Prediction of Surrounding Rock Deformation in a Highway Tunnel Using an LSTM-RF Hybrid Model
Accurate tunnel deformation prediction is critical for mitigating construction risks and ensuring tunnel stability. This study introduces a novel hybrid model integrating long short-term memory (LSTM) networks and random forest (RF) to enhance the precision of tunnel deformation predictions during c...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/je/6652758 |
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| Summary: | Accurate tunnel deformation prediction is critical for mitigating construction risks and ensuring tunnel stability. This study introduces a novel hybrid model integrating long short-term memory (LSTM) networks and random forest (RF) to enhance the precision of tunnel deformation predictions during construction. Bayesian optimization was utilized to fine-tune model parameters, ensuring optimal performance. Validated with multidepth data from the Yangjiashan highway tunnel in China, the hybrid model demonstrates remarkable adaptability to complex geological conditions. The results show that the LSTM-RF model achieves a mean square error (MSE) of 0.0025, a root-mean-square error (RMSE) of 0.0052, and a coefficient of determination (R2) of 0.9810, outperforming individual models and other hybrid frameworks in predicting deformation trends. By effectively capturing temporal dependencies and modeling nonlinear residuals, the hybrid model provides a robust and reliable solution for improving safety and efficiency in tunneling projects. These findings emphasize the potential of hybrid approaches for geotechnical engineering, particularly in predictive maintenance and infrastructure monitoring. |
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| ISSN: | 2314-4912 |