Machine Learning Classifier for Seismic Liquefaction Potential Evaluation
Assistant Professor, Department of Civil Engineering,
Liquefaction potential assessment has been a very important problem from the point of view of geotechnical engineering. It is well known that many factors such as soil parameters and seismic characteristics influence this problem. Various researchers have attempted to solve this problem using artificial neural networks (ANN), a sub-branch of machine learning (ML). However, many authors have missed important issues such as proper data modeling, ANN model selection, and performance evaluation of ANN for liquefaction potential assessment. Covering these aspects, the present paper intends to provide systematic steps to model liquefaction potential data using a ML classifier.
Keywords: Data collection, Data Modeling, Neural Networks, Liquefaction Potential Assessment, Performance evaluation
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