Guangchun Zhou, Yaqub M. Rafiq, Chengfei Sui and Lingyan Xie
A CA And ANN Technique Of Predicting Failure Load And Failure Pattern Of Laterally Loaded Masonry Panel
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M. Y. Rafiq, C. Sui, D. J. Easterbrook, G. Bugmann
Generality of using correctors to predict the behaviour of masonry wall panels
Abstract: The highly composite and anisotropic nature of masonry, which is a result of the variation in the proper-ties of the masonry constituents, makes it very difficult to find an accurate material model to predict its behaviour satis-factorily. Current research by the authors has focused more closely on the behaviour of laterally loaded masonry wall panels using model updating techniques supported by artificial intelligence (AI) tools. They developed the concept of corrector factors which models the variation in the properties over the surface of masonry wall panels. This research resulted in methodologies, which enables designers to more confidently predict the behaviour of masonry wall panels subjected to lateral loading. The paper will demonstrate the generality of using these techniques to predict the behav-iour of laterally loaded masonry wall panels tested by various sources.
Keywords: corrector factors, evolutionary computation, cellular automata
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