Modeling and debugging engineering decision procedures with machine learning

Yoram Reich, Miguel A. Medina, Tung Ying Shieh, Timothy L. Jacobs

Research output: Contribution to journalArticlepeer-review

Abstract

This paper reports on the use of machine learning programs for modeling existing engineering decision procedures. In this acitivity, different models of a decision procedure are constructed by using different machine learning programs as well as by varying their operational parameters and input. These models serve to focus on different aspects of the decision procedure thus improving its understandability, which, in turn, can assist in its evaluation and subsequent debugging. This important modeling role of machine learning programs is exemplified by modeling an existing decision procedure that is used by engineers when they need guidance in selecting among available techniques for modeling ground-water flow in a process of environmental decision making. This decision procedure was corrected and improved in the course of this work. The example demonstrates the practical utility of the modeling role of machine learning for engineering applications.

Original languageEnglish
Pages (from-to)157-166
Number of pages10
JournalJournal of Computing in Civil Engineering
Volume10
Issue number2
DOIs
StatePublished - Apr 1996

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