Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models

• By optimizing the hyper-parameters of least squares support vector regression using particle swarm optimization, we constructed the PSO-LSSVR model for the reconstruction of missing monitoring data during tunnel construction. • By optimizing the hyper-parameters of Gaussian process regression using particle swarm optimization, we constructed the PSO-GPR model for the prediction of surrounding rock deformation in tunnels. • This study achieved the goal of predicting surrounding rock deformation even with partial missing monitoring data through the constructed PSO-LSSVR reconstruction model and PSO-GPR prediction model. Predicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based Least Squares Support Vector Regression model (PSO-LSSVR), and subsequently predicted the tunnel surrounding rock deformation using the constructed Gaussian Process Regression model (PSO-GPR).The research results indicate that the average relative error of the PSO-LSSVR reconstruction model is 1.21 %, lower than the 4.82 % of the LSSVR reconstruction model and the 4.69 % of the BP reconstruction model. The relative errors of the PSO-LSSVR prediction model and the BP prediction model are 0.55 % and 2.9 %, respectively, both higher than the PSO-GPR prediction model. The PSO-GPR model considers three covariance functions: the Squared Exponential function (SE), the Rational Quadratic function (RQ), and the Matern function (Matern), with relative errors of 0.16 %, 0.15 %, and 0.23 % in the test results, respectively. However, PSO-GPR-SE has a computational efficiency advantage.Overall, PSO-GPR-SE is a suitable model for predicting the deformation of surrounding rock during mountain tunnel construction.