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«KNOWLEDGE-BASED MODEL FOR INTEGRATED TALL BUILDING DESIGN FACTORS BY AJLA ZISKO B.S., University of Illinois at Urbana-Champaign, 2003 M. Arch., ...»

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KNOWLEDGE-BASED MODEL FOR INTEGRATED TALL BUILDING DESIGN

FACTORS

BY

AJLA ZISKO

B.S., University of Illinois at Urbana-Champaign, 2003

M. Arch., University of Illinois at Urbana-Champaign, 2005

DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Architecture

in the Graduate College of the

University of Illinois at Urbana-Champaign, 2008 Urbana, Illinois

Doctoral Committee:

Professor Mir M. Ali, Chair Associate Professor Abbas Aminmasour Assistant Professor Ilinca Stanciulescu Dr. Francois Grobler, US Army Corps of Engineers UMI Number: 3337991

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ProQuest LLC 789 E. Eisenhower Parkway PO Box 1346 Ann Arbor, Ml 48106-1346 Abstract Factors influencing the design process can take many different forms, such as conceptual, physical, economic, environmental, social, and cultural. This dissertation focuses on development of an ontology to capture integrated design factors for tall buildings. It describes concepts, their attributes and relations in which objects link to one another. The factors, relations and characteristics in the design process can be explicitly defined and presented. This dissertation captures the design process of tall buildings, design factors and involved agents. Particular focus is given to environmental effects, physical systems, economic factors, socio-cultural aspects, as well as emerging technologies in design and sustainability. Integrated design as an approach is investigated, as well as the role of Building Information Modeling (BIM). BIM typically captures information about a building, such as elements, and the developed ontology is aimed at extending BIM to include contextual information. Examples of BIM implementation in architectural practice are presented. Ontology is utilized as a knowledge-based model in a web-based educational application aimed to represent integrated design factors, to assist in search and information processing during schematic design, and foster collaboration. Results show that future generations of computational tools for architectural design should foster integrated design and advanced information modeling techniques, actively assisting users from the earliest stages of project. The developed knowledge-based model contains computational representations that can be utilized in such system.

To Nur, the light of my life.

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I would like to acknowledge my advisor, Prof. Mir Ali, for his direction and mentorship during the course of my doctoral studies. I would also like to acknowledge my committee members, Prof. Abbas Aminmansour, Dr. Francois Grobler and Prof.

Ilinca Stanciulescu for their guidance.

Many thanks to Mr. Raul Pacheco from Skidmore, Owings and Merrill (SOM) for the discussions on BIM use and information technology implementation at SOM. Also, I would like to thank Mr. Amir Al-Abosy and Mr. Kiril Mirintchev from Loebl Schlossman & Hackl for their willingness to discuss projects and design processes.

Special thanks to Adil Dhanani (UIUC ECE Department) and Mike Malmsbury (UIUC CS Department) for collaboration during the development of ONTOarch application. Also, all the participants during ONTOarch application evaluations are recognized for their valuable input.

–  –  –

Figure 4.3: Integration web for sustainable tall building design, part A 102 Figure 4.

4: Integration web for sustainable tall building design, part B 103

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Figure 5.8: Overall ontology and structure of partial design factors classes 151 Figure 5.

9: Existential restrictions for a class "Vertical_transportation" and

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Figure 5.11: Classes defining DesignFactors, and ontological structure 155 Figure 5.

12: Conceptual representation of relationships between

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Figure 5.13: Properties and relationships associated with class "Tall_building".

162 Figure 5.14: Specific ontology query searching for distinct stylistic

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Figure 5.21: Extension of graphical search for design factor "Activity" 169 Figure 5.

22: Relationships between design factor "Activity" and immediate

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Figure 5.23: Larger context of design factor "Activity" in the ontology 170 Figure 5.





24: General knowledge management process in architecture 174 Figure 6.1: Correlation between decision effectiveness and implementation

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Figure 6.6: Survey responses for satisfaction with application layout 204 Figure 6.

7: Survey responses for satisfaction with application structure and

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Figure 6.8: Survey responses for satisfaction with "Site Analysis" 206 Figure 6.

9: Survey responses for satisfaction with "Energy Analysis" 206 Figure 6.10: Survey responses for satisfaction with "Material Selection" 207

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Table 6.3: List of buildings contained in ontology as instance data 195 Table 6.

4: Usability issues discovered during initial user evaluation 197 Table 6.5: Usability issues discovered during initial user evaluation 198

–  –  –

Introduction Architectural design is a complex process involving numerous factors.

Historically, designers have been researching and developing methods to aid the design process, and to represent building as a three-dimensional object occupying space by different means—words, models, drawings, perspectives, specifications, etc. Greek architects described their designs with words, and architectural models were used as early as 725 BC at Perochora; however, architectural drawings are not mentioned in Greek historical or literary sources (Hewitt, 1984). Medieval master builders used plan drawings, typically laid out geometrically. During the Renaissance period, the discovery of linear perspective by Brunelleschi in 1425 led to new representational forms of architectural design—spatial organization could be revealed in three dimensions. During the 18th and 19th centuries, Ecole des Beaux Art employed the Dessin Geometrale approach, where buildings were shown as geometrical objects in a Cartesian system using plans, section and elevations. This paradigm is still prevalent in present construction documentation.

The 20th century brought technological advancements that allowed for new tools and representation schema, such as CAD systems, three-dimensional modeling, and building performance simulations. The quest for interoperability resulted in openstandard data models for building designs and more recently in Building Information Modeling (BIM). In the leading architectural practice buildings are designed as threedimensional objects with certain properties and attributes, and the information about them is exchanged and shared. For example, IFC (Industry Foundation Classes) are objectoriented data models representing building entities and their relationships. Certain building design computer programs, such as ArchiCAD and Revit, are based on objectoriented design and BIM. Traditional CAD programs presented data based on geometric entities, capturing two-dimensional spatial relationships, but not the entity-specific information. BIMs provide common database of information about a building, including geometry. The novelty is that features and elements are represented in several different views for specific purposes; geometry does not represent the element with data added to geometric objects, rather elements contain data, and geometric representation is only one among several purposes.

The advances in telecommunications and computer technology are affecting all aspects of building industry, from design to management of buildings. Architects and engineers are overwhelmed with the amount of information that guides contemporary design, from physical, structural, mechanical systems to material selection and sustainable design. In that sense, information modeling becomes crucial part since it constructs machine-readable symbols that capture the meaning of information and organize it, so that the models become core technology for information system

engineering. Mylopoulos (1998) states:

Information modeling is concerned with the construction of computer-based symbol structures which model some part of the real world... it should be thought as a repository that contains accumulated, disseminated, structured information, much like human longterm memory, or databases, knowledge bases, etc. Assuming that information is entered through statements expressed in some language, the above considerations suggest that the contents of these statements need to be extracted and organized according to their subject

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Information models consist of a collection of symbols whose instances describe abstraction of a specific domain, operations that can be applied to the symbols, and collection of rules that define relationships between different symbols. Over the years, three different categories of information modeling have been proposed and used, reflecting historical technological advance: physical, logical and conceptual (Mylopoulos, 1998).

Physical information models expressed conventional data structures in terms of records., strings, arrays, lists, and variable names. The drawback of these models is that in terms of programming they force two sets of conflicting concerns—computational efficiency and the quality of application model. Logical information models were first developed in the 1970s, consisting of

Abstract

mathematical symbols, such as sets, arrays and relations. Conceptual information models offered more expressive methods for modeling applications and structuring information bases. These models rely on abstraction mechanisms, often inspired by cognitive science and artificial intelligence.

The focus of this research is the development of conceptual knowledge-based model for structuring information about tall building design.

1.1 Research Statement The goal of this dissertation research is to develop machine-interpretable ontology representing building design factors. The scope of the research is focused on integrated tall building design, and the model includes physical, environmental, technological, and socio-cultural factors. Physical factors include building size, program, budget, structural system; environmental factors include site and its properties, such as orientation, climate, density; social aspects include the building function, users and their activities; and cultural aspects are location-specific traditions and customs. All of these characteristics influence tall building design, and the consideration of these factors is necessary in the design process.

Tall building design is the most complex form of design due to the scale and complexity of these projects, and often involves many different professionals with specific knowledge and expertise, such as architects, structural engineers, mechanical engineers, planners, developers, economists, etc. Compared to low-rise buildings, tall building imposes more pragmatic issues, such as the structure, location and size of core with respect of net usable floor area, vertical transportation and elevators, safety, and height limit, and requires integration from the early design phase. Growing population and urban density resulted in increased construction of tall buildings all over the world. It is necessary to develop systems to guide the decision-making process in the design according to the prevailing factors.

This research focuses on development of an ontological model for tall building design in order to represent these factors in a computable form. The notion of computable refers to representation and processing of knowledge by a computer. The resultant knowledge-based model is utilized in a prototype application that assists the decisionmaking process in schematic design. Topics that are addressed in the dissertation research

include:

• Tall buildings and the design process

–  –  –

The primary intellectual contribution of this dissertation is the creation and methodology for the development of a knowledge-based model that explicitly captures implicit integrated design factors. Implicit factors are causes and requirements that lead to certain decisions during the design process, but are considered as tacit. For example, calculations for preferred thermal comfort can be considered as explicit since the known factors (such as number of occupants, building usage, temperature and humidity) can lead to a conclusion that can be expressed numerically. On the other hand, implicit factors are part of the collective design knowledge, and are obtained through education and practice.

For example, relationships between styles, form expression and material selection are implicit^ because these aspects influence each other, but are not feasible to quantify.

Certain styles require particular materials for form expression. Computational operations with architectural design knowledge are extremely challenging, as will be presented in later chapters, since representations of this knowledge are complex to capture.



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