Abstract
Abstract 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.
Highlights
Landslide is a common type of geological disaster, which always impose heavy social and economic losses [1,2,3]
This study explores the use of multivariate adaptive regression splines (MARS) [27] to capture the intrinsic nonlinear and multidimensional relationship associated with the evaluation of slope stability
One data set with actual slope collapse events was utilized for model development and to compare the performance of backpropagation neural network (BPNN) and MARS
Summary
Landslide is a common type of geological disaster, which always impose heavy social and economic losses [1,2,3]. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Each link that connects each neuron has an associated weight They can learn some target values (desired output) from a set of selected input data through a computing network system under supervised and self-adjusted or unsupervised learning algorithms [29]. The steps in the BPNN algorithm are described as follows: first, the network weights are initialized, and the input data are presented forward from the input to the output layer, thereby producing actual output. To enhance the prediction capacity of the MARS model, the backward phase considers the residual error and the model complexity, and the redundant BFs that have the least contributions are deleted. The prediction of safety factors using the MARS model with second order interaction adopted 25 BFs of linear spline function. An open MARS source code from ref. [40] is adapted to perform the analyses presented in this study
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