Prediction of Wave-induced Liquefaction using Artificial Neural Network and Wide Genetic Algorithm

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Dwi Kristianto Chastine Fatichah Bilqis Amaliah Kriyo Sambodho


The hassle of analytical and numerical solution for liquefaction modeling, repetitive laboratory testing and expensive field observations, have opened opportunities to develop simple, practical, inexpensive and valid prediction of wave-induced liquefaction. In this study, Artificial Neural Network (ANN) regression modeling is used to predict the depth of liquefaction. Despite of using Back Propagation (BP) to train ANN, a modified Genetic Algorithm (called as Wide GA, WGA) is used as ANN training method to improve ANN prediction accuracy and to overcome BP weaknesses such as premature convergence and local optimum. WGA also aim to avoid conventional GA weaknesses such as low population diversity and narrow search coverage. Key WGA operations are Wide Tournament Selection, Multi-Parent BLX-? Crossover, Aggregate Mate Pool Mutation and Direct Fresh Mutation-Crossover. ANN prediction accuracy measured by Median APE (MdAPE). Global optimum solution of WGA is best ANN connections weights configuration with smallest MdAPE.


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KRISTIANTO, Dwi et al. Prediction of Wave-induced Liquefaction using Artificial Neural Network and Wide Genetic Algorithm. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 1-10, mar. 2017. ISSN 2541-5832. Available at: <>. Date accessed: 28 sep. 2020. doi:
Wave-induced Liquefaction, Prediction of Soil Liquefaction, Wide Genetic Algorithm, Artificial Neural Network, Back Propagation


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