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This study aims to enhance the prediction of disc cutter wear in shield tunneling through optimization algorithms applied to a Backpropagation Neural Network.

May 17, 2026KSCE Journal of Civil EngineeringOpen Access

Back propagation neural network-based prediction model for disc cutter wear in SPB-TBM excavation through composite strata

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Authors

SSShangqu SunShandong University of Science and Technology JGJinlong GengShandong University of Science and Technology ZZZongqing ZhouShandong University

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Randomized trial predicts disc cutter wear in SPB-TBM excavation using optimization algorithms, indicating improved tunneling efficiency.

Key Points

This study aims to enhance the prediction of disc cutter wear in shield tunneling through optimization algorithms applied to a Backpropagation Neural Network.
Utilized 2,128 field data samples from the Shantou Bay Subsea Tunnel.
Employed Genetic Algorithm, Particle Swarm Optimization, and Sparrow Search Algorithm to optimize BPNN.
Conducted correlation analysis and evaluated models based on performance metrics like RMSE and R^2.
SSA-BP model achieved the highest accuracy in predicting disc cutter wear with R^2 = 0.9604.
RMSE for the model was 0.0434 mm and MAPE was 3.11%.
SSA significantly improved predictive capability compared to GA and PSO.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a095a427880e6d24efe065a https://doi.org/10.1016/j.kscej.2026.100628

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Back propagation neural network-based prediction model for disc cutter wear in SPB-TBM excavation through composite strata

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Key Points

Cite This Study

Back propagation neural network-based prediction model for disc cutter wear in SPB-TBM excavation through composite strata

Discussion

Member takes

Implication

Key Points

Cite This Study

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