Selected Works 2017-2019
Research / Design
Huáng Xīngtài
About me
W ORK EXP ERIENCE Disney Research China, Shanghai Huáng Xīngtài 黄星泰 Email: tedhsingtai@gmail.com Tel: 18217478592 Website: huangxingtai.com
Research intern 2017 July - September
EXT RA C U RRICULAR EXP ERIENCE DigitalFUTURE 2019 Architectural Intelligence Multi-material robotic fabrication: Design and construction 2019 July
S KI LLS Design Architectural Design
EDU C A T ION Tongji University, Shanghai
User Experience Design
Bachelor of Architecture
User Research
2015-now
Photoshop | Illustrator | InDesign | After Effects | Adobe Xd Rhinoceros | Grasshopper | AutoCAD | SketchUp | V-ray
Research Questionnaire Microsoft Office Tableau
Languages
University of Florence, Tuscany, Italy Architectural History Research Workshop 2018 July - September
AW A RDS Tongji International Construction Carnival
Fluent/Native: English | Mandarin | Cantonese
First Prize - Featherly Pavillion
Limited Proficiency: French | Russian
2016 June
Contents
01
Neighborhood Within Yards
02
A Bridge of Steel and Fiber
03
Decode Street and Emotion
Architectural Design
Material Research / Structural Design
Environmental Psychology Research
An exploration into a novel community
The first 3D metal printed and fibrous winding bridge ever built
A quantative research on the corrleation between emotions and street views
Definition Senior Studio Collaborative Work
Keywords Housing Design Shared Space Community Regeneration
Tutors Hu Yanzhe Xu Kai Yao Dong
My Contributions Site Research Masterplan Design 3D Modeling Collage Drawings
Teammates Deng Xinhe Fu Zheyuan Huang Xingtai Jacquelin Chen
Neighborhood Within Yards 2018 Nov. - 2019 Jan.
Research on the spatial structure of the site Activate the residential neighborhood with yards Connect urban space with the neighborhood with
Design Process
Site Research
Design Concept
Optimization
Module Design
Drawings
-Background gatherings -Workflow research -Interview
-Masterplan -Orientation
-Sunshine analysis -Wind analysis -Circulation
-Layout arrangement -Area calculation
-Perspective drawings -Technical Drawings
Alley
Secondary Lane
Greenland
Site Research
Open Pu
Alley
Alley
Secondary Lane
Greenland
Alley
Secondary Lane
Greenland
Alley
Courtyard
Courtyard
Open Public Greenland at Secondary Lanes
Section of Secondary Roads
Courtyard
Courtyard
Alley
Cluster Greenspace
Alley
Alley
Cluster Greenspace
O
Courtyard
Section of Cluster Greening Courtyard
Alley
Cluster Greenspace
The planning and design base is located at the northwest corner of Tongji New Village, Zhangwu
Alley
Courtyard
Open Public Greenland within Cluster
Road, Yangpu District, Shanghai, at the junction of Siping Road and Zhongshan North Second Road, at the inner ring elevated and Siping Road. The city center and sub-centers such as Wujiaochang, Bund, People's Square and Lujiazui can be reached within 30 minutes by public
Alley
Kiosk
Vehicle Lane
Pavement
Alley
transportation. The traffic is convenient. The main service population of the residential area can cover people working in the vicinity of Tongji University, Fudan Science and Technology Park, Jiangjiang District, and the subway line 10.
Section of Entrance
Alley
Kiosk
Vehicle Lane
P
O
Alley
Kiosk
Vehicle Lane
Pavement
Alley
Open Public Greenland at The Entrance
Inherit the spatial textures of the site, creating transitional space on the west with the urban road network.
Defining the main public space and the greening axis inside the community.
Absolving the inner part volume and creating small-scale courtyards that penetrate one another.
Arrange terraces stepping backwards and connect the shared space in youth apartments with hallways.
Design Concept
Small Scales
Courtyards Neighborhood Collage Mixed Community
Sports
Reading
Central Greening
Shared Life Intimacy Linking Communities Foodcourt
Pocket Park
Playground
Masterplan
Entrances to Buildings
Axonometric Drawing
水 二层 会所 平台 休闲 休闲 三层
人才
的 公寓
公共
空间
互相
池及
联通
Greening System
，加 平台 花园 顶
方的 屋 道上 两个 在车 联系 架设 构并 固结
量形 的体 台 不利 动平 采光 层活 去 二 挖 处 成一
社区
中央
口袋
公园
绿地
同济 运动
幼儿
园
公园
Distribution of first floors 小
儿童
饮食
书院
城
市
& 商业 商业 轴线 临街 区主 社
入
口
广场
广 游戏
场
区
入
口
First Floor Plan
Main Elevation
Data Analysis
Main Courtyard
Youth Courtyard
Reading Courtyard
Youth Apartment The settlement opens to Siping Road, and the combination of strip and point-like bottom business and landscape creates spatial penetration between the city and the community, and between the community and the community. The sports park is a large open green space enclosed by talent apartments. It penetrates into the commercial space along the main axis of the street and the residential area. A staggered runway divides the park into different green spaces and sports venues. The academy is at the back of the commercial street. It is a relatively private square courtyard facing the community library. The water surface, the reed landscape, the wooden platform and the tree altar form a central axissymmetric landscape sequence, which embodies the quiet atmosphere of the college.
Traverse Section
Cross Section
Food Court
West Elevation
Elevated Garden
Main Entrance
Cross Section
Playground & Food Court The Children's Game Square is located at the inner entrance of the residential area and is connected to the kindergarten in the new village by a runway extension to provide a playground for the children in the settlement and the new village after school. The pocket park is actually a strip of green space in the house. The sequence of landscape changes makes the transition from a quiet house space to a noisy community entrance and a food plaza.
70m²
70m²
70m²
70m² 35m²
Definition Tongji Elective Studio Collaborative Design DigitalFUTURE 2019 Workshop
Keywords Fiber Winding Parametric Design Robotic Fabrication Structural Optimization
Tutors Philip F. Yuan Li Wenhan (FabUnion) Zhang Liming
My Roles Material Experiment Fiber Syntax Handrail and Step Design Fabrication Files
Teammates Hao Xing Hong Xiaofei Huang Xingtai Yu Zhongsheng
A Bridge of Metal and Fiber 2019 May - July
https://vimeo.com/356635841 Apply structural optimization approach to form finding process Optimize fiber syntax and tool path for robotic fabrication Research on the material characteristics of carbon fiber and glass fiber
Design Process
Form Finding
Handrail & Step Design
Structural Optimization
Robotic Fabrication
Assembling
-Catenary Line Simulation -Topology Optimization
-Frame Design -Material Experiment -Fiber Syntax Arrangement
-FEA Analysis -Structual Checking
-Fiber Winding -Metal 3D Printing
-Pre-assembling -Onsite Assembling
Project Timeline
Fiber Experiment
Carbon fIber is applied in architectural fabrication as a novel material with its extremely high tensile specific strength (30 times the steel of the identical diameter). To explore the structural form of fiber and metal, we experimented on the possible forms and strength performances of the different fiber winding patterns in the preliminary stage of research. Carbon fiber (tensile) and glass fiber (compressive) were winded into a hardened structure for the steps and the handrails.
Form Finding Catenary Line Simlation
Arch Optimization
Preliminary Optimization
Topology Optimization Step 1 Brep
Load1
Step 2 Load2
Support
Brep
Load1
Load2
Support
Multi-Objective Optimization Frame Preliminary Fitting
Resistance to Plane Deformation
Frame Weight Loss - Resistance to Medial Deformation
Printing Angle Optimization
Facade Connection Optimization
Construction Joint Optimization
Calculation Model
位移 1.0 恒载 +1.0 全跨活载 内力 1.0 载 +1.0 跨活载
1.0 恒载 +1.0 全跨活载
位移 1.0 恒载 +1.0 半跨活载 2 内力 1.0 载 +1.0 半跨活载 2
1.0 恒载 +1.0 半跨活载 2
位移 1.0 恒载 +1.0 半跨活载 内力 1.0 载 +1.0 半跨活载 1
1.0 恒载 +1.0 半跨活载 1
Step Frame Design 以拓扑优化的拱曲线为基础，设计类三棱柱式碳纤维踏步，在满足功能需求的前提下尽可能考虑人体舒适性，将踏高限制在 205mm 以内，并使踏面宽度、踏面高度渐进变化，幅度不超过 15%
Step Pattern Design 应力线展开图
加强纤维拟合应力线
踏步缠绕顺序及路径 Weaving Order & Path Design
Handrail Design
ＲＨ＃１
ＬＨ＃１
ＲＨ＃２
ＬＨ＃２
ＲＨ＃３
ＬＨ＃３
ＲＨ＃４
ＬＨ＃４
ＲＨ＃５
ＬＨ＃５
ＲＨ＃６
ＬＨ＃６
ＲＨ＃７
ＬＨ＃７
ＲＨ＃８
ＬＨ＃８
ＲＨ＃９
ＬＨ＃９
ＲＨ＃１０
ＬＨ＃１０
ＲＨ＃１１
ＬＨ＃１１
确定均布荷载方式与支撑条件
整体框架壳体主应力线
均分扶手底边杆件形成缠绕锚点
整体框架分面应力线
加入三角形断面形成折杆杆件
分段框架应力线
扶手与主体结构横剖面图
踏步框架
焊点
扶手框架 ＲＨ＃１２
纤维编织顺序 1
#LH1 整体纤维编织顺序
纤维编织顺序 2
#LH1 整体纤维编织顺序
ＬＨ＃１２
缠绕锚点
3D 金属打印主体结构
ＲＨ＃１３
焊接边梁
ＬＨ＃１３
基座
踏步、扶手框架与边梁间焊接方式
Tool Path Simulation
Resin Bath and Entwine System Design
Dampers
Entwining Entwining Ring Splitting Rings Tension Control
Stretching Rings
Fiber Coil (x6) Balance weight Movable, provide enough tension when stretched.
Resin Impregnation Press Roller A metal cylinder pressing the fibers into resin.
Resin Bath Cooling Bath A metal pool with ice for cooling purpose, prolonging pool life of resin.
Rust-Removing & Assembling
Acknowledgement Structure Consultants: Zhou Qiang, Zhang Xiao Welderss: Qian Shifu, Qi Shifu, Liu Gong
Definition Elective Course Collaborated Research Individual Design
Keywords Urban Environment Assessment Electroencephalography (EEG) Environment Psychology Data Analysis
Tutors Ercument Gorgul Bea Camacho
My Roles Researcher Analyst Subject
Teammates Chen Chaoran Duarte Torre do Valle Huang Xingtai Olga Bialczak
Decode Street and Emotion 2017 Oct. - 2018 Jan.
Evaluate urban environments applying on site eeg data Quantify the visual elements on street Visualize the correlation between subjects' emotion and street views
Research Process
Initial Research
Survey
Research Process
Data Processing
Data Analysis
-Background gatherings -Workflow research -Interview
-Literature Overview -Questionnaires -Key Take-aways
-Ideation -Field Experiment
-Data Flow -Image Data -EEG Data
-Preliminary Findings -Regression Models -Output Coeffiecients
Conclusions
Initial Research Background The discrepancy between urban and lanscape design and the walking experience of the pedestrians has escalated througout the process of urbanization in China. The problems of the urban environment emerge in both human and city scale. One can dive into the myriads of methods to evaluate a built environment, yet few are related to what pedestrians really feel.
"Over the past 30 years, we eventually build a notorious urban environment with despicable inconvenience and dissatifaction..." -Prof. Dihua Li, Landscape Apartment of Peking University
Design Workflow of Urban Planners/Designers
Site Research
Analysis
Survey
?
Subjects can hardly react accordingly unless with real experience.
Layout
3D Modeling
?
Neglecting the nuances existing in the real sites.
Aesthetic Design
Photo Analysis
Interview
Relating little to the real experince of the pedestrians.
Receiving biased answer from the interviewees.
?
?
Validation
Subjective Evaluation
?
Questionnable metrics with long research journey.
Problem Space: The lengthy validation process is often time-consuming and uninstructable.
Survey Literature Review
Questionnaire Survey 1. How do you collect public feedbacks for a built environment?
Single Metric Evaluation Space Syntax (Hillier et al., 1984)1, Spatial Network (Barthelemy et al., 2011)2
Questionnaire Interview On-site research
Providing valuable insights from a macroscopic aspect. Remaining on a birdview observation, only providing hypothetical analysis for preliminary design.
5D Model ( Cervero et al., 2010)3, "Morpho" Model (Victor et al., 2013)4
Taking demographical data and subjective feelings into consideration. Lengthy research process, difficult for designers to learn and evaluate an existing built environment.
Physio/ Psychologial Approach 5
Smartband and GPS Tracker (Li et al., 2016)
Gathering data directly from the walking process of the pedestrians. Data need to be proceeded in labs, not available to designers and decision makers.
EEG & Emotion Relevance Studies Emotiv EPOC and OpenViBE (Yurci, 2014)
2. To what aspect the feedbacks are related do you care the most? Comfortability / Emotions Construction quality Scale
Integrated Evaluation
6
Attaining data directly related to emotions and feelings. Experiment can only be conducted in lab, the EEG detecting devices and tools for data analysis are not portable.
Open days Mind mapping Professional critiques...
Formal aesthetic Air Quality ...
3. How will you analyze the feedback data to reach conclusion? Statistics Description ...
4. What problem do you want to solve when collecting the feedbacks? Subject give dishonest or invalid opinions when surveyed Feedbacks do not provide useful insight to design optimization Difficulty in figuring out the negative factors influencing subjects' emotions Long survey journey ...
Key Take-aways 1. Research on the objective metric related to the subjects' feelings. 2. Identify the key factor causing positive/negative effects on the subjects. 3. Rule out the "improvised" feedbacks and other noise. 4. Attain feedbacks and statistics in a efficient and reliable manner.
Research Process How might we assess the urban environments according to people's feelings objectively? Input data Vision
Sound
Smell
Haptic
1
North Xiangyang Road was selected as research site. We divided it into 19 segments with 20 observatio points evenly, which will be the benchmarks of retrieving data.
Importance
20 pedestrians were randomly selected at the intersection and been informed of the experiment purpose on the date of Dec. 12, 2017.
Relevance Quantifiability Number of Sensor Proceed Time
2
Information
Output Tools EEG (Electrocardiograms)
ECG7 & Respiratory Rate8
GSR10 & Salivary Cortisol9
Facial Expressions11
Stress Level Sensitivity Valence Range
3
Arousal Range
Vision provides more than 80% of the environmental information and is most relevant to the feelings of the pedestrians. It can be quantified by their ratios in a photo, while other sensory inputs are either of low relevance or hard to quantify. ECG and respiratoty rate reflect the stability of emotional status, GSR and salivary test mainly measure the pressure level. EEG and facial expressions are two commonly applied metrics in decoding emotions, however the latter one is less sensitive to emotions of lower arousal. Therefore, visual element is chosen as the input of urban environment, and EEG (brainwave) is chosen as the output metric of people's feelings.
4
The voluntary subjects were asked to walk on a specific street wearing a head mounted EEG signal device before relaxing for 30 seconds with eyes closed to avoid noise and naturalize brainwave data.
They are also required to take video of whatever might interest them while walking on the street silently. The video footage and the EEG data were then retrieved and aligned.
Data Processing Data Flow
Visual Elements
INPUT DATA Video Footage
RECORDING TOOLS Bayesian SegNet14
EEG TGAM Module
EEG ID App
RAW DATA Segmented Picture
Bayesian SegNet
Brainwave Signal
RAW EEG Data
Building
Vehicle
Road
Pedestrian
Pavement
Bike
Tree
Pole
The photos were retrieved from the video according to the observation points, and were processed into a colorcategorized picture using a machine learning tool Bayesian Segnet, developed by Cambridge University to identify different elements, whose ratios were then calculated by Color Summarizer.
Brainwave Signals FILTERING TOOLS Color Summarizer15
OpenVibe
Ratios of Visual Elements
Filtered EEG signals
OUTPUT DATA
DATA ANALYSIS Tableau
Regression Models
Alpha band and Meditation signal: Meditation measures how calm and clear-minded you are at the moment. It indicates the level of mental "calmness" or "relaxation" with algorithm value from 0-100 outputting every second. The more your mind relax, the higher the algorithm output value.
Beta band and Attention signal: Attention measures how focused or single-minded you are at the moment. It indicates the intensity of mental "focus" with algorithm value from 0-100 outputting every second. The more you focus, the higher the algorithm output value.
Image Processing
Data Analysis Preliminary Findings
Ratio of Main Elements on 20 Observation Points Ratio / %
Observation Point
Average Attention and Meditation EEG Signal of 20 Pedestrians Band Power /dB
Attention Meditation
time/s
The EEG signals and the ratios of elements were aligned in Tableau, a data visualization softerware to compute the a heatmap of coefficients above, which describes the degree to which these two groups of data are correlated. In the bottom two rows, the coefficient between tree element and meditation signal is rather high which in accordance with the common belief that natural scenes make people more relaxed. On the attention signal, road has a high positive effect and pavement has a more negative effect compared with other elements. The heatmap above demonstrates some preliminary insights based on the data. However, most correlations between the brainwave signals and the elements are subtle, indicating that a further study on the correlation between these data is required.
Data Analysis Regression Models16 EEG Data
Different Regression Models for Meditation Signal Data: Visual Elements Data
Linear Model
Ridge Model
Lasso Model
Dataset
Training Data
Testing Data
Linear Regression
Ridge Regression
Lasso Regression
RMSE Evaluation
RMSE Evaluation
RMSE Evaluation
RMSE Evaluation Matrix
Attention Signal Optimal Model A more useful and precise mathematical model is required for more penetrating insights. To present the relationship between the two sets of data and for further prediction, several linear regression models were tested to select an optimal one through a machine learning process. The workflow is shown as above.
Training set Testing set Mediation Signal Training set Testing set
Linear Regression Ridge Regression Lasso Regression 6.64 6.61 6.65 2.98 3.26 2.29 5.69 5.66 5.53 2.38 2.59 1.94
RMSE (root-mean-square error) is a frequently used measure of accuracy, to compare forecasting errors of different models for a particular dataset. In general, a lower RMSE is better than a higher one.17 In the matrix above, among all the testing sets of Attention signal and Meditation Signal, RMSEs of Lasso Regression are the lowest. Therefore, Lasso Regression model was selected as the optimal one in current research, and computed graphs of coeffiecients between visual elements and emotion signals in the following page.
Conclusion & Future work According to the results of the regression models,
Coefficients from Lasso Regression Model Attention
Meditation
Relative Attention
Relative Meditation
1. Although the correlation between street views and pedestrians’ emotion might vary due to the individual differences, specific street elements exert different subtle influence on people’s emotions.
Coefficient Value
2. Bike, road and pedestrian have positive effect while pavement has negative effect on subjects’ attention signal intensity. Building, vehicle and tree have no apparent correlation with the attention signal intensity. 3. Tree has rather strong positive effect while pedestrian, pavement and road have negative effect on on subjects’ meditation signal intensity. Building, vehicle and bike have no apparent correlation with the meditation signal intensity. Based on the regression model, a plausible explanation for the result is that bikes and passers-by provoke the attention or alertness of the subjects. Pavement reduces the attention intensity as it arouses the sense of safety of the pedestrians.
Bike
Building
Pavement
Pedestrian
Road
Tree
Vehicle
For the future work, since we also take the dynamic visual factors on the street into consideration, we would like to add more elements into our variables such as sound and smell, which could make the result more comprehensive and adaptive.
References 1. Hillier B, Hanson J, 1986: The Social Logic of Space 2. Marc Barthelemy et al., 2011: Spatial network, Theory and applications 3. Robert Cervero, Reid Ewing, 2010: Travel and the built environment: A meta-analysis 4. Oliveira Victor, 2013: Morpho: a methodology for assessing urban form 5. Xin Li et al., 2016: Assessing essential qualities of urban space with emotional and visual data based on GIS technique 6. Erim Yurci, 2014: Emotion detection from EEG signals: Correlating cerebral cortex activity with music evoked emotion 7. Joong Woo Ahn et al., 2019: A Novel Wearable EEG and ECG Recording System for Stress Assessment 8. Martin Čertický et al., 2019: Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment 9. Masahiro Matsunaga et al., 2017: Association between salivary serotonin and the social sharing of happiness 10. Martin Čertický et al., 2019: Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment 11. Marian Stewart Bartlett et al.: Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction 12. According to https://www.britannica.com/science/human-sensory-reception 13. According to Vera Shuman et al., 2015: The GRID meets the Wheel: Assessing emotional feeling via self-report 14. Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla: "Bayesian SegNet": Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding 15. Martin Krzywinski: http://mkweb.bcgsc.ca/color-summarizer/?home 16. The machine learning process was adopted according to the Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011. 17. https://en.wikipedia.org/wiki/Root-mean-square_deviation
Design is a data-driven process
Huáng Xīngtài