Citation:
Date Published:
2021Abstract:
Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (ΔhEQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (ΔhEQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.