Accurate prediction of ground settlement induced by rectangular pipe jacking, a
prevalent trenchless technology in urban infrastructure development, remains a
significant challenge. This study addresses this by developing and evaluating a
robust machine learning (ML) framework. Leveraging 104 sets of field monitoring
data from the Liuye Avenue West Extension rectangular pipe jacking project in
Hunan, China, key construction parameters including jacking force, advance rate,
and grouting pressure were utilized as inputs to predict ground settlement. A
Particle Swarm Optimization (PSO) algorithm was integrated for automated
hyperparameter tuning of six distinct ML models: standalone Least Squares
Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), Random
Forest (RF), and their respective PSO-optimized counterparts. Comprehensive
performance evaluation using Mean Squared Error (MSE), Mean Absolute Error
(MAE), and Coefficient of Determination (R^2) revealed that the
PSO-LSSVM hybrid model achieved superior predictive accuracy and generalization
capability. Specifically, on the test dataset, the PSO-LSSVM model yielded an
MSE of 0.367, MAE of 0.424, and an R^2 of 0.941. These findings
demonstrate that the proposed PSO-enhanced LSSVM model significantly outperforms
baseline models, offering a highly effective and reliable tool for predicting
ground deformation in similar complex pipe jacking projects.