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An approach for qualitative structural analysis

Published online by Cambridge University Press:  27 February 2009

Renate Fruchter ,
Kincho H. Law  and
Yumi Iwasaki
Renate Fruchter
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305
Kincho H. Law
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305
Yumi Iwasaki
Affiliation:
Knowledge System Laboratory, Department of Computer Science, Stanford University, Stanford, CA 94305, U.S.A.
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Abstract

In preliminary design, the details of a structure are insufficient to warrant the use of numeric tools traditionally used in structural analysis. However, an accurate prediction of the behavior of a structure and its components in the preliminary design phase can have a significant effect on the final design process in reducing the number of alternative solutions, avoiding the costly design revisions, and improving the quality of design. Presently, there are few tools available for preliminary analysis of structures. This study represents an initial effort towards the development of a tool that can be used in the conceptual design stage to qualitatively evaluate the behavior of a structure.

This paper describes a prototype system, QStruc, for qualitative structural analysis, which combines first principles in structural engineering and experiential knowledge of structural behavior. The purposes of QStruc are: (1) to generate qualitative models from the schematics of a structure; and (2) to infer the qualitative response of the structure in terms of deflected shape, moments, and reactions. The qualitative analysis strategy employs: (1) a greedy depth-first approach that tries to expand the derived response as much as possible from known parameter values; (2) a causal ordering mechanism, which enables the system to identify the solution path for the qualitative analysis; (3) qualitative calculus, which enables the qualitative evaluation of the physical quantities of the causal model that describes the behavior of the structure; and (4) Quantity Lattice (Simmons, 1986) which enables the system to reason about partial ordering among physical quantities and to reduce some of the ambiguous conclusions caused by the impreciseness of the information. Examples are provided to illustrate the effectiveness and limitations of the prototype system.

Information

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 3 , August 1993 , pp. 189 - 207
Copyright
Copyright © Cambridge University Press 1993

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