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Tunnel Portal Section Crown Settlement SVR Prediction Models Based on
Different Optimization Algorithms and Their Comparative Evaluation
(1. School of Highway, Chang'an University, Xi'an 710064; 2.Shaanxi Provincial Key Laboratory of Highway Bridges and Tunnels,Chang'an University, Xi'an 710064)
Abstract Precise prediction of settlement and deformation at tunnel portal section is a priority in ensuring safe en?
try into tunnel, and it is of great importance to solve the problem of high dimensionality of input layer and accurately describe the performance of machine learning prediction model. Therefore, the principal component analysis (PCA),optimization algorithm and support vector regressor (SVR) are combined to generate 6 combination prediction models based on PCA, optimization algorithm and SVR. First, the main factors that affect crown settlement are identified by using PCA. Next, the optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimizer (GWO) are used to optimize the penalty factors and kernel parameters of SVR. At last, the combination prediction model is applied to the Shibitou Tunnel in Wenzhou, the comparative evaluation of the performance of the prediction model is conducted by using the correlation coefficient (R), root mean square error(RMSE) and mean absolute error (MAE), and the model query table is generated. The results indicate: the combination prediction models have high precision, with R≥0.987 0, RMSE≤6.792 4 mm and MAE≤3.493 7 mm. After dimensionality reduction by PCA, the GA optimization allows SVR prediction model to increase its prediction efficiency by 65%, while PSO and GWO optimization allows SVR prediction model to decrease the dimensionality of its input layer but increase its k value, which reduces the prediction efficiency, particularly so in the case of PCA-GWO-SVR.
PCA-PSO-SVR prediction model has a better robustness.
Abstract：
Precise prediction of settlement and deformation at tunnel portal section is a priority in ensuring safe en?
try into tunnel, and it is of great importance to solve the problem of high dimensionality of input layer and accurately describe the performance of machine learning prediction model. Therefore, the principal component analysis (PCA),optimization algorithm and support vector regressor (SVR) are combined to generate 6 combination prediction models based on PCA, optimization algorithm and SVR. First, the main factors that affect crown settlement are identified by using PCA. Next, the optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimizer (GWO) are used to optimize the penalty factors and kernel parameters of SVR. At last, the combination prediction model is applied to the Shibitou Tunnel in Wenzhou, the comparative evaluation of the performance of the prediction model is conducted by using the correlation coefficient (R), root mean square error(RMSE) and mean absolute error (MAE), and the model query table is generated. The results indicate: the combination prediction models have high precision, with R≥0.987 0, RMSE≤6.792 4 mm and MAE≤3.493 7 mm. After dimensionality reduction by PCA, the GA optimization allows SVR prediction model to increase its prediction efficiency by 65%, while PSO and GWO optimization allows SVR prediction model to decrease the dimensionality of its input layer but increase its k value, which reduces the prediction efficiency, particularly so in the case of PCA-GWO-SVR.
PCA-PSO-SVR prediction model has a better robustness.
ZHANG Shichao1,
2 WANG Yaqiong1,
2 GAO Qidong1 etc
.Tunnel Portal Section Crown Settlement SVR Prediction Models Based on
Different Optimization Algorithms and Their Comparative Evaluation[J] MODERN TUNNELLING TECHNOLOGY, 2023,V60(6): 139-150
2023, Vol. 60 