be transfered to meshes. All brep surfaces in the two examples in this article are all triangles or quadrangles. They can be transfered into meshes faces that are constructed by three or four vertices. A mesh with more than four vertice will be deconstructed to several small standard meshes. However, there are some conditions that the number of brep surface vertices is large than four. For instance, all faces in a dodecahedron are pentagons. In order to apply this strategy to the dodecahedron, all faces have to be to be converted to triangle meshes or rectangle meshes. In this condition, the topology of the former dodecahedron is changed. Corresponding, the basic stress condition of the force diagram is changed. So our strategy now is limited to force prototypes that modeled well with triangle or rectangle faces. The future work will be extending this strategy to all force diagrams.

[9] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv preprint arXiv:1612.00593, 2016. [10] A. Tabatabaei Ghomi, M. Bolhassan, A. Nejur, and M. Akbarzadeh. Effect of subdivision of force diagrams on the local buckling, load-path and material use of founded forms. In Proceedings of the IASS Symposium 2018, Creativity in Structural Design, MIT, Boston, USA, July 16-20 2018. [11] Jiajun Wu, Chengkai Zhang, Tianfan Xue, William(a)T Freeman, and Joshua B Tenenbaum. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances In Neural Information Processing Systems, pages 82–90, 2016.

References [1] M. Akbarzadeh, T. Van Mele, and P. Block. Three-dimensional compression form finding through subdivision. In Future Visions. Proceedings of the International Association for Shell and Spatial Structures (IASS) Symposium 2015, 2015.

(a)

[2] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Networks. arXiv e-prints, page arXiv:1406.2661, Jun 2014. [3] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. CoRR, abs/1812.04948, 2018. [4] Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Dec 2013. [5] Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov. Point cloud gan. arXiv preprint arXiv:1810.05795, 2018.

(b)(b) Figure 15. (a) & (b): A closer look at the generated final pipe forms of example.1 and example.2

Figure 15. (a) & (b): A closer look at the generated final pipe forms of example.1 and example.2 8

9 10

ANTI-PAVILION

阴阳亭

Competition Project Date: 2017.4 Site: Urumchi, Xinjiang, China

竞赛项目 Date: 2017.4 Site: Urumchi, Xinjiang, China

Parametric design is now a hot topic in computeraided design. Parametric design provides strong support for the study of variables in the everincreasing world complexity. Basing on the traditional Chinese classical book “Zhou Yi” and its core ides, Yin Yang, focusing on Pavilion Structure as the main study object and the start point, we attempt to set aside shackles of underlying parameters, embracing the uncertainty, take a top-down approach and combine the tradition with the basic working principle of contemporary computers, to expand the study of forms and structures of architecture. Deriving a series of logical possibilities to, we experimentally find the sustainable possibilities and surprises of new Chinese architecture.

参数化设计是计算机辅助设计的一个热点。参数化设计为在日益复杂的世界中研究变量提供了强有力的支持。以中国传统经典著作《周易》及其核心思想《阴阳》为基础，以亭子结构为主要研究对象和出发点，试图抛开潜在参数的束缚，包容不确定性，采用自上而下的方法，将传统与当代计算机的基本工作原理相结合，拓展了对建筑形式和结构的研究。通过一系列的逻辑推理，我们实验性地发现了中国新建筑的可持续发展的可能性和惊喜。

The figure shows one of the hexagrams images, through the diagonal made from the midpoint of the quartet towards the four corners, then moving clockwise or counter-clockwise. With the analysis, I extract clockwise and counterclockwise unit.

(0, 0, -1) (1, -1, -1) (2, 0, 0) (3, 1, 1) (4, 0, 1) (5, -1, 1) (6, 0, 0) (7, 1, -1) (0,-1,-1) (1, 0, -1) (2, 0, -1) (3, 0, 1) (4, 1, 1) (5, 1, 0) (6, 0, 0) (7, -1, 0)

Shown above is another form of hexagrams image , but also to make the midpoint of the quartet go toward the horizontal axis and the vertical axis and reach the midpoint of the top and bottom outline of quartet, and then do a clockwise from point or counter-clockwise motion . (0, 0, 0) (1, 0, -1) (2, -1, -1) (3, 0, 0) (4, 1, 1) (5, 0, 1) Follow the steps, I get the coordinates of each point of a pattern of circular movement , and has been placed on the Y- Z axis plane, and then take the X-axis as the direction of circulation .

(0, 0, -1) (1, -1, -1) (2, 0, 0) (3, 1, 1) (4, 0, 1) (5, -1, 1) (6, 0, 0) (7, 1, -1)

Then I divergence out the points in loop section toward the X-axis and get a series of dot matrix.Through the analysis of dot matrix , I think the first set of patterns are most likely to develop in the direction toward the building component , because the locus of points included compared with the other two is more uniform change in control, the direction and movement also has maintained a stable angular velocity.

The forabove is shown the morphological development process from the first point to the end point. The left shows the record of the projection of this process on X-Z axis plane. From this record , we can see the characteristic process from three-dimensional space on a twodimensional drawintgs. The process from point to the line, then to curve, and to the surface is more intuitive proof our extracted information from " Book of Changes”, and it is very suitable for the development of body mass on the build

In the preparation process of the GH , starting from the same point , dealing these points by a data processing system, we got the data set for the streaming of a different structure . Depending on the parameters of each structural features required to set out the qualifications needed to filter the data , and based on the relationship between them, these data finally reorganize together. It was how we get the truss structure lines from this form.

Deep Church

' 深度 ' 教堂

Individual Research Project Date: 2019.11

个人研究性项目日期 : 2019.11 月

This Project tries to use most basic and popular machine learning algorithm to generate some interesting form. In this design. Basic algorithms such as Deep Convolutional GAN is used to generate new images of church sections and church rose windows. Then, I put the generated images into the style-transfer algorithm to generate a serious continuous changing section. With the section and Monolith, I can generate a complete church building with interesting section at every different section cut.

这个项目尝试使用最基本和流行的机器学习算法来生成一些有趣的形式。在这个设计。 Deep Convolutional GAN 等基本算法被用于生成教堂部分和教堂玫瑰窗的新图像。然后，我将生成的图像放入 style-transfer 算法中，生成一个严重的连续变化的截面。通过剖面和 Monolith，我可以生成一个完整的教堂建筑，每一个不同的剖面都有有趣的部分。

03 RAW INPUT DATA

CHURCH MODELS GENERATED FROM SECTIONS

EPOCH: 0

RESULTS OF STYLE TRANSFER ALGORITHM

EPOCH: 300

With the continuous changing style section drawings, we can combine a series of them to generate mesh models. By transfering the drawings to grey images, and then use specific algorithm to get the brightness information and location of each pixels, we can images which only contain two colors: (0,0,0) and (256,256,256). Then map them to mesh models. So when cutting the final models, you can see different styles of the same section. People walk inside can experience the beautfy of 'Style Transfering'

EPOCH: 800

EPOCH: 1000

GENERATED DATA

tetN eN GG AteA SNSrG orfA otfS utpurnopifnsitluseplxenoxivosv)lec)x(cod(vnda)(a) nca;(s;pdsenpraeb(a) r;bospno(a) einhribrh)rob(a) n)(bih;(m r;m e)bt(a) es(tys;sm yos(a) enotsinm yism oodond)iam )(ao.(5d.5e)raeu(rgu.5igFieFrugiF

3. Structure-Aware Generative Network

3DNetwork MODEL GENERATION 3.1. Overview

(a)

基于 SAGNET 的 3D 模型生成

BASED

)c(

ON SAGNET

SAGnet SAGnet is another important 3D generative model[Wu et al. (2018)][12], which was published in 2018 ACM. The Individual Research 独立研究性项目 model combines severalProject crucial existing machine learning algorithms, such as RCNN, VAE, etc. In this model, given a set of Date: 2020.3 : 2020.3 relationships between them (the structure) segmented objects of a certain class, the geometry of their parts and日期 the pairwise Forjointly architects, 3D models more comfortable to dealspace with compared 2D 对于建筑师来说，模型比 2D 图像更容易处理。然而，目前 are learned andare embedded in a latent by an with autoencoder. The encoder3Dintertwines the geometry and structure images. However, now most machine learning are about 2D images, the 大多数机器学习算法都是关于二维图像的， features into a single latent code, whilealgorithms the decoder disentangles features and reconstructs the geometry很少有机器学习 and structure of and few of them have structural information, which is important in architecture 算法具有结构化的信息，而结构化的信息在建筑设计中非常 the 3D model. The autoencoder consists of two branches, one for the structure and one for the geometry. The two branches design. Structure-Aware Generative Network(SAG) is a new machine-learning 重要。结构感知生成网络 (SAG) 是由深圳大学 VCC 团队提 exchange information between each other and learnIt the dependencies between the structure and geometries, and encoding algorithm proposed by a team from VCC, Shenzhen University. can generate 出的一种新的机器学习算法。它可以根据模式内不同部件的 a relatively big 3D features, model based on theare structural two augmented which then relationship fused intoofadifferent single parts latent code. (Figure 3) 结构关系生成较大的三维模型。本文论证了利用结构感知生 inside the mode. paper demonstrates the possibility of usingthe Structure-Aware 成网络帮助生成架构模型的可能性和这种新网络的局限性。 Because of theThis structure-aware quality of SAGnet, relationship between different parts in a single model can be learned Generative Network to help generative architecture models and the limitation of this new network.

)b(

)c( )c(

)b( )

teNtG AG S rof tupni slexov )c( dna ;sperb onihr )b( ; eN teNAGSAroSfrtoufptnuipsnliexsloevxo)cv( )dcn( adn;sap;esrpbeorbniohnr i)

)c(

(a) (a)

Domino Output Group 1 (10000 Epoches) (a)

)b(

(b) (b)

(c)

(b) (b)(b) (b) (b)

(b) (b) (b)(b)

(b) (b)

)a(

teNGAS rof tupni slexov )c( dna ;sperbFigure onFigure ihr )5. b((a) ;5. m(a) edomino tsydomino s onisystem; mosystem; d )a((b) .5 e(b) rugrhino iFbreps; rhino breps; andand (c) (c) voxels voxels input input for for SAGNet SAGNet Figure 5. (a) domino system; (b) rhino breps; and (c) voxels input for SAGNet

Domino Output Group 2 (10000 Epoches)

(c)

After iterating over 10000 epoches , SAGNet can be used to output the form which has a small number of elements (less than 4) with a certain degree of complexity,.Output Group 1 is a simple 2-layer, 1-span domino .te.tseastaa asdeaheta thd tntenh m etm gtn uge aum aog tou )t2a).21o.1to)iot2ai.tr1 a&ro&ditnd .11n.& 1a& o1iot.ai1tra& :n ro:onitiosain e y,2,i.d 21.y1o,iot2ai.tr1 a% ro% oiot1ai.tr1 a:n ro:onitiosasystem. in e idxe(m xg(n inlaxilc(agscnGroup sdilnd 73-layer nd7 an2◦a0◦d80n18a1◦08 .td e aan r&a1d rs:enm om iisdnidyem i1ta.11r.1% rs:enm om iisdn igd aan csa)2◦d)0is◦n70a )2d◦0 While output a2 2-span domino system, It's more complex stwere upsntui pmade metsyon stsothe 6De. than Group1.Statistics yentssim oynsoioD mnoi.m stnuipm niem o results of 10000 epoches training, and the Slab (d) (d) (d) (d) (d) (d) availability of the output results of Group 1 Slab Column Figure 8. Generated voxel results ofresults three(a) catagories: (a) generated domino systems; (b) generated two-story domino systems; (c) generated Figure 8. Generated of three (a) generated (b) generated two-story domino systems; (c) generated Figure 8. Generated voxel of three catagories: (a) domino systems; (b) generated two-story systems; (c) generated Figure 8. Generated Figureresults Figure 8. Generated voxel 8. Generated results voxel of three results voxel catagories: results of generated three ofvoxel catagories: three (a) generated catagories: generated domino (a) catagories: generated systems; domino domino (b) systems; generated systems; (b) domino generated two-story (b) generated domino two-story two-story systems; domino domino (c) systems; generated (c) generated (c) while generated Column Stair_1 was around 73systems; percent, that of Group 2 .tfloor e(d) s.taplans dtaedh(d)tethntetm gm u(d) aStair_2 t )o2t.1)2was o.1itaoabout ri&tad adn 1a.11&.o1i& taori:tnaor:in sn id yid,2y.1,2o.1it furniture layouts; and (d) generated floor andplans (d) generated furniture layouts; (d) generated floor plans Stair furnitureand layouts; furniture furniture and layouts; (d) layouts; generated andfurniture (d) and generated floor (d)layouts; plans generated floor floor plans (d) plans (d)a (d) percent. etsa nte gm uoa rit&na62 o:e insm . t e s a t a d e h t n e g u a o t ) 2 . 1 o r & d n a 1 . 1 & o i t a r oniesm nemid y ,2 Domino Output Selection (15000 Figure 8.Figure Generated Figure 8. Generated voxel Figure 8. Generated results 8.Figure voxel Generated ofvoxel results Figure three 8.Epoches) Generated results voxel catagories: of 8. three Generated results ofvoxel three catagories: (a) ofresults catagories: voxel generated threeresults (a) catagories: of three generated domino (a)ofgenerated catagories: three (a) systems; domino catagories: generated domino (a) systems; (b) generated generated domino (a) systems; generated (b)systems; domino generated two-story (b) generated domino systems; (b)two-story domino generated systems; two-story (b)systems; generated domino two-story (b)domino generated systems; (c)two-story generated domino systems; two-story (c)systems; domino generated (c) generated domino systems; (c) generated systems; (c) generated (c) generated (a) (a) 5. Conclusions And Future Work 5. Conclusions And Future Work 5. Conclusions And Future Work furniture furniture layouts; furniture and layouts; furniture (d) layouts; and furniture (d) and generated furniture floor (d)layouts; and generated plans (d) layouts; floor generated and floor plans (d)and generated plans floor (d) generated plans floor plans floor plans 5. Conclusions 5.generated Conclusions 5.layouts; Conclusions And Future And And Future Work Future Work Work

stsutpunpinsm itum eptenstyissm yosenotsinm yism ooD onDi.m 6.6eorD eurgu.6igFieFrugiF

Figure 3. Architecture of SAGNet. (Image courtesy: SAGNet) well, which can be of great use in the study of architecture design. Take Figure ?? and Figure ?? as examples. These two figures are the generated results of the basic interior layout designs. After we get these results, we can use them directly as a design reference or use the generated bounding boxes, which represent location and scale information of the small parts in a big model to help us in further interior design. SAGnet SAGnet is another important 3D generative model, which was published in 2018 ACM. Themodel combines several crucial existing 3.2. 2-Way VAE machine learning algorithms, such as RCNN, VAE, etc. In this model, given a set ofsegmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure)are jointly learned and embedded in a latent space by an autoencoder. The

As shown in Figure ??2 way), upperfeatures branchinto in a the algorithm architecture is intended for processing theand geometry, while encoder intertwines the geometry andthe structure single latent code, while the decoder disentangles the features reconstructs thelower geometry and structure of the 3Dstructure. model. TheThe autoencoder consists of two branches, one for structure and layers one for on the the geometry. The side, the branch processes the geometry branch consists of five 3Dthe convolutional encoder two branches exchange information between each other and learn the dependencies between the structure and geometries, and encoding two accepting series of k(32*32*32) voxel aslatent input. The 3D convolutional layers down-sample the voxel maps by a ratio augmentedafeatures, which are then fused intomaps a single code. of 16 and are followed by a fully-connected layer to compute k 512D features. In parallel, the structure branch has a fully.tesaby tadthe ehencoder t tnemgare ua connected layer to process K pairs of bounding boxes, producing K 512D features. The features output fed into two different GRUs. These GRUs account for the relationships between parts in terms of geometry and structure, exchange information between them using the Geometry and Structure Attention components and eventually output the k 512D features and K 512D features to the 2-Way VAE. Finally, the decoder echoes the encoder with five 3D deconvolutional

(a)

(b) 3

ot )2.1

(b)

(c)

Figure Figure 6. Domino 6. Domino system system inputs inputs

180 and 270 ) and scaling(x dimension:ratio 1.1% ratio 1.2, y dimension:ratio&1.1 and &ratio 1.2) to augment th

but also but thealso but valuable also the butvaluable the information also valuable but theinformation also valuable but about information the alsovaluable the information the about structure valuable about information the structure about the of information buildings structure the about ofstructure buildings about or of thebuilding buildings structure the oforbuildings structure building parts. orofbuilding buildings This or parts. ofbuilding buildings two-way parts. This or building parts. This two-way or neural building two-way This parts. network neural two-way parts. This neural network structures, two-way This neural network two-way structures, network neural structures, neural network structures, network structures, structures, 7 7 7 7 7 7 each of which eacheach offocuses which of each which focuses onofexplicit each which focuses on of each focuses targets. explicit which onofexplicit which focuses targets. on explicit targets. focuses on targets. explicit on explicit targets. targets.

)b)(b( )b(

)a)(a( )a(

tutpunpintituutoupyonayilateul roeuyrtuaintlirneurfurtfionriorueirtfenrtino)7bi)r(bedt(nndia)nb7a;(tu;dtoun7yoaay;lateul7roeuyrtuaintlirneu7rfurtfionri7orueirtfenrtino)iar)e(at.(n7i.7e)raeu(rgu.7igFieFrugiF

(c)

Figure 5. (a) domino system; (b) rhino(b) breps; and (c) voxels input for SAGNet (a)

stupni metsys onimoD .6 erugiF

With theWith help of help SAGnet, we can now generate architecture models that have not only a similar language withmodels input the SAGnet, we can now generate architecture models that have not only amodels similar withmodels input models With the help SAGnet, we can now generate architecture models that have not only athat similar input models Figure 6. Domino system Withof the help Withof the With SAGnet, help the of help we SAGnet, of can SAGnet, now we generate can weof now can generate architecture now generate architecture models architecture that models have models not only have thatlanguage anot have similar only not language awith only similar ainputs similar language with input language with input withlanguage input models but also the valuable information the of or neural building parts. This two-way neural network structures, but also the valuable information about the structure buildings or network building parts. This two-way neural network structures, but 5. alsoConclusions the valuable information about the structure of buildings orstructure building This two-way structures, butConclusions also the but valuable also but the also valuable the valuable information about information the about structure about the structure ofabout the buildings of structure buildings or parts. of building buildings orbuildings parts. building orofbuilding This parts. two-way parts. This neural two-way This two-way network neural neural structures, network network structures, structures, 5. Conclusions 5. 5. And Conclusions Future 5. And Conclusions And 5.information Future Conclusions Work And Future Work Future And Work And Future Work Future Work Work each ofeach which focuses on explicit targets.targets. ofexplicit which focuses on explicit each of which targets. each focuses of which eachon focuses each of explicit which of which on focuses explicit focuses on targets. on explicit targets. targets. ◦ ◦ ◦ ◦ ◦ ◦ 0 1language oita r180 & the d nWith a 1and .the 1270 &SAGnet, othe it)ahelp rand :the n)◦we oof isscaling(x nthe eWith mSAGnet, idcan yofhelp ,2SAGnet, .we 1dimension:ratio o itgenerate awe r% 1we .now 1 o1.1% igenerate tnow aarchitecture rthat :ngenerate oratio imodels sarchitecture nratio enot m ionly d1.2, (not g nonly idimension:ratio&1.1 lhave amodels conly slanguage dn asimilar )language 0similar 7language 2input dan amodels With help of help With of SAGnet, With help can SAGnet, of now we help the generate we now can now generate ofarchitecture can SAGnet, now can architecture generate models architecture can models have that models architecture have that adimension:ratio&1.1 models that not a that similar not have athat only not have a with only not with only language similar with input a8 similar input models with language models input with models with input models 180 and 270 and scaling(x dimension:ratio 1.1% 1.2, yxhave ysimilar and and &ratio &ratio 1.2) 1.2) toinput augment tomodels augment thethe da ◦With

(c)

Figure 5. (a) domino system; (b) rhino breps; and (c) voxels input for SAGNet

egergarlael g herthaftol feoehsetusfa ucaeecsbeubsaecsiero irgosegeteiarto cagceeetra ehcrthet eherthhgttngeonhm ot m anaeonem onaoxexln epo lm pm tosom ocme st isehihchtichsiw hi w plpr,n ora olflopflerco eno cen sresrdiissyieryo cagcderdtih ritchetdherT o b g xoecolpctsm tsheothm h,cn,ian hla w fldeeidscien rrgsoe igytearto a ihhTt ehT

)ebe The author chooses two categories of datasets in this paper: domino system and interior layout. These two are the most common and)6)(6(;m (r)e a(o (b) o;o)1ar)o(adm d beob)r1d)(1e:l(be:lde)1 od(m o:lm e)tbm lep( l)m pbe(m o neoncone inn si tosra tnrpai pslltalrlm m s slflo oresrbefm bom urn d(teo tecolpcem ap afm eubnmu ;)m o6o( ro;m g r ngoino v(b) irvl ig)l5n)(5 ik)h4)c(4ti(;k3;)3m o;o3rodm redobeob)r3d)(3e(;b2;)2m o;o2rodm redobeob)r2d)(2e(;b1;)1m iv(;inl ;e)n5he(cht;cinkte 4(m 3(m 2(m Figure 5. as (a)inputs domino system; (b) rhino (a) in a common (b) breps; and (c) voxels input for SAGNet (c) regular architecture elements residence building.

e;ar)1ulm tio r1turiofntm ruosofrieorr)ero7tt)nis(7 i)bn;()m a;dm ;tuoyaioldn eru ,ta,tflaflsi,stta iefltseastiaatd asdnaantalapdlprnoraolflopflero heo thflh t gheughout hohthgn tuenovehevt e,nse,esvdeeids,iesB eB dtust.ys)d0)u10t(1s(d)nd0an1a(;yd;aynw aaw k;lka w aw w )9k)(9 2;)2m ot;uo2protntm sruitepsottruneuooir)pr8ytn)us(a8ioel(;tyr1 e d.iys.deyuB yla la(;w 9(m rnlu;o )dtbno(n(iro uo8ye(am erusrrtte inruf rreo(;trm ireio7 )brgn(ngoadinonn;itrianudg ;tyuaionl i ybybnrnayreablen ol rtoarterldeordatrharehsdisrsai m sm osoi rsom trntoenoreerrfetfn ifdfeidn w ew tnetebeebpwiph rita helthe,trs,d o,swrderh etw hotorneInhI.tdo.ednteaItca.idclpe ilm ptam occo reoorm ocmesrisoliem ldeod ftofeoehreturftuoctucerurtu srttse herthtst e h reenfe fid teishbnson pio ithaitslaenlre oe esrhdotrw o ilpcem sm iolm eedheothm cu egreagl reahl tefh ot efo suacueabce se irsoegireotgaecte e t rehht tegnogm am enoen xelpxe mlp om c toscotm em ht bctd crttorareh se o ereorm omeerveoavm hahh hw ollw alla,froe,rp teptN eaN G AteA SNSeGheAthSnt inedihedt senusi ua inndiinag ieatngre sentsseauasata asb doaw td rm foe iw tehiw pm coepcnkm rkoo rxw ocew tlekptnrem thtn ecvhiachhiw hfcofih lelappa,rpe pG dteaastdaudgangta itrnaheia rdtthndetanhe sa e wem f th ina wropam dm onew lthe fa otstde e sota ew irrtoo gea ceefm eheerthorhth te htdn thedo gtre aora o oh cte st oee m o o rd ) 6 ( ; m o o r g n i v i l ) 5 ( ; n e h c t i k ) 4 ( ; 3 m o o r d e b ) 3 ( ; 2 m o o r d e b ) 2 ( ; 1 m 5 h ipa kctept)e ;T 3o oiT (n)lr3 ;2(rietar;e m tetnernertetne IrehethnInterior m tI m sfmnsoanrlap lp 4d0ad0oa4lonEpoches) lw na w oodold toortedrlew at ra hos(thu)u6t;au ho etutro ha,o tgt)e ,aet(sie et(ate h;de tnrce eeath rh .p phroisdho.nesrob n e u reto ucm tucerorudrtou srerttsb ct)ib2 eohroftrLayout fOutput s0n0a40lp(10000 d now olw rcerclewheathrcst esesehust urso)er6 r(oaeo ,v)td e p p ieth)aib3tslae o ro u r)clatp(is())laxp2;(e t1)x(iace(;m i1 lpox ;hm rn ghvasntiltaia ial (sd)ea5;hn irek4prr(ap )o4 (eTr;m 3s.opso m dio 2l(b) dctie )tbIn(e (a) (a) (b) , t a fl s i t e s a t a d n a l p r o o fl e h t h g u o h t n e v e , s e d i s e B . y d u t s ) 0 1 ( d n a ; y a w aaco flarasE i tesltla aandltm rllap olo flo heo totrafter n ,e soheeth,dsm e0hiB .o(b) y2pd0u.puy5utdepoches, em em asaesem T hnsTi.ettur.ha torTpa vdriuundd rete orroooirorem rh,ttcnh p rao erSimilarly, thflu w to a ad dw oteto treiterating ftio fedo 25fB ks2uck)tipcp i1 p)d(0knd1 inidraenstiansnia)dabed(stedaatneaad aisoeeiyrcaetlacsepa(a) irspuestrcio arropumsfm o anm uasnln aa srrn phflte o eh avnm te10000 cdani(npaa eautn su d nsp anm p o(b)interior oelflflo h tg h gw urh h edm ve esover s50 s0u ddn;n th uepa yapl.latleruaardu upitdvilnia finirnivoa ;rtsttuFigure n7. i,t)E afurniture (flh..yc7s.layileaE ru g.ya ilayout; Flaa Figure 7. (a) (a) interior furniture layout; and and (b)interior furniture furniture layout layout input input yinterior binterior n r a e l o t r e d r a h s i s m o o r t n e r e f f i d n e e w t e b p i h s n o i t a l e r e h t , s d r o w r e h Figure 7. (a) furniture layout; and (b)interior furniture layout input SAGNet can be used to output system with bih restad hrreae ssrhw ieusw tantrd e nd re eirq hnsiteh .te.tseastaa asdsaishta ithdtnytsen m eyntm ugne aurla aooeg totl u dtroe serou ue so ewarsota aetsdrn oe w oftfraw tiedrtfe m eewm rtow fm etrth uin stiau sau n,hcu hsteq cd,te .td e n m adt d d ei trsseoetm so e ae slq u e irsnotdhwrco bgrta ssm fnriod n erfoe eteof whb eopntbfiihnepdsihie htsoe snand osdliee trelatively e aNcertain number elements eroemroemvaehvahhcih w f o l l a , r e p a p t e G A S e h t n i d e s u a t a d g n i n i a r t e h t d n a hnhceihd hciw raegp tp edcomplexity, N S hsuch teh nti as d sdueroom ignniainrtiaerthte hefw ,era etap plow ne iflat sautaadlayouts, tagdnbecause egral eht fo esuaceb seirogetac eerht eThe ht The gthird nom acategory encategory o xelpis mresidence oisc residence tsom ehfloor t sfloor i hplan, cihplan, w which ,nalperroo othe fleevthe camost sicomplex rocomplex sfliloaylr,lo caamong rtie hGN tAeG hATeSthe third ism the three three categories because of enrewhich tn hI tis m othe rf most pa0 0400done aoone la none wlonamong damong odtoutput roetlw ca ercategories et ssueLayout in terms of The third category is residence floor tplan, which is most complex three n eIente em m osm rnfoasrln n o rthe eatresults.Interior lrw chatrecshcategories srsouehsrtuouhartobecause uehahtutea,htet tre5bedroom Ihto ech te f(2) ara lpp4 0ll0adcompared 4m ds(3) a no wtobobedroom d uo re3; litw ehkitchen; t elements mocomplete ordeb model:(1) )1(model:(1) :ledtoem trnee l5tpn n isslnptbedroom foowl(3) re m n )6( ;moor gnivil )5( ;nehctik )4( ;3 mnumber oonumber rdeb of )3(small moparts orparts debin)2one 1complete 5 of;2small in( ;one bedroom 1; (2) 1; bedroom 2; 2; bedroom (4) 3; (4) kitchen; (5) (5) living livin r Domino, has more e m a s e h T . t r a p l a u d i v i d n i n a s a d e t a e r t s i e c a p s r o m o o r h c a E . y l l a u number of small parts in one complete model:(1) 1; d(2) bedroom 2; (3)erbedroom kitchen; (5).yliv soaretsshbedroom .8t r(a.t;p1 iuvrd arelatively aepcsa3;prelations rso(4) om o(there roohrcahE ln l. eaom erh)T r(10) ala pu(10) ivenirid(c) nBesides, iand sodaoertdaeven t escshape ithough rm o cdatas aEda ,tafl si tesatad nalp roofl eht hguoh(c) tdining nedining ve room; ,seroom; dise(7) B (7) .restroom ydurestroom ts )01(1;dn ;restroom yaw klaw)92;( ;(9)walkway; 2em m eT m olaostudy. tisdstudy. )n 7a (nsBesides, ;am gentasimple iteven neisrdithough (8) 1;a (8) restroom 2; (9)walkway; andand the the floor floor plan plan are no three-dimensional components such .tes.tathough d asd ihthe tsithntefloor m gm uaplan t dod Besides, even dining room; (7) restroom 1; (8) restroom 2; (9)walkway; and (10) study. etsa th nte gm uoa .taetsrooms aThe tarooms dresults siis tneharder guto asehole in Domino). yb nrael ot redrah si smoor tnereffidthe nethe e wtstructure eb pihof sno lmodel er model eht ,issdmore risow recomplicated. htcomplicated. o nI .detacIn ilpm c ewords, rom si lthe edthe orelationship m relationship eht fo rubetween tcand ubetween rtsstairs ehtdifferent structure the ofitathe more other Inoother words, different harder is the structure of the model is more complicated. In other words, the relationship between different rooms is harde of 10000 epoches training are statistically erom evah hcihw fo lla ,repap teNGthe ASthe ehtnetwork ni decompared su compared atad gnwith iniawith rtthe ehthe tformer dnformer a sttwo esatwo tdatasets addatasets owt reand mrand ofthe ehthe ttraining httraining iw dedata rapdata mused oanalyzed. c used kroinwthe tin enthe eavailability htSAGNet network SAGNet paper, paper, all all of which of which ha The of the output the network compared with the former two datasets and the training data used inpercent. the SAGNet paper, 5all5of whic It can be seen from tenretnI eht morf snalp 004 daolnwexplicit od explicit ot restructure lwastructure rc eht relationships. serelationships. su rohtua eTo ht ,To tesprepare atad e htthe e radataset, perp othe T the .sauthor pihauthor snouses italuses er the erresults uthe tcucrawler rtis s 68 ticto ilpdownload xtoe download prepare the dataset, crawler 400 400 plans plans from from the 5 results that the relationship explicit structure relationships. To prepare the dataset, the author usesthethe crawler tolayout download 400ofplans from emas ehT .t rap laudividni na sa deand taeand rpick t sipick eup cap s r250 oofmthem o hctoaEdraw llathe una m sfloor nplans alpplans roomanually. fl e ht wardEach oEach t mroom ehtroom foor05space 2 pspace u elements kis ciptreated ainterior normal has been part. up 250 oforthem to .ydraw the floor manually. or isdn treated as design as an an individual individual part. T Door and pick up 250 of Closet them to draw theBed floor plans manually. Each roomestablished. or space is treated as an individual par The domino system consists of three elements: (1) floor slabs, (2) columns, and (3) a stair. The inspiration comes from the Domino System .tused esaused tain d sthe ihtthe tFloor neformer mgutwo a Desk otwo tdatasets dedatasets su eWindow rewere w swere teused satused adtoow t raugment emrof this ehtthis ndataset. i ddataset. esu seuqinhcet techniques techniques in former augment to proposed by Le Corbusier in 1914. This system can represent the essential elements of contemporary reinforced concrete buildings. Compared techniques usedSelection) in the former two datasets were used to augment this dataset. Interior Layout Output (15000 with the classic architecture with complex decorations, this advanced system provides more space possibilities. Figure 5. (a) domino system; (b) rhino breps; and (c) voxels input for SAGNet

Figure 6. Domino system inputs

Figure 6. Domino system inputs Figure 6. Domino system inputs

)a()a(

Self-Adaption Shading Pavilion Seminar Research Project Date: 2019.10 Instrutor: Jessica Zofchka

自适应遮阳亭研讨班研究性项目日期 : 2019.10 导师 : Jessica Zofchka

Abstract

摘要

Based on the multi-objective optimization genetic algorithm, this paper presents an expendable parametric sunshade pavilion design, which can be changed according to the external environment.The purpose of the design is to provide people with a better outdoor experience.There are two central evaluation values :UTCI(Universal Thermal Climate Index) and DGP(Daylighting Glare Probability).In order to harmonize these two indicators with a particular pavilion form, the author designs specific rules to help find the best unfolding form and corresponding control parameters.

基于多目标优化遗传算法，本文提出了一种可展开的参数化遮阳亭设计，可根据外部环境改变形式。设计的目的是为人们提供更好的户外体验。有两个中心评价值 :UTCI( 通用热气候指数 ) 和 DGP( 采光眩光概率 )。为了使这两个指标与某一特定的展馆形式相协调，作者设计了具体的规则，以帮助找到最佳的展开形式和相应的控制参数。

Key Word: Self-Adaption, Multi-Objective Optimization, Genetic AlgorithmParametric, High-Performance, Daylighting, Deployable Structure, Simulation.

关键字 : 自适应 , 多目标优化，遗传算法，参数化 , 高性能 , 日照 , 可展结构 , 模拟

Advanced Fabrication

Seminar Research Project Date:2019.4 Instructor: Masoud Akbarzadeh

Abstract

先锋建造研讨班研究性项目日期 :2019.4 导师 : Masoud Akbarzadeh

摘要

Nature is the best teacher of people. People learn from birds and invented the plane. Architects learning from insects and invent thin-shell structure. Now bionics design is a hot topic. This project aims to design new structure prototype based on natural structure system. Through rigorous tests, we optimize the final form of the structure and apply it on a tiny house wall. The final wall should be aesthetically pleasant and efficient.

大自然是人类最好的老师。人们向鸟学习，于是发明了飞机。建筑师向昆虫学习，发明了薄壳结构。目前仿生学设计是一个热门话题。本项目旨在基于自然结构体系设计新的结构原型。通过严格的测试，我们优化了结构的最终形式，通过机械臂热丝切割只做了该墙体单元的混凝土模型，并尝试将其应用在一座小房子的墙上。最后这堵墙应该是美学上令人愉悦 , 且高效的。

Key words: Bionics, Structure, Hot-wire Cutting, Robotic Arm, Finite Element Analysis

关键字 : 仿生 , 结构 , 热丝切割 , 机械臂 , 有限元分析 80

We choose the bird bone as our inspiration. To lessen the weight and keep the mechanical property at the same time, birds evoloved the almost hollow bones strcuture.

Image credits: A microscopic image of bird bone magnified 25 times. Image Courtesy:Manfred Kage / Science Photo Library ()

Based on the final analysis result, we changed the volume proporation. We increase the proporation of critical part to 25 and lower parts without much pressure

With the prototype, we test the mechnical property in different volume porportions. We have to consider the mechnical property and lessen the prototype weight at the same time.

Our wall peice is a tricky one with a sharp end point at the outside of the whole wall. We do not direct upwards support from the ground

Hot Wire Cutting File Preparation

Volume Proportion:15% Supports: Bottom face Loads: Top Layer Side Surface (5MpA)

Based on the simulation, no surprise, the end point of the wall got most pressure. The edge connected with the other wall also needs more support.

STUDIO OFFICE DESIGN

STUDIO 办公室设计

'INNOCENCE WORLD' CAFE

天真者咖啡馆

Professional Project Site: Xihongmen, Beijing, China

实践项目项目位置 : 中国 , 北京 , 西红门

Professional Project Site: Tongying Center, Beijing, China

实践项目项目位置 : 中国 , 北京 , 通盈中心

This project is a renovation project that aims to renovate a traditional chinese quadrangle courtyard which is lack of maintaince to a architecture studio office for ours team. In order to make the courtyard a better place for work but also keep the precious traditional elements of the courtyard, we decided to use new design skills to represent traditional elements in elevation and interior design. In addition, we design a 'black box' which can be used as conference room and projection screen.

这个项目是一个改造项目，旨在将一个缺乏维护的中国传统四合院改造为我们团队的建筑工作室办公室。为了使庭院成为一个更好的工作场所，同时保留庭院中珍贵的传统元素，我们决定在立面和室内设计中使用新的设计技巧来代表传统元素。此外，我们设计了一个“黑盒子” ，可以用作会议室和投影屏幕。

This project is a small charity cafe design which aims to help autistic children. Autistic children tend to not be willing to talk with other people. However, they are willing to express through simple drawing. The cafe collect autistic children's drawing and send them and donate the money for further treatment of children's autism. People also have the chance to know more about autism. The layer element comes from the requiremtn of store lots of drawings.

这个项目是一个小型的慈善咖啡馆设计，旨在帮助自闭症儿童。自闭症儿童往往不愿意与他人交谈。但是，他们愿意通过简单的绘画来表达。咖啡馆收集自闭症儿童的绘画并寄给他们，并为儿童自闭症的进一步治疗捐款。人们也有机会更多地了解自闭症。层元素来自于存储大量画作的需求。

1.Box

2.Black Box Design

3.Interior Design

1.Box

UI System

5.Projection

4.Bird View

2.Shadow

6.Reversible Facade

5.Section

3.Opening

4.Details

7.Interior Design

8.People Stream

Movable Parts

Putting Paintings Between Layers

Ye Huang University of Pennsylvania Architect/LEED AP BD+C Tel: 15851628800 E-Mail: 392057135@qq.com