Intelligent prediction of long-term tunnel service performance in complex strata via a deep surrogate model

Abstract

Tunnels often traverse variable and complex strata， thereby posing significant challenges in analyzing long-term tunnel service performance. This study proposes the tunnel service performance deep surrogate model （TSP-DSM）， an intelligent prediction framework designed for predicting long-term tunnel service performance under complex geological conditions. The TSP-DSM framework employs DenseNet as a deep surrogate model， extracting multilevel feature representations from tunnel geological cross-sectional diagrams and horizontal-convergence monitoring data， and adaptively learning the potential nonlinear mapping relationship between geological conditions and service performance. To increase the prediction accuracy， the model’s hyperparameters are optimized via random grid search. Experimental results demonstrate that the TSP-DSM achieves a training accuracy of 91.29%， outperforming GoogleNet by 12.79% and ResNet by 5.72%； the prediction performance achieved using grayscale images is comparable to that attained using RGB images. On test samples， which were sourced from complex strata， the TSP-DSM achieves a prediction accuracy of 78.19%， demonstrating strong generalization across various strata. A tunnel service-performance visualization map， constructed based on the intelligent prediction results， provides an intuitive basis for tunnel condition assessment and risk prediction.

Keywords

tunnel service performance / tunnel convergence / deep learning / surrogate model / intelligent prediction / complex strata

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Funding

National Natural Science Foundation of China(52192664)