Artificial Intelligence
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Toward the implementation of automated analysis idealization control
Proceedings of the third ARO workshop on Adaptive methods for partial differential equations
Toward intelligent object-oriented scientific applications
Engineering computational technology
Ontologies for supporting engineering analysis models
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Intelligent FEA-based design improvement
Engineering Applications of Artificial Intelligence
Framework for controlled cost and quality of assumptions in finite element analysis
Finite Elements in Analysis and Design
Reusability-based selection of parametric finite element analysis models
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Improved knowledge management through first-order logic in engineering design ontologies
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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The authors propose a knowledge-based framework for assisting users in setting up, interpreting, and hierarchically refining finite-element models in a structural engineering domain. The central mechanism for providing modeling assistance is the explicit representation and incremental activation and refraction of modeling assumptions that operate on functional descriptions.Finite-element analysis programs and their pre- and post-processors have reached very high levels of maturity and sophistication in analytical capabilities. As a result, finite-element methods have become the standard techniques for evaluating the physical performance of structural systems in various engineering applications. In contrast, systems that assist engineers in the critical tasks of modeling and model interpretation have not matched this maturity.Current finite-element preprocessors operate at a relatively low level. They do not let analysts simply describe a physical structure with high-level analysis objectives and obtain corresponding finite-element models appropriate for these objectives. Similarly, current postprocessors cannot evaluate the reasonableness of the assumptions built into the models or suggest model refinements if appropriate. Nor are there general-purpose tools that can generate high-level abstractions of the numerical results (for example, in terms of stress paths), identify possible failure modes, or link results to applicable code and standard provisions. Such tools for finite-element modeling assistance have great potential for improving the overall efficiency and reliability of analysis. In this article, we describe the issues and obstacles in the modeling and model interpretation tasks and present a knowledge-based framework to assist users in performing these tasks. We illustrate the framework through an implementation in a structural engineering domain.