On June 20th, 1992, a workshop entitled the “Preliminary Stages of Engineering Analysis and Modeling” was held at Carnegie Mellon University, Pittsburgh, U.S.A. The workshop was organized in conjunction with the 2nd International Conference on Artificial Intelligence in Design (AID ’92), and aimed to provide a forum for engineers and computer scientists to examine how AI techniques could be exploited effectively in assisting engineers with the preliminary stages of engineering analysis and modeling. This report has two objectives; firstly it serves as a summary of the workshop and secondly it introduces four of the workshop papers that have been rewritten and extended for this issue.

This paper discusses a work in progress in the development of computer tools for qualitative modeling analysis and evaluation of conceptual structural designs. In the conceptual design stage the description of a structure is incomplete and imprecise, and does not permit the use of traditional numerical analysis tools. We describe a prototype system, QLRS, for qualitative evaluation of lateral load resistance in frames. The primary goal of the evaluation of structural response is to identify undesirable structural behavior. In QLRS, the evaluation process consists of three basic tasks. (1) identification of the story and structure models comprising the lateral load resisting system. We term this task structural system interpretation. (2) Qualitative analysis of the story and structure models, and approximate evaluation of the story drifts. We term this task structural behavior interpretation. (3) Assessment of the performance of the lateral load resisting system, in which the results of the structural system interpretation and the structural behavior interpretation are compared against the requirements for complete load path and relative story drift. Currently, QLRS is able to reason about load path discontinuities and soft-story behavior problems in 2-D moment resisting frames.

Arguing that design is a social process, we expand the meaning of modeling and analysis to include all activities facilitating continual refinement and criticism of the design requirements, process and solutions. We do not assume any a priori methods for modeling or analysis; rather, we provide a framework and an approach to study designers and give them whatever modeling and analysis capabilities they choose. Our approach is the basis for a support tool, n–dim, currently under development.

This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.

The task of modeling, i.e., of creating a set of equations that can be used to predict the behavior of a physical object, is a key step in engineering analysis. This paper describes a computer system, MSG, for generating mathematical models to analyze physical systems involving heat transfer behavior. MSG is motivated by the need for modeling in an automated design process. The models are sets of equations which may include algebraic equations, ordinary differential equations and partial differential equations. MSG uses the strong domain theory to guide model construction in three sequential tasks: identify regions of interests on an object, determine relevant heat transfer and energy storage processes, and transform these processes into equations. The decisions in these tasks are guided by estimates of variation in temperature and material property, and the relative strengths of heat transfer processes.