Portfolio

PORTFOLIO

Kazuki Hayashi

2

PROFILE

Kazuki Hayashi With full of curiosity, I have studied at the interface of architectural design and structural engineering for more than 8 years. I am a PhD candidate at Kyoto University now, and my current research topic is to develop a novel architectural design process that connects architectural design and structural engineering through machine learning and structural optimization.

I am looking for a structural engineering firm where I can support architectural designers but also positively influence the design and the construction process through structural engineering and computational methods.

E-mail

: hayashi.kazuki.55a@gmail.com

Skype ID : hayashi2229 Website : hayashikazuki.net

Date of Birth

Nationality

Specialty

May 10, 1993

Japanese

architectural design / structural mechanics / structural optimization / computational morphogenesis / machine learning

Language

Programming

Software

Japanese (Native)

Python

Microsoft Office

Revit

Visual Studio

Passed the Fundamentals of Engineering (FE) exam

English

(Fluent)

C#

Adobe CC

Dynamo

Abaqus

Passed the 1st exam of 1st-class Kenchikushi (Japanese

French

(Intermediate)

Fortran

SketchUp

Rhino

SOFiSTiK

architects and building engineers)

MATLAB

AutoCAD

Grasshopper

midas iGen

Licensure / Exam

3

Work Experience 2018.8 - 2018.12 Jun Yanagimuro Structural Design (structural design office) | internship → part-time Support structural design through computational methods 2017.4 - 2018.1 Taiyo Kogyo Corporation (membrane product company) | part-time Debug and create a manual of the company's own analysis software at the research center 2016.9 Arup (design, engineering, architecture and business consultant) | internship Study an overall structural design process through practical training 2015.8 - 2016.3 Design 1st (architectural design office) | internship → part-time Create architectural drawings and 3D models for renovation of Japanese traditional housings 2012.6 - 2014.3 Yotsuya Gakuin (cramming school) | part-time Teach mathematics, physics, chemistry and English to elementary, junior high and high school students

Education 2018.4 - (2021.3) Kyoto University | PhD of Engineering, GPA: 4.0/4.0 PhD thesis: "Reinforcement learning for optimal design of discrete structures" 2019.6 - 2019.9 École polytechnique fédérale de Lausanne (EPFL) | visiting student Apply reinforcement learning for control of adaptive trusses 2016.4 - 2018.3 Kyoto University | Master of Engineering, GPA: 3.8/4.0 Master thesis: "Simultaneous optimization of topology and geometry of trusses using force density as design variables" 2017.1 - 2017.3 Massachusetts Institute of Technology (MIT) | visiting student Develop a structural optimization tool for early-stage design of trusses 2012.6 - 2016.3 Kyoto University | Bachelor of Engineering, GPA: 3.6/4.0 Diploma project: "Hotsure -architecture for embracing social illness-"

Award 2019 International Student Competition in Structural Optimization | 1st prize 2018 Annual Convention of Architectural Institute of Japan | best presentation award for young researchers Structural design competition of Colloquium Analysis and Generation of Structural Shapes and Systems | highest prize Kyoto University | honor prize for master thesis 2017 Annual Convention of Architectural Institute of Japan | best presentation award for young researchers 2016 Japan Sign Design Association | best 100 of SDA award Colloquium Analysis and Generation of Structural Shapes and Systems | best presentation award

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14 24

27 18

2015

2016

2017

2018

2019

10 26

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6

5

19

22

CONTENTS 2020

2021

Moving roof

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č§ŁăƒŹ -architecture for embracing social illness-

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18

Design mapping with auto-encoder .........................................................

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29

Taiken-inn Gwan-gwan

Force density method for simultaneous optimization of trusses Reinforcement learning for topology optimization of trusses Gable roof maker AHP app

Tree column analysis 25

6

Box placement analysis CG / Drawing / Note

6

ARCHITECTURAL DESIGN

Moving roof YEAR

: 2018

LOCATION

: Tokyo, Japan

SITE AREA

: 29000 m2

KEYWORD

173000

25000

How would we behave if we were under a roof that moves up and down corresponding to the people underneath? We might stare at the movement, take photos, sprawl underneath it, chase it, or gather to inflate the structure. Through the interaction with the structure, we might be able to stimu-

actuator steel bar

late our unexploited sensitivity toward spaces.

height increases/decreases through actuators depending on the number of people underneath.

25000

173000

: truss / kinetic architecture / idea competition

Moving roof is a kinetic structure composed of variable geometry truss (VGT), wires to hang ceilings, circular membrane ceilings, and cylindrical steel columns. The spatial distribution of the people is obtained by sensors, and the distribution is used for calculating the height of the structure; the

123000

+z -z

L

L+a

7 1 2 3 4 5

VGT

6 7

φ=200, t=12

8 9 10 11 12 13 14 15 16 17

wires

18 19 20 21 22 23 24 25 26

Ω ← indices of variable control points a ← 6000 d ← 7.5 n ← current number of people observed forall control point i do z̄i ← initial z coordinate end ∆ ← 0.0 forall person p do forall control point i ∈ Ω do zi ← z̄i δ ← r pa√n

compilie

i

zi ← zi + δ ∆ ← ∆+δ end end forall control point i ∈ Ω do zi ← zi − ∆/n end u ← maxi |zi − z̄i | if d < u then forall control point i ∈ Ω do zi ← zi − (zi − z̄i )(u − d)/u end end return zi

Grasshopper component programmed in C# for computing target surface membrane ceilings

cylindrical steel columns φ=1000

Free-form surface is converted to discrete surface

install the component in 5×5 grids

25

00

0 12

0

2500

50

00

00

1250

Axial stresses are analyzed for various possible geometries

8

9

10

ARCHITECTURAL DESIGN

解レ -architecture for embracing social illness-

YEAR

: 2015-2016

LOCATION

: Osaka, Japan

SITE AREA

: 5000 m2

KEYWORD

: diploma design / renovation

Kamagasaki ( 釜 ヶ 崎 )―an area for day laborers beyond relief from social welfare―. People with serious circumstances live there without interacting mutually. Meanwhile, Airin labor welfare center has served an important role as a complex of hospital, apartment and labor welfare center, and consequently as a crossing point for this region. However, this center is going to be dismantled and the functions will be dispersed into other sites by local government.

hospital

apartment

GL+12200 : rooftop

labor welfare center

GL+200 : atrium vocational training center

GL+43000 : lookout point

office GL+200 : piloti

GL+200 : passage

tuted spaces are capable of embracing narrow living spaces, black markets, temporary structures, and supports from humanitarian organizations ―Still, some people might remain hopeless suffering from social illness.

What can I do for them as an architect?

The proposal is to re-vitalize the space by re-constituting the existing structure and three programs.

The design attempts to stimulate the DNA of Kamagasaki, not only by responding to the function-

The proposed design is intended to orient the architecture toward the setting sun. The internal spaces are originally confined by extremely thick RC walls. By providing large openings on the west side

al demands that have changed through 46 years from the first construction, but also by planning "non-refusing spaces" adjusted to the unique scale of life in Kamagasaki. Accordingly, the reconsti-

and access to the higher places, visitors are able to become aware of the setting sun. Here people live for unchangeable tomorrow, finding out a ray of sunlight.

11

East elevation

12

West elevation

A’

B’

A

B

Plan (GL+11000)

13

A-A' section

B-Bâ&#x20AC;&#x2122;section 1/200 B-B' section

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ARCHITECTURAL DESIGN

Taiken-inn YEAR

: 2015-2016

TEAM

: Shuzo Kawakami, Kenichiro Tanaka, and Yushi Yamamoto

LOCATION

: Kyoto, Japan

SITE AREA

: 100 m2

KEYWORD

: renovation / tradition and modernity

Project site before renovation

design team

This is a renovation project of kyo-machiya ( 京町屋 ), a traditional wooden townhouse specific to Kyoto, into an accommodation. The origin of this house goes back to more than 100 years; it was first owned by a thread wholesaler and had been used as both residence and workplace. However, many structural elements have deteriorated to be maintained through the period, and large-scale

wall. All openings are carefully designed so that they orient towards the sky or garden plants, which enables the guests to enjoy open feeling despite the small area. In addition, we planned a small central garden and a common space as focal spaces for interaction among the guests. This way, privacy and publicness are able to coexist without conflict. The finishings include not only conven-

repairs were necessary to utilize the spaces in the long term. At first, we carefully selected which elements to be left in order to preserve the history of this building. After the inspection of the existing structural components, we proposed a design that is intended to revitalize the remaining spaces, in which independent 3 buildings are loosely combined through a consistent narrow path and white

tional materials such as wood panels and roof tiles, but also such experimental materials such as washi, Japanese traditional paper, and plywood in order to harmonize old elegant atmosphere and modern lifestyle. Consequently, the resulting architectural spaces embrace airs of both historicity and modernity.

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(Photos in this page: Kosuke Arakawa)

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(Photo: Kosuke Arakawa)

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Elevation

: extension/rebuilding

Plan

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ARCHITECTURAL DESIGN

Gwan-gwan YEAR

: 2017-2018

TEAM

: Shuzo Kawakami, Daiki Yamaguchi

LOCATION

: Amsterdam, Netherlands

KEYWORD

: form-finding / bridge / design competition

A bridge composed of two free-form surfaces is proposed. This two-dimensionally developed bridge allows pedestrians to take a variety of actions straying from the path such as sitting, eating, shopping, or circulating around art works, instead of just walking linearly.

Form-finding analysis is carried out to obtain a catenary shell which effectively resist against gravitational force by assigning upward distributed forces onto the whole surfaces. In this planning, the shells are also shaped such that they can drag the river water into the square plan and that boats can pass underneath them. This way, hydrophilic space can be achieved where people on the bridge and on the boats dynamically crosses each other. We expect the bridge to become a focal point of art in harmony with vivid water-scapes in Amsterdam.

19 Units to be trained

x1

inputs

・・・

mid. Layer m mid. Layer m’ outputs mid. layer 1 mid. Layer 1’

encoder

・ ・・

・ ・・

xn

・・・

x̂1

xˆn

inputs’

decoder

1 n 2 minimize = F (θ) ( xi − xˆi ) ∑ n i =1 x1

z1 ・ ・・

xn

clustering

・・・

zn ' encoder

RESEARCH

Design mapping with auto-encoder YEAR

: 2018-2019

TEAM

: Takenaka Corporation / Makoto Ohsaki (Professor, Kyoto Unviersity)

KEYWORD

: image analysis / auto-encoder / unsupervised learning

What if an AI classifies interior images without supervision from humans? This project aims to obtain the solution to this question by utilizing an auto-encoder, one of the prevalent unsupervised learning methods, as a generator of 2D design map.

The proposed method starts from training an encoder and a decoder in the form of neural network so as to they output the input image as similar as possible. After this training, only the encoder is utilized for obtaining latent features of the input image. These extracted features are compressed into two-dimensional values through t-distributed stochastic neighbor embedding (t-SNE), and the values become the location of the image on the design map.

Interestingly, the trained encoder successfully captures high-dimensional data such as atmosphere of the room and the location of ceiling, wall, floor and furniture.

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optimization

RESEARCH Force density method for simultaneous optimization of trusses YEAR

: 2016-2018

SUPERVISOR : Makoto Ohsaki (Prof., Kyoto Unviersity), Caitlin Mueller (Assoc. Prof., MIT) KEYWORD

: truss / design exploration / structural optimization

size optimization

topology optimization geometry optimization

There are three major categories in the field of truss optimization. First one is size optimization, where cross-sectional areas of members are changed during optimization. Second category is

These three types of optimization can be simultaneously conducted by setting cross-sectional areas of members and nodal coordinates as design variables; however, it is difficult to solve because it is

topology optimization, where the connectivity of members varies. As well as size optimization, topology optimization is a well established field of research. Above all, ground structure method is widely used; it starts from a highly connected structure called ground structure and eliminate unnecessary members. The last category is geometry optimization, which controls nodal locations to change

necessary to modify the topology by removing coalescent nodes while varying the nodal locations.

We developed a novel efficient tool for simultaneous optimization of topology and geometry of truss structures. Force density method (FDM) is applied to formulate optimization problem to minimize compliance under constraint on total structural volume, and objective and constraint functions are

overall truss geometry. Although numerous mathematical programming approaches have been studies for optimizing truss geometry, it is necessary to set constraints on nodal locations to prevent numerical difficulty due to existence of extremely short members, called "melting nodes" or "coalescent nodes". Therefore, there is little possibility to obtain a sparse optimal topology by simply setting nodal coordinates as design variables.

expressed as explicit functions of force density only. This method does not need constraints on nodal locations to avoid coalescent nodes, and enables to generate optimal solutions with a variety in topology and geometry at a low computational cost. The optimization problem is solved using sensitivity coefficients and the optimizer is compiled as a component compatible with Grasshopper, an algorithmic modeling plug-in for Rhinoceros, which is a popular 3D modeling software.

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loaded point load vector

outputs

supporting condition

Required inputs

node member

nodal feasible region (x) nodal feasible region (y) nodal feasible region (z) design surface random seed

optional inputs

max. member length

force density

cross-sectional area

objective function

max. constraint violation

Grasshopper component that packaged the proposed method

The component working in Grasshopper

pin-support

1

Z 1

X

Y

1

1

1 1 1 1 1

1

The proposed method is also applicable to 3D trusses

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RESEARCH Reinforcement learning for topology optimization of trusses YEAR

: 2018-2021

SUPERVISOR : Makoto Ohsaki (Professor, Kyoto Unviersity) KEYWORD

: machine learning / graph embedding / truss

μ(t) w μ(t) μ(t)

v

v

μ(t)

μ(t+1)

μ(t) Extraction of member features using graph embedding

This paper addresses a combined method of reinforcement learning and graph embedding for binary topology optimization of trusses to minimize total structural volume under stress and displacement constraints. Conventional deep learning methods owe their achievement to a convolutional neural network that is specially designed for extracting the feature values from raster images formed by pixels in a rectangular array. This implies that convolution is difficult to apply to discrete struc-

Although it requires high computational cost for the training, the trained agent is capable of finding sub-optimal solutions for trusses with different geometry, topology, and boundary and loading conditions without re-training. Moreover, it is confirmed that the computational cost of using the trained agent is much lower than using genetic algorithm. The high performance of the proposed method demonstrated that the agent successfully acquired a policy for a structural design problem that

tures due to their irregular connectivity. Instead, a method based on graph embedding is proposed here to extract the features of bar members. This way, all the bar members have a feature vector with the same size representing their neighbor information such as connectivity and force flows from the loaded nodes to the supports. The features are used to implement reinforcement learning

requires complex decision making: reducing the total structural volume while satisfying the stress and displacement constraints. The proposed method for training agent is expected to become a supporting tool to instantly feedback the sub-optimal topology and enhance our design exploration.

to train an action taker called agent so as to eliminate unnecessary bars from Level-1 ground structure, where all neighboring nodes are connected by members.

23 Start

TRAIN

TEST

Input GS, upper-bound values and graph embedding class

Initialize cross-sectional areas

Initialize cross-sectional areas

Randomize the boundary condition

Set the prescribed boundary condition

Compute state s = {v, w}

Compute state s = {v, w}

Compute Q for remaining members

Compute Q for remaining members

Assign a small cross-sectional area to a member chosen as removed by ε-greedy policy

Assign a small cross-sectional area to a member chosen as removed by greedy policy

Observe reward and next state

Observe reward and next state

episode ← 0 TRAIN episode ← episode + 1 true

episode % 10 == 0 false false

TEST

Max episode reached

Update Θ using RMSprop

true output the best parameters Θ

false

Stop

Terminal state true

false

t=7

t = 19

t = 27

t = 31

t = 42

t = 44

t = 51

Terminal state

t = 53

t = 54

V = 22.3 [m3]

disp. violated

true Best total reward

Stop

false

true Save current Θ

Stop

Training workflow 1

1

1

1

[5]

[10]

[15]

[20]

[4]

[9]

[14]

[19]

[3]

[8]

[13]

[18]

[2]

[7]

[12]

[17]

[25] 1

[24] 1

[23] 1

[22] 1

Cumulative rewards in one episode

[1]

[6]

[11]

[16]

Y X

[21]

A truss used as a training material. Nodes 1, 2, 4 and 5 are candidates of pin-supports, and nodes 21-25 are candidates to which two unit loads are applied.

V = 8.8 [m3]

disp. violated

45.0 40.0

35.0 30.0

highest score : 43.5

25.0

t = 35

20.0

t = 63

t = 84

t = 108

t = 138

t = 139

V = 20.5 [m3]

disp. violated

15.0 10.0

0

1000

2000 3000 4000 Number of trained episodes

5000

History of cumulative reward of each test measured every 10 episodes

The trained agent is reusable to other trusses with different geometry, topology and boundary conditions

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COMPUTER PROGRAM

Gable roof maker YEAR

: 2018

LANGUAGE

: C#

KEYWORD

: Rhino plugin / solid processing

For architectural projects, it is sometimes necessary to make numerous volumes around the site in the modeling software. If you feel troublesome in making pitched roofs on extruded solids, this component might be helpful for you.

This program is a customized Rhino command for automation of generating a gable roof from an extruded solid. It is super-easy to use the command; just put "gable" in the command prompt of the Rhino window and select the extrusion that you want to convert. As default, this command automatically identifies the longest edge of the plan of the extrusion and set the side face including that edge as gable side. You can easily rotate the pitch direction by specifying the rotational angle when executing the command.

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System architecture

COMPUTER PROGRAM

AHP app YEAR

: 2018-2019

LANGUAGE

: C#

KEYWORD

: mobile application / analytic hierarchy process (AHP)

We do NOT make decisions intuitively. We make a decision based on the objective, the criteria and sub-criteria, influences, and alternative actions. This useful information is organized in order to rank the decision options. However, it is very difficult to quantify the importance of the criteria and sub-criteria, and accordingly, it is a difficult task to quantify the priorities of the decision options.

Analytic Hierarchy Process (AHP) is a method based on the assumption that our decision process is shaped like a hierarchical structure and might be the solution to this quantification problem. AHP is able to obtain the weight values of the criteria through pairwise comparisons. Although AHP was first proposed in 1970s, this has been extensively studied and applied to actual group decision making. This application is a flexible interface to construct, visualize, and share userâ&#x20AC;&#x2122;s own decision making structure expressed as AHP.

Weighting criteria

Visualization of criteria

Total score of all the scorers

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COMPUTER PROGRAM

Tree column analysis YEAR

: 2018

COPYRIGHT : Jun Yanagimuro, Kazuki Hayashi KEYWORD

: design interface / Rhino / Grasshopper / stress analysis

Tree columns have been used as structural components of a large-scale structures; however, due to complexity of structural analysis and joint, it is difficult to design an irregular shape for them. This difficulty becomes more serious for small-scale structures because of limited available labor cost.

The developed interface connects architects and structural engineers for designing tree columns with an irregular shape. By providing lower and upper bounds for design variables in view of feasibility of construction, the shape can be varied within feasible regions. Moreover, the structural performance can be considered during the study because the member stresses are instantly visualized when the shape is changed. The tree shape determined by the architects maintains the digitalized format, which facilitates the overall design workflow because the data is easily converted to a structural analysis model, 2D drawings, and data for processing of the parts.

(Architect: Shigenori Uoya / Photo: Yohei Sasakura)

The interface is actually used for design and construction of a share house

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COMPUTER PROGRAM

Box placement analysis YEAR

: 2018

COPYRIGHT : Jun Yanagimuro, Kazuki Hayashi KEYWORD

: design interface / Rhino / Grasshopper / strength analysis

"If it was possible to make a building with such a method how something was merely scattered, I thought it was a dreamlike building. And, as for this method, surprisingly precision planning is possible. As opposed to the complicated program called for, moving a box delicately, the plan can be flexibly packed just because it is random." â&#x20AC;&#x2022; Sou Fujimoto (https://www.archdaily.com/8028/children%E2%80%99s-center-for-psychiatric-rehabilitation-sou-fujimoto/)

However, such a design method is difficult to be realized without consideration of structural requirements, and this problem is particularly the case when no expansion joint is installed between the boxes. To tackle this problem, we developed a system for architects to visualize structural performance for current box placement in real time. Once an architect moves the location of boxes, Grasshopper components instantly compute eccentricity and wall strength against horizontal loads of the strucure, and the design feasibility can be immediately confirmed in Rhino window.

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Design proposal for Media Commons in Kyoto University main library (2014)

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Design proposal for Artist-in-residence in Higashi Kujo (2018)

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Study of designing an architecture inspired by Barcelona Pavilion (A1, 2013)

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Study of Hu-Tong House originally planned by Waro Kishi (A1, 2012)

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Extracted pages of my study on reinforcement learning (B5x2, 2018)