Machine learning user preferences for structural design

Research output: Contribution to journalArticlepeer-review


The design process often proceeds through iterative stages of design configuration, analysis, evaluation, and redesign with the ultimate goal of optimization. Numerical methods for structural design optimization of only one attribute such as weight, strength, or cost are well known. However, these methods do not reflect the fact that designs are evaluated by the user in terms of their performance in several attributes. It has been extremely difficult to incorporate multiple attributes into design optimization algorithms because the acceptable tradeoffs between these attributes vary significantly between users. This paper presents a new method for learning user-specific preferences and integrating them into the design evaluation, analysis, and optimization process in a meaningful way. The approach is a synthesis of formal decision theoretic methods with conventional design analysis techniques. The overall design objective is optimization of multiattribute utility from the viewpoint of the user. A user-interactive computer-aided Multiattribute Structural Design Evaluation and Optimization System (MSDEOS) is presented. It enables machine learning of the user's willingness to make tradeoffs between performance attributes. With this system, it is feasible to integrate site-specific consideration of multiple attributes directly into computer aids for structural design optimization. Two examples are presented: seismic design, where tradeoffs are made between cost and damage index, and design of a three-story steel frame structure, where attributes are cost and drift index. The system learns the preferences of different users and reflects those preferences through the identification of a different optimal solution for each user.

Original languageEnglish (US)
Pages (from-to)185-197
Number of pages13
JournalMicrocomputers in civil engineering
Issue number3
StatePublished - 1994

ASJC Scopus subject areas

  • General Computer Science
  • General Environmental Science
  • General Engineering
  • General Earth and Planetary Sciences


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