BUILDING KNOWLEDGE LEVEL ONTOLOGY FOR CONCEPTUAL DESIGN OF STEEL STRUCTURES Onuegbu Ugwu #, Chimay Anumba##, Tony Thorpe##, Tomasz Arciszewski### # Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
[email protected] ## Centre for Innovative Construction Engineering (CICE), Dept of Civil and Building Engineering, Loughborough University, Leicestershire, UK {c.j.anumba, a.thorpe}@lboro.ac.uk ### Civil, Environmental & Infrastructure Engineering Dept, George Mason University, Fairfax, USA
[email protected]
Abstract: This paper presents a problem-oriented approach to ontology construction and knowledge representation in the application of agent-based systems for conceptual design. It identifies current problems associated with ontology import and re-use and emphasises the need for standardisation in ontology development. It concludes that building domain ontologies is a requirement for successful agent-based applications and enterprise integration, and that standardisation of ontology construction will improve knowledge re-use and minimise duplication of development efforts. Also, the authors provide recommendations for ontology development in the AEC sector.
KEYWORDS: Conceptual design, Intelligent agent, Ontology, Steel structures, Knowledge representation INTRODUCTION Domain ontologies are widely seen as the solution to current problems of data/information sharing, knowledge re-use, and inter-operability between software applications. This is because ontology at the knowledge level encapsulates both concepts that define a domain as well as the various task Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
primitives, and domain knowledge required for problem solving. This paper presents a problem-oriented approach to ontology construction and knowledge representation in the application of agent-based systems to conceptual design. In this approach, the conceptual model of the domain of task agents underpins the resulting ontology. The paper describes the development of an ontology for steel structures, using an agent development shell (Disciple) that was developed at George Mason University, Fairfax, USA [1,2]. It uses some case-study research projects to demonstrate the application of the problem-oriented approach, identifies some problems associated with knowledge import, ontology re-use, and emphasises the need for standardisation in ontology development. The authors conclude that domain ontologies constitute the main drivers for successful agentbased enterprise integration, and that standardisation of ontology construction will improve re-use and minimise duplication of efforts. Recommendations are given for ontology development in the AEC sector. The structure of the paper is as follows: Section 2 describes a conceptual design process. Section 3 discusses knowledge modelling, and identifies the process of modelling the problem (application) domain and hence the requirements of the domain ontology. Section 4 discusses applications of the ontology and shares the practical problems we encountered in implementing the ontology with emphasis on knowledge re-use, and ontology imports. Finally, section 5 draw some conclusions and gives recommendation for ontology development in construction.
2. CONCEPTUAL DESIGN OF STEEL STRUCTURES This section defines a context for the ontology construction in the domain of conceptual design. It gives a brief description of the design process, and looks at the requirements for constructability evaluation at different design interfaces. Generally, design commences upon the receipt of a client’s brief that outlines the functional requirements of a proposed project. In the traditional procurement route, the client briefs the Architect who translates the brief into a set of conceptual structural configurations (dimensions) that meet the requirements. At the conceptual level of a scheme design, the key decisions would focus on basic plan layout/outline, and the dimensions of mechanical spaces etc. The Architect’s specification would form a basis for conceptual Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
and subsequent detailed structural design, which translates the plan and loading requirements into structural layouts, dimensions (section and length) of structural members such as beams, columns, rafter etc. Previous interviews with industry professionals have highlighted the need to evaluate things like constructability implications of design decisions at various decision points [3]. As an illustration, constructability requirements would be improved at the conceptual stage by relating structural dimensions to member design length, checking standard sizes. What is required is therefore an intelligent agent that uses its knowledge base (i.e. ontology + rules) to act as an assistant to designers, helping them to choose design options that address the various requirements of the team.
3. ONTOLOGY FOR CONCEPTUAL DESIGN This section discusses the steps in ontology construction, and identifies the structure, features and content of the ontology for conceptual design using agent-based systems. 3.1. Phases in Ontology Development Ontology development involves a sequence of phases. However, the first requirement is actually to decide the use and scope of application of the ontology. The ensuing section summarises these phases. In phase one, necessary information on the processes and data requirements are obtained using appropriate knowledge elicitation techniques such as interviews from domain experts, reference standard documents, and published research papers etc. The second phase involves abstraction and identification of design concepts, attributes/ features that define the concepts. This may involve a protocol analysis of knowledge acquisition data using appropriate knowledge acquisition tools. The third phase involves a reformulation of the identified concepts by grouping existing concepts together, creating new concepts or combining some synonymous concepts. Phase four involves generating relationships that show structural relationship between the concepts while the last phase involves verification of the concepts/processes and the derivation of a common consensus on the choice of terms. Ugwu et al [3, 4] describe these phases in detail. Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
3.2. Knowledge & Problem Domain Modelling Intelligent agents require rich knowledge base(s) as the underlying infrastructure with which to perform their tasks. This section describes knowledge representation and the structuring of a typical agent’s knowledge base into ontology and a set of problem solving rules. In such representation, the ontology serves as a generalisation hierarchy that an agent uses in performing its designated design tasks. In order to develop the agent’s knowledge base, we perform a task based modelling of the conceptual design process, and give considerations to the requirements of various design specialists and the tasks that are often assigned to them. Thus, we adapt a task reduction paradigm in the problem solving. This means that a given design task to be accomplished is successfully divided into a number of subtasks, which can be considered independently and their results integrated. Boicu et al [5] give a detailed description of this paradigm in problem solving, and identified six types knowledge elements that are required in applying this paradigm to an application domain. These include: objects, features, tasks, examples, explanation, and rules. However, this paper focuses on the first three elements as they form the core composition of an agent’s ontology, and constitute the basic knowledge elements. These key features of the ontology are discussed in the ensuing section. 3.3. Composition of the Ontology Objects: The objects represent sets of things (including individuals) in the application domain. They are organised hierarchically according to identified generalisation/relationships such as superclasses, and instances. Features: The features are sets of attributes used to further describe the objects and other problem solving tasks. A feature has a domain (i.e. the sets of objects that could have these attributes) and range(s) – the sets of possible values of these attributes. Tasks: A task is a representation of the problem that can be accomplished using the ontology. Tasks are also hierarchically organised. Consider the simple case of designing a single bay, 20m span portal frame building that has a loading of 10 KN/m2. The sets of design subtasks include designing structural elements (such as beams, columns, rafter), column base area, holding down bolts, flats, angles etc to meet the specifications. An important feature of Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
this approach to knowledge modelling for a given problem domain, is that the task and problem-solving processes identify the concepts that are required in the ontology in order to use it and solve the design problem. Figures 1 –3 below are snapshots of the resulting ontology.
Figure1: Object Browser for the Concepts
Figure2: A hierarchical composition of the Ontology
Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
Figure3: Features of the Ontology
4. APPLICATIONS OF THE ONTOLOGY The above ontology has been applied in two different contexts in construction. The problem domains include collaborative design of light industrial buildings, and automated agent learning. These are described below. Collaborative Design of Light Industrial Buildings: The Agent-based Collaborative Design of Light Industrial Buildings (ADLIB) is a research project that investigated the issues involved in the application of the agent technology to design problems, with a focus on the conceptual design of portal frame structures. One of the core issues investigated was automated negotiation in a multi-agent environment. The collaborating agents share a common ontology for the problem domain, and use the underlying ontology and their respective problem solving rules to execute tasks and reach design decisions [6,7,8]. Automated Agent Learning: Another application of the ontology focused on teaching agents how to solve design problems using a mixed-initiative learning strategy. The core objective was to investigate the potential application of agents that learn from domain experts using the task Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
decomposition paradigm, underpinned by the underlying domain ontology. This work involved implementing the ADLIB ontology in the Disciple shell, and using a set of problem-solving examples to train the agents on how to generate solutions to design problems. The agent learns by analogical reasoning, and automatically generates rules appropriate for the design solution. The above projects demonstrate that domain ontologies underpin successful development and application of intelligent agents to problem solving. However, some key issues emerged during the above investigation. It was observed that while the original ADLIB ontology was constructed using another agent toolkit ZEUS, the existing ontology file could not be imported directly into Disciple. Consequently the entire ontology was manually re-created in Disciple. This resulted in duplication of the ontology building efforts, and hence mitigated against domain knowledge re-use. There is clearly a requirement to standardise ontology development in the AEC sector.
5. CONCLUSION This paper described ontology development for conceptual design and highlighted the problems associated with modelling, and automating the design process. It identified some knowledge elements required for conceptual design using intelligent agents. The authors used case study applications to highlight the practical issues and problems related to ontology import and knowledge re-use and emphasised the need to standardise ontology creation efforts in the construction domain. It is hoped that our description of ontology for conceptual structural design, will stimulate some discussion on symbolic knowledge modelling and other representational issues in construction. This will be a major step towards developing and implementing intelligent co-operating systems in the architecture, engineering and construction sector.
ACKNOWLEDGEMENTS The ADLIB research project was conducted at Loughborough University, UK and funded by the Engineering and Physical Sciences Research Council Ugwu O. O, Anumba C. J., Thorpe A, Arciszewski T; 2002: ‘Building Knowledge Level Ontology for the Collaborative Design of Steel Frame Structures’, Advances in Intelligent Computing in Engineering - Proceedings of 9th International Workshop of the European Group of Intelligent Computing in Engineering (EG-ICE), Schnelleenbach-Held M and Denk H Eds. Technische Universitat Darmstadt, Germany, August 01-03, 2002, pp 71-78.
(EPSRC) UK, (Grant No: GR/M42169) as part of the Innovative Manufacturing Initiative (IMI). The work on Automated Agent Learning was investigated during Dr. Ugwu’s visit to the Civil, Environmental and Infrastructure Engineering Department in the Information Technology and Engineering School at George Mason University in January/February 2002, as a Visiting Research Scholar.
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