TU/e: Technische Universiteit Eindhoven

智慧結構、材料與空間生活 IN 愛因霍分科技大學

OUTLINE

• 愛因霍芬科技大學簡介• DDSS Research programme

– MAS in Collabortive Design– Human behaviour simulation– Measuring Housing Preferences Using Virtual

Reality and Bayesian Belief Networks– 4D CAD

愛因霍芬科技大學簡介• 成立於 1956 年 ( 今年 50 歲了 )

• 荷蘭的三所工科大學之一 ( 荷蘭大學都是國立的 ) • 1998 年被德國評定為歐洲最好的工科大學 • 世界排名 301~400 大學 ( 根據上海交大排名 ) 等同於清大• 八個院系（建築系、電子工程、化學及化學工程、工業工程及管理科學、

應用物理、機械工程、哲學及社會科學、數學及電腦科學） • 大學部約 6000 人，碩士生約 200 人，博士生約 4

50 人； 3000 多名教職員工， 300 多位教授

DDSS Research programme

• In Eindhoven University of Technology, DDSS is the name for several of our activities.– First of all, Design & Decision Support Systems is the

name of our Research Programme. – DDSS also stands for the

International Research School, in which we collaborate with a number of similar groups in European universities.

– Then, DDSS is also the name of our Master of Science Programme that is related to our DDSS Research Programme.

DDSSDDSS

Design Planning

Artificial Intelligence ICT

DDSS Research programme

• 主持人 :Prof. Dr.ir. B. de Vries• MS & PhD at the Department of

Architecture and Building at the Eindhoven University of Technology

• 研究人員多達 12 人• 學程包含：

– MSc Courses– MSc Projects– Graduation Projects– EU and PhD projects

目前進行的計畫

Graduation Projects

• Space utilization simulation of office buildings( 空間利用模擬 ) • Generative Design • Generation of a construction planning using a 3D CAD model(3D 建

模時程規劃 )• Digitally managing the quality of (architectural and urban) designs• Electronic Document Management in production processes • Search systems for building product information( 建築材料收尋系

統 )• Digitally checking location plans• Interactive modular house design( 共同設計 )• Generating a long-term maintenance planning from product model

data

EU and PhD projects

• Building Management Simulation Centre• Decision Support System for Building

Refurbishment• Measuring User Satisfaction through Virtual

Environments• Using a Virtual Environment for Understanding

Real-World Travel Behavior• Co-located Decision Support Space• Simulation of Human Behavior in the Built

Environment• MAS for the support of Collaborative Design

Design Systems Lab. 設備• Desk-Cave• CAD software• VR hard/software• Simulation software• User interfaces

MAS in Collabortive Design

Agent-mediated collaborative Design an building process in a Semantic Web context

MAS in Collabortive Design

• 使用單一套建築輔助軟體，來協助設計師來滿足顧客多樣客製化需求，現已顯得捉襟見肘

• A system will be developed that assists the designer in an effortless manner to get information related to the current design task and to automatically offer solution to design problems.

MAS in Collabortive Design

• The aim of this research is to analyze the potential of different techniques of Multi Agent Systems for the use in the domains of architectural design and the building process as a whole.

Local machine /Intranet /Internet

Agent

Human Expert

Agent

Agent

Agent

Agent

Human Expert

Agent

Agent

Agent

MAS in Collabortive Design• Among the most important steps in this project are:

– Gather information and build a knowledge base with minimal additional workload for the user

– Identify problem and context based on the current actions of the user

– Identify related knowledge domains and previous use cases, the agents representing them and the corresponding communication protocols including their ontologies

– Gather strategies, opinions and solutions and adapt them to the problem and hand.

– Generate suggestions and their representations and offer them in a convenient, non-distracting way

– Offer approaches to user and incorporate reaction into knowledgebase

do

ma

in s

pe

cific

ag

en

t(s)

User

Genericdomain specific

application

Listen and record

KB

query

Suggest

HC

I

Add / retrive

Agent society onlocal machine /

Intranet /Internet

Jakob Beetz, Bauke de Vries, Jos van LeeuwenDesign Systems group TU/Eindhoven

Agent-mediated collaborative Design an building process in a Semantic Web context

Traditional Working Methods

• Traditional CA(A)D data is– Non-deterministic and

ambiguous– Episodic– Highly dynamic– Does not contain

machine readable knowledge

**

*

*

*

*

*

*

Central Building Information Model

• Central Building Information Model– Founded on central

databases– No specification for

interaction– Assumes

completeness

Building Information Model mediated by agent technology

Local machine / Intranet /Internet

Agent MarketplaceActor Agent

Actor Agent

Wrapper Agent

Resource Agent

KB

A simple MAS scenario

I would like to change the size of this roomWill your HVAC unit still fit in?

PDB

Yes but +10dB

Same Specs but max size 2x3x4m ?

Yes but it’s +10 dBRegulations Reasoner

No

Sound insulationsatisfactory?

Problem aspects: semantic mapping

I would like to change the size of this roomWill your HVAC unit still fit in?

PDB

Yes but +10dB

Same Specs but max size 2x3x4m ?

Yes but it’s +10 dBRegulations

DB

No

Sound insulationsatisfactory? Ok, we leave it unchanged

Yellow pages

UserAgentUserAgent

A

B

C

D

PDB SemWeb Service

SemWeb Service

PDB SemWeb Service

SemWeb Service

PDB SemWeb Service

SemWeb Service

PDB SemWeb Service

SemWeb Service

Mapping and reasoning service

Mapping and reasoning service

Cooling Unit Product Y

200 cm

422 cm

24.000 BTUs

Width

Height

Capacity

…

...

Cooling Unit Product X

3 m

2.5 m

2 Tons

Width

Height

Capacity

…

...

SI Unit conversion Rule

1 Energy to melt one ton of ice = 12,000 British Thermal Units per Hour (BTUH)

Problem aspects: semantic mapping

Mapping and reasoning service

Cooling Unit Product Y

200 cm

422 cm

24.000 BTUs

Width

Height

Capacity

…

...

Cooling Unit Product X

3 m

2.5 m

2 Tons

Width

Height

Capacity

…

...

SI Unit conversion Rule

1 Energy to melt one ton of ice = 12,000 British Thermal Units per Hour (BTUH)

Problem aspects: semantic mapping

I would like to change the size of this roomWill your HVAC unit still fit in?

PDB

Yes but +10dB

Same Specs but max size 2x3x4m ?

Yes but it’s +10 dBRegulations

DB

No

Sound insulationsatisfactory? Ok, we leave it unchanged

Components of a MAS in the Semantic Web context:

• Ontologies for buildings, parts, regulations…

• Mapping services• Agent communication

protocols• Semantic wrappers

around Services

ConclusionsConclusion:• MAS can take care of some of tiresome

communication overhead in distributed collaboration environments

• MAS in a semantic web environment can help to discover and process project-relevant information (even at design time)

• Semantic web technologies can help in a clean separation of Data and business logic

User Simulation of Space Utilisation

User Simulation of Space Utilisation

• Up to now no methods for performance evaluation are available which involve the occupants of the building.

• The aim of the project is to a develop a method for the simulation of space utilisation.

Human behaviour simulation

• Building performance analysis is a well-established tradition in the context of structural engineering and building physics.

• No model for building

simulation involving

the actual users.

User Simulation of Space Utilisation

• Simulated activity schedule versus observed activity pattern.

• This project integrates two methods, namely Colored Petri Nets and Activity Based Modelling.

System overview

Input The workflow of the organisation. The design of the building in which the

organisation is (or will be) housed: the spatial conditions.

Organisation

Building design

Space utilisation

U ser S imulation of S pace U tilisation

System overview

OutputData about the activities of the members of the organisation

and their location in the building space.

From this performance indicators can be deduced, like: Average/maximum walking distance/time per individual. Number of persons per space in time. Evacuation time/distance. Usage of facilities. ..

Experiment

Using RFID to capture the real space utilisation.Merge spaces into zones.

Compare the predicted with observed space utilisation.

Ontruimingsplan

Hands-freetoegangscontrole

Automatisch tijdregistratie(per zone/afdeling)

AutomatischeRoute analyse

Objectbeveiliging Asset management

Werkplekbeveiliging

Ontruimingsplan

Hands-freetoegangscontrole

Automatisch tijdregistratie(per zone/afdeling)

AutomatischeRoute analyse

Objectbeveiliging Asset management

Werkplekbeveiliging

Measuring Housing Preferences Using Virtual Reality

and Bayesian Belief Networks

Measuring Housing Preferences Using Virtual Realityand Bayesian Belief Networks

• This research aims to provide better insight in the housing preferences of (future) inhabitants. The project is guided by three research goals:– Develop a method (Bayesian Belief Network) to elicit

preferences based on individually designed houses.– Comparison with conjoint analysis (CA) of validity and

reliability.– Make a design support tool for non-designers to

create a design.

Utility Convergence

Measuring User Satisfactionin Virtual Environment

Maciej A. OrzechowskiDesign System and Urban Planning Group

@ TU/e

Workshop Mass Customisation 26.06.2003

The user is asked to modify that design according to his/her needs and desires.

General Idea ofMeasuring User’s Preferences

The Virtual Environment (VE) is used to present an architectural design to a user.

Behind that visual system there is a statistical model to estimate and predict respondent’s preferences based on applied modifications.

MuseV – VR System

MuseV3 – a virtual reality (VR) application with functionality of a simple CAD system for non-designers.

Two categories of modifications:• Structural modifications (change of layout)• Textural modifications (change of visual impression)

Structural Modifications

The most important from the point of view of estimation of user’s preferences.

Change of internal and external layout

Direct impact on overall costs

Expressed in simple and direct commands: create/resize/divide space; insert openings

Textural Modifications

Secondary modifications (visual impact), mainly used to check proportions, dimensions (inserting furniture) and to decorate (applying finishes).

Not included in the preference model

No influence on costs

MuseV3 in Desktop CAVE

Belief Network

Searching for new, flexible method to access user’s preferences.

Criteria:Criteria:• Interaction with the model during the time of preferences estimation

• Possibility to find weak points (where the knowledge about preferences is the worst)

• Improve data collection by direct feedback

• Incremental learning

Short explanation of BN

What it is?• Belief network (BN) also known as a Bayesian network or probabilistic causal network• BN captures believed relations (which may be uncertain, stochastic, or imprecise) between a set of variables which are relevant to some problem (e.g. coefficients and choices).

How does it work?After the belief network is constructed, it may be applied to a particular case. For each variable you know the value of, you enter that value into its node as a finding (also known as “evidence”). Then Netica does probabilistic inference to find beliefs for all the other variables.

Incremental learning.After the beliefs are found (post priori) MuseV updates the network, so they become a’ priori for the next respondent.

Step 0 Step 1 Step 5 Step 15 Step 64

BN - Model

In our proposal the network (model) is learning while a user is modifying a design!

To improve the quality of collected data and the knowledge about design attributes, the system, (based on beliefs), can post a question to user.

4D CAD

Construction Analysis during the

Design Process

www.ddss.arch.tue.nl

Bauke de Vries

www.ddss.arch.tue.nl

Bauke de Vries

4D CAD

• Linking building components with construction activities

• Manual task of the construction planner• Dedicated systems: NavisWorks, 4D Suite, …• Advantages: Simulation, Visualization

Challenge

Automation of the planning process.

Advantages:

• Independency from the planner

• Quick first concept plan

Implementation

EquipmentLabour

Constr.Analysis

Comp.Rel. +Dur.

CADmodel

Formulas

CADProject

PlanningPlanningSchema

Design evaluation

Construction algoritms

Analysis by object name:Walls are bearing floors, colums are bearing beams, etc.

Analysis by object elevation:Object with a lower elevation is bearing an object with a higher elevation

Construction algorithms

Analysis by directed graph:

Each object is a node in a connection graph.

Construction algorithms

Analysis by object adjacency:

Each object is a node in a topological graph

A

C

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FD

JE

H K

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- Objecten zonder voorganger

Planning comparison

Real planning

Generatedplanning

Complete process