Prediction of lateral wall deflections of excavations in water-rich sands by a modified multivariate-adaptive-regression-splines method

Machine learning methods have advantages in predicting excavation-induced lateral wall displacements. Due to lack of sufficient field data, training data for prediction models were often derived from the results of numerical simulations, leading to poor prediction accuracy. Based on a specific quantity of data, a multivariate adaptive regression splines method (MARS) was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands. Sensitivity of lateral wall deflections to affecting factors was analyzed. It is disclosed that dewatering mode has the most significant influence on lateral wall deflections, while the soil cohesion has the least influence. Using cross-validation analysis, weights were introduced to modify the MARS method to optimize the prediction model. Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method (MMARS) is more accurate than that based on the traditional MARS method. The prediction model established in this paper can help engineers make predictions for wall displacement, and the proposed methodology can also serve as a reference for researchers to develop prediction models.