Machine learning in engineering analysis and design: An integrated fuzzy neural network learning model

Shih-Lin Hung*, J. C. Jan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Applying neural network computing to structural engineering problems has received increasing interest, with particular emphasis placed on a supervised neural network with the backpropagation (BP) learning algorithm. In this article, we present an integrated fuzzy neural network (IFN) learning model by integrating a newly developed unsupervised fuzzy neural network (UFN) reasoning model with a supervised learning model in structural engineering. The UFN reasoning model is developed on the basis of a single-layer laterally connected neural network with an unsupervised competing algorithm. The IFN learning model is compared with the BP learning algorithm as well as with a counterpropagation learning algorithm (CPN) using two engineering analysis and design examples from the recent literature. This comparison indicates not only a superior learning performance in solved instances but also a substantial decrease in computational time for the IFN learning model. In addition, the IFN learning model is applied to a complicated engineering design problem involving steel structures. The IFN learning model also demonstrates superior learning performance in a complicated structural design problem with a reasonable computational time.

Original languageEnglish
Pages (from-to)207-219
Number of pages13
JournalComputer-Aided Civil and Infrastructure Engineering
Issue number3
StatePublished - 17 Dec 2002


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