Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Slope stability assessment is a critical concern in construction projects. This study explores the use of multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters that are associated with the evaluation of slope stability. A comparative study of machine learning solutions for slope stability assessment that relied on backpropagation neural network (BPNN) and MARS was conducted. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Research results suggest that BPNN and MARS models can model the relationship between the safety factor and the slope parameters. Also, the MARS model has the advantages of computational efficiency and easy interpretation.