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Applying design-dependent knowledge in structural engineering design

Published online by Cambridge University Press:  27 February 2009

H. Craig Howard ,
Jenmu Wang ,
Francois Daube  and
Taufiq Rafiq
H. Craig Howard
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305
Jenmu Wang
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305
Francois Daube
Affiliation:
Schlumberger Palo Alto Research Center, Palo Alto, CA 94304, U.S.A.
Taufiq Rafiq
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305
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Abstract

Recent knowledge-based expert systems for structural engineering design have focused on design-independent knowledge (abstract reasoning rules for designing), and while great strides have been made in that area, there is still a significant need to develop systems to take advantage of the wealth of knowledge contained in every substantial structural design. On the other hand, previous database-oriented design efforts have focused primarily on knowledge-poor databases of solutions, in which the traditional engineering handbook of solutions has simply been replaced by digital data. The challenge is to find a way to capture and apply the kind of case-based, design-dependent knowledge that structural engineers have traditionally used. The long-term results will be better structural designs and better structural designers. This paper discusses the character of the design-dependent knowledge in a structural engineering context, describes two initial applications of case-based reasoning to component design, and presents a general paradigm for a knowledge-based design system integrating rule-based and case-based reasoning.

Type
Research Article
Information
AI EDAM , Volume 3 , Issue 2 , May 1989 , pp. 111 - 123
Copyright
Copyright © Cambridge University Press 1989

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References

Cagan, J. and Agogino, A. 1988. Innovative design of mechanical structures from first principles. Working Paper, Dept of Mechanical Engineering, University of California, Berkeley.Google Scholar
Carbonell, J. G. 1982. Learning by Analogy: Formulating and Generalizing Plans from Past Experience. Technical Report CMU-CS-82–126, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA.CrossRefGoogle Scholar
Carbonell, J. G. 1985. Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition. Technical Report CMU-CS-85–115, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
Daube, F. and Hayes-Roth, B. 1988. FIRST: A case-based redesign system in the BB1 blackboard architecture. Proceedings of the AAAI-88 Case-Based Reasoning Workshop.Google Scholar
Gentner, D. 1983. Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.Google Scholar
Hammond, K. J. 1986. Learning to anticipate and avoid planning problems through the explanation of failures. Proceedings of AAAI-86, Fifth National Conference on Artificial Intelligence pp. 556–560. Philadelphia, PA: Morgan Kaufmann Publishers.Google Scholar
Hayes-Roth, B. 1985. A blackboard architecture for control. Artificial Intelligence, 26, 251–321.Google Scholar
Howe, A. E., Cohen, P. R., Dixon, J. R. and Simmons, M. K. 1986. Dominic: A domain-independent program for mechanical engineering design. The International Journal for Artificial Intelligence in Engineering, 1, 23–28.Google Scholar
Huhns, M. N. and Acosta, R. D. 1987. Argo: An Analogical Reasoning System for Solving Design Problems, MCC Technical Report AI/CAD-092–87.Google Scholar
Kedar-Cabelli, S. 1985. Purpose-Directed Analogy, Technical Report ML-TR-1, Department of Computer Science, Rutgers University, New Brunswick, New Jersey.Google Scholar
Kolodner, J. L. (Ed.) 1988. Processing of the Darpa Workshop on Case-Based Reasoning. Kluwer, J..Google Scholar
Maher, M. L. and Fenves, S. J. 1985. HI-RISE A Knowledge-Based Expert System for the Preliminary Structural Design of High Rise Buildings, Technical Report R-85–146, Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
Mostow, J. and Barley, M. 1987. Automated Reuse of Design Plans, Technical Report ML-TR-14, Department of Computer Science, Rutgers University.Google Scholar
Murthy, S. S. and Addanki, S. 1987. PROMPT: an innovative design tool, Proceedings of AAAI-87, Sixth National Conference on Artificial Intelligence, Seattle, Washington, pp. 637–642. The American Association for Artificial Intelligence.Google Scholar
Navinchandra, D. 1988. Case based reasoning in CYCLOPS, a design problem solver Proceedings, DARPA Case-Based Reasoning Workshop. Los Altos, CA: Morgan Kaufmann.Google Scholar
Nii, H. P. 1986. Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architectures (Part One). AI Magazine, 38–53.Google Scholar
Nii, H. P. 1986. Blackboard systems: Blackboard application systems, blackboard systems from a knowledge engineering perspective (Part Two). AI Magazine, 82–106.Google Scholar
Prieditis, A. (Ed) 1988. Analogica. Los Altos, CA: Morgan Kaufmann.Google Scholar
Proceedings AAAI-88 Case-Based Reasoning Workshop, Minneapolis, St. Paul, MN: American Association for Artificial Intelligence.Google Scholar
Quillian, M. R. 1968. Semantic memory in Minskey, M. (Ed), Semantic Information Processing, Cambridge, MA: MIT Press.Google Scholar
Rafiq, T. 1989. Similarity in structural component case bases, unpublished Engineer’s Degree Thesis, Department of Civil Engineering, Stanford University, 1989.Google Scholar
Schank, R. C. 1981. Failure-driven memory. Cognition and Brain Theory, 4, 41–60.Google Scholar
Slade, S. 1988. Case-Based Reasoning: A Research Paradigm, Technical Report YALEU/CSD/RR#644, Department of Computer Science, Yale University.CrossRefGoogle Scholar
Smith, R. G., Mitchell, T. M., Chestek, R. A. and Buchanan, B. G. 1977. A Model for Learning Systems, Proceedings of the Fifth IJCAI, pp. 338–343.Google Scholar
Sowa, J. 1984. Conceptual Structures. Reading, MA: Addison Wesley.Google Scholar
Sriram, D. 1986. Knowledge-based approaches for integrated structural design, unpublished Ph.D. Dissertation, Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
Tulving, E. 1983. Elements of Episodic Memory Oxford: Oxford University Press.Google Scholar
Wang, J. 1988. Design-dependent knowledge for structural engineering design, unpublished Ph.D. Thesis Proposal, Department of Civil Engineering, Stanford University.Google Scholar
Winston, P. H. 1980. Learning and Reasoning by Analogy: The Details, Technical Report AIM 520, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.Google Scholar
Zhao, F. and Maher, M. L. 1988. Using analogical reasoning to design buildings. Engineering with Computers, 4, 107–119.CrossRefGoogle Scholar