Techniques for Predicting Cracking Pattern of Masonry Wallet Using Artificial Neural Networks and Cellular Automata

Abstract

This paper introduces innovative artificial intelligent techniques for directly predicting the cracking patterns of masonry wallets, subjected to vertical loading. The von Neumann neighborhood model and the Moore neighborhood model of cellular automata (CA) are used to establish the CA numerical model for masonry wallets. Two new methods—(1) the modified initial value method and (2) the virtual wall panel method—that assist the CA model are introduced to describe the property of masonry wallets. For practical purposes, techniques for the analysis of wallets whose bed courses have different angles with the horizontal bottom edges are also introduced. In this study, two criteria are used to match zone similarity between a “base wallet” and any new “unseen” wallets. This zone similarity information is used to predict the cracks in unseen wallets. This study also uses a back-propagation neural network for predicting the cracking pattern of a wallet based on the proposed CA model of the wallet and some data of recorded cracking at zones. These techniques, once validated on a number of unseen wallets, can provide practical innovative tool for analyzing structural behavior and also help to reduce the number of expensive laboratory test samples.