Probabilistic Design of Retaining Wall Using Machine Learning Methods

Abstract

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement in technology, the implementation of artificial intelligence techniques is done for the reliability analysis of the structure. Designing the structure based on the probability of failure leads to an economical design. Machine learning models used for predicting the factor of safety of the wall are Emotional Neural Network, Multivariate Adaptive Regression Spline, and SOS–LSSVM. The First-Order Second Moment Method is used for calculating the reliability index of the wall. In addition, these models are assessed based on the results they produce, and the best model among these is concluded for extensive field study in the future. The overall performance evaluation through various accuracy quantification determined SOS–LSSVM as the best model. The obtained results show that the reliability index calculated by the AI methods differs from the reference values by less than 2%. These methodologies have made the problems facile by increasing the precision of the result. Artificial intelligence has removed the cumbersome calculations in almost all the acquainted fields and disciplines. The techniques used in this study are evolved versions of some older algorithms. This work aims to clarify the probabilistic approach toward designing the structures, using the artificial intelligence to simplify the practical evaluations.

Dynamic displacement of sheet pile walls adjacent to the railway track

“…This testing is essential for guaranteeing railroad tracks wellbeing and life span [5]. To give an extensive assessment, the segment tested should include commonplace track parts, like rails, sleepers, counterbalance, and subgrade [6]. Ground movement induced by excavation is a challenging problem in geotechnical engineering.…”

Perspective on the Application of Machine Learning Algorithms for Flow Parameter Estimation in Recycled Concrete Aggregate

“…Machine learning methods have been extensively used in recent decades to generate predictive models of material properties [ 18 , 21 , 22 ]. Support vector regression (SVR) was used by Mishra et al, (2021) to predict the probabilistic design of a retaining wall [ 23 ], and Amin et al, (2021) used an artificial neural network (ANN) to design the optimal portions content of RHAC [ 24 ]. Lechowicz and Sulewska (2023) applied ANN to develop empirical relationships used in a preliminary design to evaluate the settlement and unconfined shear strength of embankment-loaded organic soil [ 25 ], while Chou et al, (2016), among others, used data mining including linear regression, classification, and regression tree analysis (CART) to identify factors affecting the shear strength and predict the peak FRS friction angle [ 26 ].…”

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

“…Hence, ML does not assume a statistical model [25][26][27]. Additionally, these techniques have been widely applied in engineering [28][29][30][31][32][33]. For example, Zhang et al [34] used XGBoost and Bayesian optimization to predict the shear strength of soft clays.…”