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Portfolio of Yuning Wu Yuning Wu 吴雨凝 M.Arch. M.Sc. B.Arch.

| Tsinghua University, China | Politecnico di Torino, Italy | Tsinghua University, China

Addr. BLDG 23, 4-501, Hualu Yuan,Hi-Tech Zone, Dalian, China E-mail. wuyuning14@tsinghua.org.cn Tel. +86 150-1025-1970

凝 / níng / Hanzi, stands for crystalization, sublimation.


CONTENT

STATEMENT OF PURPOSE 01

PART I | AID 02 < AI × Architectural Design = Artificial Intelligent Design > "Retriever" & "ArrNet" ---------------------------------------------------- 02 An Archi-Recognizer 07 "Archi" ---------------------------------------------------- An AI Architect Embryo PART II | BEING AN ARCHITECT 12 < do what machines can't do > Erosion ---------------------------------------------------- 12 An Experiment of Time "Playground": Heritage × Drama ------------------------------------------ 19 Riverside Revival Urban Interface ---------------------------------------------------- 24 Resstore and Reshape Wall, Silk, Light ---------------------------------------------------- 29 Retrospection on the Past RESUME 30


YUNING WU

+ 86 150-1025-1970 wuyuning14@tsinghua.org.cn Architect | AI observer & practitioner | Large-scale data analyst

STATEMENT OF OBJECTIVE

I’m an architect, I'm also an AI observer and practitioner, a skilled data analyst. I do not practice common CAAD, nor parametric design, nor digital fabrication. My endeavour is AID (Artificial Intelligent Design).

information from patterns.Understanding the possibilities and current restrictions of AI, I therefore dissected AID’s development into three stages.

AID, unlike CAAD, is the application of AI technology in the process of design, so that computer can master some degree of independent design capacity. Elementary task of such capacity is to gradually take over repetitive design work previously done by human designers. Realisation of AID requires a deep understanding of design, a pervasive comprehension of latest AI frontier and an effective collaboration of designers and computer scientists.

Logical understanding. Architecture is essentially a spatial presentation. Therefore, research on spatial recognition can be imported, logically decomposing architecture in a way that machines can understand. Once they can “read”, they can be trained. By the end of stage one, machines will not only know what is a house, but also know how to topologically construct a house.

AID's future is not confined to architectural design. Its origin, however, is closely related to an architect's dilemma. I was a junior architect at Tokyo Headquarters of Nikken Sekkei. Just away from school, my mind was filled with ambitions of being a true designer, a pursuer of aesthetics. Conversely, I was given almost nothing to create in my first days. All I have been doing was implementing ideas handed down by senior architects, repetitively adjusting the preliminary floor plan of a residential building. It is not design. This mechanised work can, and should, be done by machines. I may not be the first architect to think this way. I may be the first to actually develop it.

Form finding. Without form, AID can possess no concrete achievement. Training machines’ own capability of creating reasonable forms is the primary task of stage two. Abstracting human experience into machinereadable guidance is challenging work. It must be noted that AID is not common parametric design. Though both can create digital forms, AID has independent “mind” to create on its own, while parametric design carries on programs written by human designers.

Aesthetic cognition. After successful form finding, the ultimate goal of AID is to emulate human being’s cognition of beauty. Almost all existing approaches in AI are focused on rational aspects. The study of AID, a combination of art and science, may shed some light on aesthetics aspects of human intelligence.

My determination to look for a new solution is empowered by the experience of being a mechanical engineering student, a research assistant in Chinese Academy of Sciences (CAS), and a self-educated CS insider by learning EECS courses on MIT Opencourseware. Initially admitted as an ME freshman, I was given a year of solid STEM education, as well as an embodied problem-solving instinct. Ranked No.1 in Department of Energy and Power Engineering, I then changed my major into architecture. B.Arch, M.Arch, M.Sc degree was acquired whereafter in Tsinghua University and Polytechnic Institute of Turin, with distinctive graduation. Later time as research assistant in Chinese Academy of Sciences provided me with a platform to combine my knowledge in architecture, engineering and computer science. It also gave me access to new knowledge in GIS, large-scale data analysis, remote sensing technologies, etc. I've participated in projects such as ecological planning of 2022 Winter Olympic Games, GIS and deep-learning based land assessment system in Shenzhen, etc. WHY AID? “Machines are slaves to architects” is most people’s impression of computer’s role in design. Architects acknowledge computer’s contribution as a convenient tool, but mostly refuse to fathom the possibility that computer can master design on its own, if properly trained. The notion of AID come into being refuting such conventional beliefs. AID is not baseless, nor imaginary. It’s feasible for the following reasons. Firstly, unlike pure art creation, architectural design has rules and objectives to follow. If design is dichotomised into two parts, the rational and the perceptual, the former is essentially a deductive flow of work. Such flow is imitable in some degree. Secondly, the development of AI, such as machine learning and deep learning, has provided applicational solutions to many visual problems in 2D/3D. Embedding these achievements in architecture will bring a new angle. Finally, if each architect’s stylistic way of creation is treated as a “pattern”, and if all patterns are stored in a way that machines can understand, then AID is essentially a problem of “pattern recognition”, namely retrieving and deciphering

In my writing sample and affiliated demo, two elementary projects of the first two stage are presented. “Retriever” is an archi-recognizer converting architectural drawings into topological graphs, a kind of machine-readable presentation. Result of such conversion also established a new dataset called “ArrNet”. “ARCHI” is an AI architect embryo, who can create simple architectural plans by arranging boxes. In general,

• •

with “Retriever”, machines can “read” architecture, with “ARCHI”, machines can “write” architecture.


PART I AID < AI × Architectural Design = Artificial Intelligent Design >

Unlike CAAD, AID is the application of AI technology in the process of design, so that computer can master some degree of independent design capacity. In my writing sample and affiliated demo, two elementary projects of the first two stage are presented. “Retriever” is an archi-recognizer converting architectural drawings into topological graphs, a kind of machine-readable presentation. Result of such conversion also established a new dataset called “ArrNet”. “ARCHI” is an AI architect embryo, who can create simple architectural plans by arranging boxes. In general, • with “Retriever”, machines can “read” architecture, • with “ARCHI”, machines can “write” architecture.


Entirely personal project. Since 2017.1.

Floor Plan

Floor Plan

Floor Plan

#1

PART I AID

In the study of artificial intelligent design , converting products by human architects into machine-readable data is the foundation of later work. Interpretation of different aspects in architectural design requires different kinds of conversion, in other words, different datasets. ArrNet is a topological dataset created for the purpose of abstracting logical relations from existing architectural presentation. Such abstraction is accorded with human architects’ behaviour of drawing bubble graphs in concept design. These graphs are logical supports for later concrete form finding, and are critical for machines to understand the inner philosophy of architectural design. "RETRIEVER" is the name of this project, while ArrNet is the product of it.

RETRIEVER

AN ARCHI-RECOGNIZER Personal Project Since 2016

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© All rights reserved by Yuning Wu


An Archi-Recognizer based on Computer Vision and Machine Learning

An Archi-Recognizer based on Computer Vision and Machine Learning

Since ArrNet is built upon existing architectural presentation, its establishment involves answering the following questions. Firstly, what existing architectural presentation can be used. Secondly, by which mechanism data is retrieved. Thirdly, what form of result is stored in the dataset. Finally, what meaning can these results possess. To answer these questions, a roadmap is designed and presented in Fig. 1. To realise of this plan, proven techniques such as machine learning and object recognition are exploited.

Table 1.1 Tactical Comparison ---------------------------------------------------------------------------+-------------------------------------------------------+---------------build an independent database | + necessary in the long term | + positive | + slow | ---------------------------------------------------------------------------+-------------------------------------------------------+---------------| + plausible | + negative wait for development in CV so that, -- efficiency and accuracy of training improves | + passive | -- required size of dataset reduces | + why not update with CV | ---------------------------------------------------------------------------+-------------------------------------------------------+---------------| + duplicated data | + positive make maximum use of what has already been gathered. | + doubt on adequacy | + practical | + CV ML pre-processing required | ---------------------------------------------------------------------------+-------------------------------------------------------+----------------

Generally, the most useful kind of presentation is floor plan. However, common sources of architectural presentation such as ArchDaily always have various and mixed types of images. Therefore, initially a pre-processing procedure is conducted to filter out floor plans. Converting a floor plan into a bubble graph can be complex matter in computer vision. A mapping from floor plan to point matrix is hence introduced for efficiency and precision. Later recognition of labeled objects with topological or type-hint significance is closely related to the demarcation of rooms and their interdependence. The result is a capsuled topological graph[1] of linked room with the structure of connected components.

0 | Data pool

0 & 1 | Data type & pre-processing

Key to efficient learning is a rich, labeled database. Given current efficacy of CV training, nothing could be achieved without it. Example of ImageNet and ScanNet in 2D/3D recognition well elucidates this point. ImageNet has over 14 million image samples. ScanNet has 2.5 million views in more than 1500 scans, a number still in growth. In order to realize architectural recognition, establishment of such database is necessary but currently missing. According to options listed in table 1.1, exploiting existing data pools such as ArchDaily, DesignBoom, Pinterest is comparatively advised in present context.

A disadvantage of online data pools is their incompletely categorized images. For example, images of different types (i.e. floor plan, elevation, rendering, photo) are sometimes labeled with Arabic numberals in project directory, missing basic type description. Valid images for recognition are technical drawings. Therefore, images such as rendering, diagram, photo should be ruled out first. A step of CV pre-processing is executed, using semi-supervised machine learning. Photos and renderings are relatively easy to make out. Ruling out diagrams and presentation drawings are more complicated, given the diversity and non-repeatability nature of such imagesW.

“RETRIEVER”

“RETRIEVER” 2

© All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


fig.2.1 Disconnected (left) and connected (right) rooms.

fig.2.3 Labeled door samples.

fig.2.2 Labeled door samples.

fig.2.4 Labeled furniture samples and their weight.

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Š All rights reserved by Yuning Wu


fig.3.1 Point matrix M on floor plan of Casa Luis Baragan

fig.3.2 Flare angle

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Š All rights reserved by Yuning Wu


DEMO ------------------------------------------contour recognition special object recognition ------------------------------------------Contour recognition of rooms is demonstrated in the right figure. The shaded area is not exactly the same with the shape of room. This is the advantage of topological study. A logical and statistical result will be enough for this stage of research. Note that not all rooms below are areas. Some are unclosed poly-lines. They are also sufficient in topological representation. Specific object recognition is achieved, as in figure on the left. Doors are marked in orange, whereas specific objects are marked in green, along with their type-hint.

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WEIGHT TABLE ---------------------------------------------------------------------------------------------typehint | weight | rooms connected ---------------------------------------------------------------------------------------------| 5 | bedroom, dining, unknown, toilet, U, U -- living -- toilet | 4 | living, dining, U, U -- U | 4 | garage, bedroom, U, U -- bedroom | 3 | dining, U, U -- dining | 3 | bedroom, U, U -- bedroom | 2 | U, U -- U | 2 | bedroom, U ...

DEMO ------------------------------------------Weighted topological graph ------------------------------------------Firstly, consider results above as two layers of the same image. Then, assume each door adds a “path” between rooms. Also, each path adds a weight to rooms on both sides. In this way, a room is weighted by the number of paths it connects. Finally, store all path, weight, type-hint in a structure of connected components, a topological weighted graph is completed.

typehint/ option 1 | garden option 2 | unknown option 3 | unknown

-- garden | 1 |U -- garage | 1 |U ----------------------------------------------------------------------------------------------

WEIGHTED TOPOLOGICAL GRAPH room 1 type weight

+

door

=

room 3 type weight room 4 type weight

/ garden /1

/ unknown /2

/ unknown /2

room 5 type weight

/ living /5

door

door

door

door

typehint/ option 1 | bedroom option 2 | unknown option 3 | unknown

typehint/ option 1 | dining option 2 | unknown option 3 | unknown

door

door

door

door

typehint/ option 1 | living option 2 | unknown option 3 | unknown

door door

door

door

door door

door

door door door door

door

door

room 9 type weight

/ unknown /1

room 6 type weight

/ bedroom /2

/ dining /3

/ bedroom room 10 type / unknown /3 weight / 2 room 12 type / unknown weight / 2

room 13 room 14 type / unknown type / bedroom weight / 2 weight / 2

door

door

door

door

door

/ toilet /4

room 11 type / unknown weight / 4

room 15 type / garage weight / 1

door

door

door

room 8 type weight

room 7 type weight

room 2 type weight

OLD LABEL ---------------------------------------source / online retrieve content / residential, villa

door typehint/ option 1 | bedroom option 2 | living option 3 | unknown

NEW LABEL (EXPECTED) ---------------------------------------- / machine learning source content / residential

---still in tuning

EXPENCTED RESULT ---------------------------------------RETRIEVER makes a prediction: this is residential. In a residential architecture, living room is the most popular room. Living room is sometimes connected with bedroom. ...

6 © All rights reserved by Yuning Wu

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© All rights reserved by Yuning Wu


Entirely personal project. Since 2015.9.

#2

PART I AID

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Fast design has become a popular phenonemnon in China. It has been the formal way of selecting qualified architects since 1980s. Graduate admission, employment, professional registration all envolve this special kind of skill. It is a known fact that fast design is more of a formulaic, mechanized procedure than an innovative brainstorm.

ARCHI

AN AI ARCHITECT EMBRYO

While fast design is an extreme example of architectural design, it triggers my attention. I consider this worthless duplication as a waste of human architects' time and effort. Can machines create architecture? Can they help us?

Personal Project Since 2015 7

© All rights reserved by Yuning Wu

This idea gives birth to "ARCHI". © All rights reserved by Yuning Wu


Base 1st layer Node ---------------------------------------class Node(object): ...

P1 (1, 1, 2) Prototype #1 ------------------------------------------Regularly ordered insertion Single room insertion Modular point matrix -------------------------------------------

y

Prototype #2 P2 (1, 2, 2) ------------------------------------------Regularly ordered insertion Group room insertion Modular point matrix -------------------------------------------

2nd layer Edge ---------------------------------------class Edge(Node): ...

3rd layer Surface ---------------------------------------class Surface(Edge): ...

4th layer Box ---------------------------------------class Box(Surface): ...

z

x

Prototype #3 P3 (2, 2, 2) ------------------------------------------Hierarchically ordered insertion Group room insertion Modular point matrix ------------------------------------------...

Fig. 0.1 Layer and key feature of data structure

Fig. 0.2 Scenario and prototype version

Code Sample. 0.1 Function build on recursive data structure

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Š All rights reserved by Yuning Wu


An AI Architect Embryo

An AI Architect Embryo

PROTOTYPE #1 | HOW TO PUT BOXES INSIDE A BOX?

PROTOTYPE #2 | CLUSTERING, THE OLD WAY AND THE NEW WAY

Let's put aside cultural, critical, personal propositions for a moment. When getting down to architectural design, what we architects actually do can be summarized into one sentence. Given a specific spatial range, we put rooms inside. The range, or the rooms can all be seen as instances of object Box. Therefore, what we're dealing with actually, is how to put boxes inside a box.

What architects do before placing many single rooms is clustering. It might be dividing rooms into correlated sections, or gathering rooms of similar attributes into a group. Either way, it's a matter of finding sub-order within the general frame.

Preparation ---------------------------------------Box has methods and functions regarding its inner and outer topological attributes.

Preparation ---------------------------------------To be recognized, Box should have labels. They can be function label, popularity label, or anything.

Box.anchor Box.vacancy

Box.label_1 Box.label_2

| a Box's anchor node | a list of taken sub-boxes

| an abstract example | another example

Description of Prototype #2 ---------------------------------------Before insertion, rooms are categorized into clusters by labels. Labels also have orders, reflecting a matter of hierarchy. i.e. In a building complex, when we cluster rooms, label of general feature, such as business or resident is considered first. Then label of functional feature, such as bedrooms, offices.

Description of Prototype #1 ---------------------------------------Randomly insert the rooms one at a time. Make sure there is no overlap or overflow inside the given range.

Pseudocode ---------------------------------------The clustering process can be realized with a recursive sieve of Eratosthenes algorithm cluster(rooms, labelname) is input: list rooms, instances of class Box labelname, the name of label to be refered to output: nested list clustered_rooms note: is_related(label, A, B) is a function returning True, if A and B are related measured by label. -- initially, list clustered_rooms is empty while True, if list unmarked_rooms = rooms - marked_rooms is empty, break the loop list marked_rooms is empty for each room in unmarked_rooms do if is_related(unmarked_rooms[0], room) is True mark room, put it in marked_rooms put list marked_rooms in list clustered_rooms

Pseudocode ---------------------------------------algorithm insert_rooms is range, instance of class Box input: list rooms, instances of class Box output: list distribute, containing position infos of inserted rooms left, instance of class Box, a final look of range after rooms are inserted note: random(room, left) is a function that returns a random, new position of room within left -- initially, left = range for each room in rooms do inserted_room = random(room, left) add inserted_room to distribute update, left = left - inserted_room return distribute, left

return list clustered_rooms

algorithm multilayer_cluster(rooms, labelnames) is a recursive cluster(rooms, labelname) Such algorithm has a time complexity of O(n log log n), largely reduce the time used to cross reference each element.The actual implementation is a segmented sieve, which is more space-efficient. An alternative would be an implementation of Sorenson sieve1, which is slower but even more space-efficient. However, this algorithm is based on an ordered sequence. If this presupposition is abandoned, other more efficient algorithm and data structure such as hash table may be applied. Moreover, if all rooms are properly tagged, samples of training are nicely chosen and tuned, when things are getting complicated, clustering is what unsupervised learning does best.

It's a core, it's easy. Of course, many questions are to be asked. For example, what order should it follow to put the rooms? What should I do if I want an agregation of rooms, but instead, it gives (very likely) a scattered motley? What should I do with inefficiency led by the randomness of the placement? (i.e. A total of 60m3 of 4 rooms inside 70m3 range can easily be done by human. But if by Prototype #1, some room may not fit in.) Random search may seem easy at first glance, but everything comes with a cost. Let's request a simple condition: if boarding deck is close to waiting hall, the case "survives". Otherwise, it "dies". In 100 cases produced by Prototype #1, 47 survived, a survival rate lower than 50%. Actually through many times of experiment, it can be concluded that given a specific condition, the survival rate is generally stable. If condition is evaluated prior to initial production, the survival rate will increase.

1 Jonathan Sorenson, An Introduction to Prime Number Sieves, Computer Sciences Technical Report #909, Department of Computer Sciences University of Wisconsin-Madison, January 2, 1990

“ARCHI”

“ARCHI” 9

© All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


DEMO | PROTOTYPE #1 ------------------------------------------Regularly ordered insertion Single room insertion Modular point matrix -------------------------------------------

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Pseudocode ---------------------------------------algorithm insert_rooms is range, instance of Box input: rooms, instances of Box distribute, position infos of inserted rooms output: left, instance of Box, range after insertion note: random(room, left) is a function that returns a random, new position of room within left -- initially, left = range for each room in rooms do inserted_room = random(room, left) add inserted_room to distribute update, left = left - inserted_room return distribute, left

* This is a scenario based on the Yokohama project mentioned in part two. It's a ferry terminal, major function includes boarding deck, waiting hall, custom, commercial area, office, etc.

10 © All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


DEMO | PROTOTYPE #2 ------------------------------------------Regularly ordered insertion Single room insertion Modular point matrix -------------------------------------------

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* This is a scenario based on the Yokohama project mentioned in part two. It's a ferry terminal, major function includes boarding deck, waiting hall, custom, commercial area, office, etc.

11 © All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


An AI Architect Embryo

An AI Architect Embryo

PROTOTYPE #3 | INTRODUCTION TO HIERARCHY

PROTOTYPE #4 | HIERARCHY FROM "RETRIEVER"

Prototype 2 describes a mechanism that assumes all rooms are already labeled and a hierarchy has been introduced. Where does this hierarchy come from? Normally, architects command machines by input manually. This is a flexible solution, with low error rate and high performance. Below is a retrospection and dissected demonstration of such workflow, starting from receiving an assignment.

Manual input of hierarchy, like hard coding in computer programming, is not flexible, nor beautiful. If assignment changes, hierarchical orders must be input again. Solving this dynamic input dilemma involves the participance of "RETRIEVER" and ArrNet. Remember that ArrNet is a dataset consisting of weighted topo;ogical graphs. If we'd like to design an architecture of type A, we let machine refer to topological graphs in ArrNet with the label of type A. Below is a demonstration of how this is achieved.

<1> Assignment ---------------------------------------T his is an exa mple agai n based on Yokoha ma Passenger Ter m i na l. T h is l ist i nd icates basic function description and area requirement from Party A. Area in this case, is an approximated value. What architects do now is, -- develop a module grid, -- count "squares" by division.

Statistical rank ---------------------------------------Successful topological graph of passenger terminal has not yet been stored in ArrNet. Therefore this example goes back to an easier example, villa design. Calculating all topological graphs with label "residential" returns the following weight table. should the machine start to follow this table and insert rooms one by one? The answer is no.

<2> Grouping ---------------------------------------Architects put related functions into a group based on common sense, personal perception of space, and industry regulations. Generally, there are rules to follow. If machines are to know such rules, there are also two approaches, -- hardcoding many rules, -- training the machine to learn.

Table of Weight (Label Residential) typehint | rank | room connected ---------------------------------------------------------------------------------- living | 1 | bedroom, dining, unknown, toilet, U, U -- toilet | 2 | living, dining, U, U -- bedroom | 3 | cloakroom, U, U -- dining | 4 | kitchen, bedroom, U, U -- U | 5 | garage, bedroom, U, U ... ... ... -- garden | n-1 |U -- garage | n |U ---------------------------------------------------------------------------------

Connected Compunents ---------------------------------------Relation among rooms are stored in a structure of connected components. Let the machine starts with living room (rank No.1 in weight table).As in the graph, component 1 is directly connected to four other components. Therefore, these components are clustered, being inserted as a whole before segmentation. In this example, bedroom, dining and living are clustered. Within this cluster, bedroom is form its own cluster with cloakroom. dinining form another cluster with kitchen. This nested cluster resembles that discussed in Prototype #2,

The latter is what "RETRIEVER" does in project 1. <3> Flows ---------------------------------------There are also r ules to follow when architects decide which groups are to implemented first. Groups are firstly put in chains, or flows. Each flow has particular separated or aggregated attribute. Sometimes, this is called publicity.

Tree No Exception ---------------------------------------Just like architects won't start his villa design from toilets, cerrtain rooms like toilets and stairs have great weights, but relatively little significance in concept design. These rooms raise exceptions, and are inherently moved backwards in design order.

<4> Hierarchy ---------------------------------------Combination and implementation of all steps above is called hierarchy. It is clearly logical. Though having various expressions, if abstracted properly, cores of each expression belong to the same fashion. There are many kinds of results. Some describes residential, some describes passenger terminal, some describes office building, etc. These result change from time to time, from era to era. What stays the same is abstraction itself.

Direct connectedness ---------------------------------------It might be confusing if machine follows paths on the graph one after another. This infinite tracing is meaningless. Though possessing the ability of infinity, Tracing from component 1 to 8 to 2 actually contains two layers of cluster, not two clusters. Therefore, in each layer of clustering, only direct connectedness is counted for.

Therefore, "RETRIEVER" is necessary. However, some hardcoding is still needed. This part of work has not been done yet. Minds of architects can be changed, such variation is triggered by industry codes and regulations. How to combine understanding of such regulations with "RETRIEVER" is still in progress.

“ARCHI”

Complete No

Bipartite No

DAG No

Graph of data structure

First component in a cluster is the core element. Each element in the cluster connects with the core element. But not all elements are connected. 1. Component 1 (C1) 2. Cluster (C1, C0, C2, C3, C5, C6) 3. Nested-Cluster (C1, (C0, C4), (C2, C4), (C3, C7), C5, (C6, C4, C7)) 4. Merge shared elements. (C1, (C4, C0, C2, C6), C5, (C7, C3, C6)) 5. Stop

“ARCHI” 12

© All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


DEMO | PROTOTYPE #4 ----------------------------------------------Hierarchy from Retriever and ArrNet -----------------------------------------------

Test 01 > 4 successful results

Test 02 > 2 successful results

Test 03 > 1 successful result

Test 04 > 3 successful results

Test 05 > 2 successful results

Test 06 > 1 successful result

Test 07 > 3 successful results

Test 08 > 2 successful results

Test 09 > 2 successful results

Test 10 > 3 successful results

Path List ---------------------------------------------------| | 0-1 0-2 0-9 | 1-2 1-3 1-8 | 2-4 2-7 | 3-4 3-8 | 4-5 4-8 | 5-6 5-7 | 6-7 | 7-9 | | ----------------------------------------------------

Adjacency Matrix -------------------------------------------------------------------------------------------------------------------| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | | 0 | 1 1 1 | 1 1 1 1 | 1 | 2 | 1 1 1 1 1 1 | 3 | 1 1 1 | 4 | 1 1 1 1 | 5 | 1 1 1 | 6 | 1 1 | 7 | 1 1 1 1 | 8 | 1 1 1 1 | 9 | 1 1 1 --------------------------------------------------------------------------------------------------------------------

Adjacency List -----------------------------------------------------------------| | | 0 | 1 2 9 | 0 2 3 8 | 1 | 2 | 0 1 4 7 8 9 | 3 | 1 4 8 | 4 | 2 3 5 8 | 5 | 4 6 7 | 6 | 5 7 | 7 | 2 5 6 9 | 8 | 1 2 3 4 | 9 | 0 2 7 ------------------------------------------------------------------

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PART II Being an Architect < do what machines can't do >

If... If repetitive parts in architectural design are replaced by machines, what do human architects do?This question can be further extended as, what is the true value of human design? Itâ&#x20AC;&#x2122;s the sparkling creation, the original proposition, the irreplaceable emotional and rational expression that defines us. Therefore, this is what I do. Do what machines canâ&#x20AC;&#x2122;t do.


Mentor Group Site Time

2015 |

#1

PART II Being an Architect

EROSION

| Prof. Weiguo Xu, Prof. Weixin Huang | Ananda des Vos, Liang Xu | Yokohama | 09/2015

| 2065

Yokohama Passenger Terminal was designed by FOA twenty years ago. This design studio aims at cast a retrospection on it. Our proposal is an experiment about time. Should architecture stay almost the same over fifty, eighty, or even a hundred years? Architecture are said to be the recorder of history, then why not let it seriously record? This is the main idea of EROSION. Let time, nature, human change architecture, architects are not the only designer. Let it erode.

YOKOHAMA 2015 · 2065 2015 Princeton - Tsinghua Joint Design Studio "Yokohama Redux" 10

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© All rights reserved by Yuning Wu


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Š All rights reserved by Yuning Wu


input sample Year 2048, Month November pipe | monthly --wind constant: 37.8 jitter range: 9.6% --rain constant: 67.5 jitter range: 8% --earthquake occurrence probability: 5.2% occurrence: False jitter range: undefined --tsunami occurrence probability: 1.5% occurrence: False jitter range: undefined --other conditional probability: 15% occurrence: False jitter range: undefined

end

4 | Morphosis Environmental input is seen as a "force" morphing the shape. Points matching the matrix are moved, shaping the initial geometry into a milder one.

2055 2050

start geo start data matrix start section line start control points

1 | Weathering prototype Weathering creates marvelous artworks in nature. Study of such prototype includes research on its physical mechanism and figure establishment through extensive collection. 2065

weathering 2045

match input match control points move control points interpolate & modify concatenate

weathering

end section line end floor untwisted grid twisted grid end wall

wind data example

2015

CFD simulation

2035

2020 2025

CFD simulation

3 | CFD Simulation --Input Environmental data package Initial form and shape --Output Spatial environmental data matrix

2 | Gather & analyze Yokohama environmental data Analysis of Yokohama's environmental data in the past fifty years is the foundation of future prediction. The following values are computed first. --AVG Average of wind and rain, later input as constant. --STD Average of wind and rain, later input as jitter range. --P(E) Probability of earthquake. --P(T | E)* Conditional probability of tsunami. *Since tsunami is always a secondary disaster triggered by earthquake, according to Bayes Theorem,

start

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FORM FINDING

1. initial geo

2. initial data matrix

3. initial section line

4. initial division points

5. match data with points

6 move points with vector

7. interpolation

6. eroded section line

9. eroded walls

10. eroded geo

2015

2065

EROSION

蝕 | 2015

蝕 | 2020

蝕 | 2045

蝕 | 2050

蝕 | 2025

蝕 | 2035

蝕 | 2055

蝕 | 2065

100% artificial, 0% natural

81% artificial, 19% natural

91% artificial, 9% natural

40% artificial, 60% natural

59% artificial, 41% natural

22% artificial, 78% natural

31% artificial, 69% natural

9% artificial, 91% natural

TIME RANGE

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2015

2065

EROSION

自然 Shize. 幽玄 Yugen.

Naturalness. Absence of artificiality. Suggestion rather than revelation.

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Edge and corner

2015

2065

Edge and corner

EROSION

1st

1st

Inner courtyard

Inner courtyard

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Mentor Group Site Time

| Prof. Wenyi Zhu, Prof. Jian Liu, Prof. Alan Plattus, Prof. Andrei Harwell | Xiyao Wang, Xudong Sun | Tianjin, China | 09/2015

CRANE

OLD SHIP

RAIL CARRIER

#2

PART II Being an Architect

PLAYGROUND: HERITAGE × DRAMA

Conservation and revitalization of industrial heritage is not a new topic. Inevitably, its solutions fall into patterns: exhibition facility, cultural and memorial parks, ecological restorations, etc. Schemers yell out "new" ideas one after another, each being an idealess mutation to old templates. Before getting down to another well developed yet "familiar" design, I ask myself, is there a truly new way? Tianjin is a city of inductrial heritage, its shipyards are treasures to be exploited. Why not present them in a "fresh" way?

RIVERSIDE REVIVAL

2014 Yale - Tsinghua Joint Design Studio "Riverside Revivial" 6

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© All rights reserved by Yuning Wu


PLOT | "AUDIENCE", "ACTOR", "LOCALE" "Audience" is a metaphor for all kinds of viewers. "Actor" is a metaphor for all kinds of displayers, no matter they are objects or human. Their interactions with locale are visualized in plots below, discussing current examples of museum and theater. AUDIENCE action | random promenade and stop feeling | random fluctuation

-------------------------------------------------< common museum > feeling action ACTOR

| none | stay still

AUDIENCE action | random promenade and stop feeling | random fluctuation

-------------------------------------------------< enhanced museum > feeling action ACTOR

| none | regular pattern

AUDIENCE | mainly stay still action feeling | random fluctuation

-------------------------------------------------< common theater >

feeling action ACTOR

| complicated pattern of fluctuation | complicated pattern of fluctuation

AUDIENCE action | regular pattern feeling | random fluctuation

-------------------------------------------------< enhanced theater > feeling action ACTOR

| random fluctuation | random fluctuation

PROVIDES MAXIMUM POSSIBILITIES OF INTERACTION AUDIENCE action | random fluctuation feeling | random fluctuation

------------------------------------------------- < playground > feeling action ACTOR

| random fluctuation | random fluctuation

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© All rights reserved by Yuning Wu


RHINOCEROS IN LOVE

A Theatrical Demonstration of "Playground"

platform

gadget

stage

-

size

+

platform

gadget

stage

platform

gadget

stage

-

size

+

platform

gadget

stage

platform

gadget

stage

-

size

+

platform

gadget

stage

platform

gadget

stage

-

size

+

platform

gadget

stage

compose | departure

compose | scene & stage set

audience | phase 1

audience | phase 2

actor | phase 1

actor | phase 2

TIANJIN

Tianjin site regional surrounding

context | scale 1

platform

gadget

stage

site | crane and rail

compose | rail & node

audience | phase 3

actor | phase 3

DR

DR

shipyard Hai River urban surrounding

context | scale 2

URBAN CONTEXT

platform

AM DR A AM AM A A

gadget

stage

site | why not drama?

SITE ANALYSIS

compose | addition

COMPOSITION

audience | phase 4

“AUDIENCE“

actor | phase 4

"This is an age of too many things. This is an age of too much emotion. This is an age of too much knowledge. This is an age of too much information. This is an age of intellect and reason. This is an age of sincerity and pragmatism."

“ACTOR“

8 © All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


O CR A M

ISO | SCENE MATRIX

RO AC M HIP O S CR IP 01 I H S -M 11 HIP S 21

E GU W O L VIE E RO R P O00 GE 0 1

RY O O T Y CR IS I R H -M TO 02 RY HI S O T 2 1 HI S 22

S SC 0

S

N CE

E0

02

01

F 00

OU

N

DI N

R OG R P

G

D 10

S 12

ES

SH

IP

IL

H DS

N CE

N CE

2 E1

1 E1 S

0 E1 S

N CE

N CE

E2

2

1 E2

NT

ES S

BU 11 IG N

P

2

S S

N CE

E0

01

0

S

SE RE

E EN

N CE

HI

E PR

IP

IR PA

2

U 21

I-

I 2U

-P

SE RE

NT

RY TO S I H

CRANE ELEVATOR

TURNING MACHINE

AUDIENCE TUBE

CRANE ELEVATOR

CRANE ELEVATOR

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EXPLOSION | FACILITIES Assemble functional facilities on site. Everything is new, everything is old. Stages are mobile, which means "actor" is mobile. Likewise seats are mobile, which means "audience" is mobile. Mobililty and flexibility enables interaction and diversity, which means possibility. It breaks the boundary between display and view.

compose action SOP

| rail -- on-site | turnplate -- on-site | rotation and alignment | "audience"

TURNING MACHINE

CRANE ELEVATOR

compose action SOP

| cargo carrier -- on-site | seats -- new | horizontal / vertical move on rail | "audience"

47 Š All rights reserved by Yuning Wu

compose action SOP

| crane -- on-site | cargo lift -- on-site | vertical displacement on track | "audience"

compose action SOP

| cargo carrier -- on-site | stage prop -- on-site | horizontal displacement on rail | "actor"

AUDIENCE TUBE

MORPHING STAGE

10

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SECTION | ROUTE AND STAGE Like categorized items in museum display room, each stage has its own "scene". There are six major stage sets which can be divided into sub-sets. While travelling with tubes, audience can experience a shift of scenes alongside the shift of locations.

enro st ar

EM

BA

RK

ING

PL A TFO

ute

t

RM

enro

SET

enroute

STA GE

ute

I

enroute

STA GE

SET

II STA GE

SET

enroute

crossing

III

crossing

OP

ER A

HO

USE

crossing

MA

IN

crossing

enroute

OP

HO

D IT

OR

IUM DE

USE

BA

RK

ING

enroute

STA GE

ER A

AU

SET

IV STA GE

SET

MA

V enroute

OP

ER A

IN

AU

D IT

OR

IUM DE

HO

USE

MA

IN

AU

D IT

OR

BA

RK

ING

IUM DE

BA

RK

ING

11 © All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


TO R

IN

O

PO

RT A

NU

OV A

ST AT IO

N

Mentor | Prof. Rong Rao, Prof. Michele Bonino Site | Torino (Turin), Italy Time | 09/2017

#3

PART II Being an Architect

URBAN INTERFACE

Conservation and revitalization of industrial heritage is not a new topic. Inevitably, its solutions fall into patterns: exhibition facility, cultural and memorial parks, ecological restorations, etc. Schemers yell out "new" ideas one after another, each being an idealess mutation to old templates. Before getting down to another well developed yet "familiar" design, I ask myself, is there a truly new way? Tianjin is a city of inductrial heritage, its shipyards are treasures to be exploited. Why not present them in a "fresh" way?

RESTORE AND RESHAPE 2017 Politecnico di Torino - Tsinghua Joint Graduation Research 11

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Š All rights reserved by Yuning Wu


CONCEPT & REASONIING

urban resource | transport | flow

SITE ANALYSIS

MERGE Setting up connection between: 1. Platforms & station’s first floor. 2. Urban spaces cut by station 3. Two restored facades.

URBAN | transport

URBAN | transport

EXTRACT KEY-INFO

C URBAN | resource

A triangle is formed by facade gaps. It is the shared space of connections, thus creating possibilities for space and human activities.

STATION | flow

RESEARCH SCOPE / INTERFACE

A B

naming | documentation | abstraction | index

RESULT The first conncetion is realised by indoor linear space with both recreational and commercial function. The second connection is set up similarly. The third connection is a bicycle track.

RESEARCH SCOPE | Facade Naming A

RESEARCH SCOPE | Facade Naming B

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INTERFACE & FLOW Main passage Bicycle route Touring Staff

B A A

B

C

C

1st floor: +9.30m

Ground floor: ±0.00

34 13 © All rights reserved by Yuning Wu

© All rights reserved by Yuning Wu


slice 09

slice 08

slice 07

slice 06

slice 05

slice 04

slice 03

slice 02

slice 01

FACADE RESTORATION

FACADE RESTORATION

IN ALL

RESTORED FACADE LEGEND & APPROACHES

BICYCLE TRACK

FIELD RESEARCH

Beam

Manual cleaning

Column

Saturated vapor

Joint

Low pressure water spray

Fence

Blast cleaning

Stairs

Chemical product cleaning

Platform

Metal

Protective vanish

Brick

Insecticide resin for consolidation

Plastic

Anti-graffiti coating

Biological attack

Repair external fissures

Crust

Reintegration of detached objects

vandalism

Apply acrylic resin

Efflorescence / crystallization Surface deposit Concretion

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© All rights reserved by Yuning Wu


INTERFACE & INTERSECTION

PASSAGE

DETAIL

CANOPY

GROUND

LITTLE PLAZA

i Tra

no

is nd

y

pl a

“F

t ra d” e l il

ck

s

i Tra

no

is nd

y

pl a

IN ALL

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© All rights reserved by Yuning Wu


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