TU/e: Technische Universiteit Eindhoven

1. 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

2. 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

3. 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450人；3000多名教職員工，300多位教授

4. 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.

5. Planning Design DDSS ICT Artificial Intelligence

6. 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

7. 目前進行的計畫

8. 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

9. 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

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

11. MAS in Collabortive Design Agent-mediated collaborative Design an building process in a Semantic Web context

12. 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.

13. 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.

14. 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

15. Jakob Beetz, Bauke de Vries, Jos van Leeuwen Design Systems group TU/Eindhoven Agent-mediated collaborative Design an building process in a Semantic Web context

16. Traditional Working Methods • Traditional CA(A)D data is • Non-deterministic and ambiguous • Episodic • Highly dynamic • Does not contain machine readable knowledge

17. Central Building Information Model • Central Building Information Model • Founded on central databases • No specification for interaction • Assumes completeness

18. Building Information Model mediated by agent technology

19. A simple MAS scenario

20. Problem aspects: semantic mapping

21. Problem aspects: semantic mapping

22. Problem aspects: semantic mapping

23. Components of a MAS in the Semantic Web context: • Ontologies for buildings, parts, regulations… • Mapping services • Agent communication protocols • Semantic wrappers around Services

24. Conclusions Conclusion: • 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

25. User Simulation of Space Utilisation

26. 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 methodfor the simulation of space utilisation.

27. 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.

28. 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.

29. 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.

30. System overview Output Data 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. • ..

31. Experiment Using RFID to capture the real space utilisation. Merge spaces into zones.Compare the predicted with observed space utilisation.

32. Measuring Housing Preferences Using Virtual Realityand Bayesian Belief Networks

33. 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

34. Measuring User Satisfactionin Virtual Environment Maciej A. Orzechowski Design System and Urban Planning Group @ TU/e Workshop Mass Customisation 26.06.2003

35. General Idea ofMeasuring User’s Preferences The Virtual Environment (VE) is used to present an architectural design to a user. The user is asked to modify that design according to his/her needs and desires. Behind that visual system there is a statistical model to estimate and predict respondent’s preferences based on applied modifications.

36. 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)

37. Change of internal and external layout Direct impact on overall costs Structural Modifications The most important from the point of view of estimation of user’s preferences. Expressed in simple and direct commands: create/resize/divide space; insert openings

38. No influence on costs 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

39. MuseV3 in Desktop CAVE

41. Belief Network • Searching for new, flexible method to access user’s preferences. • 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

42. 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.

43. Step 1 Step 5 Step 15 Step 64 Step 0

44. 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.

45. 4D CAD

46. Construction Analysis during theDesign Process www.ddss.arch.tue.nl Bauke de Vries

47. 4D CAD • Linking building components with construction activities • Manual task of the construction planner • Dedicated systems: NavisWorks, 4D Suite, … • Advantages: Simulation, Visualization

48. Challenge Automation of the planning process. Advantages: • Independency from the planner • Quick first concept plan

49. Implementation

50. 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