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Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network

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  • Feng, Liuyang
  • Zhang, Limao

Abstract

This paper constructs the prediction intervals (PIs) of the tunnels’ settlement caused by the shielding steering process. The hybrid genetic algorithm -neural network (GA-NN) is developed to obtain the upper and lower bound of the settlement based on a series of shield operating parameters, geological condition parameters, tunnel geometric parameters, and anomalous conditions. An improved prediction interval-based cost function is proposed to enable the consideration of the uncertainty from model misspecification and noise variance. The genetic algorithm optimizes the weighted parameters in the neural network by minimizing the cost function value. This study adopts a metro tunnel construction case in China to verify the effectiveness of the proposed hybrid genetic algorithm-neural network approach. The results based on the study case illustrate the superiority of the proposed hybrid approach in (1) overcoming the limitations of the conventional prediction interval indicator; (2) achieving comparative results with the deterministic estimation based on the least squares support vector machine; (3) providing a probability prediction of the settlement only based on deterministic input multivariables. Overall, this study contributes to (1) the uncertainty assessment of tunnel settlement based on the deterministic variables, (2) developing a new PIs based cost function which is stable and reliable, (3) the engineering practice for a safer assessment based on the prediction intervals.

Suggested Citation

  • Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001053
    DOI: 10.1016/j.ress.2022.108439
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    References listed on IDEAS

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    1. Feng, Liuyang & Zhang, Limao, 2021. "Assessment of tunnel face stability subjected to an adjacent tunnel," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. Zhu, Xujia & Sudret, Bruno, 2021. "Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    4. Bensmain, Yassir & Dahane, Mohammed & Bennekrouf, Mohammed & Sari, Zaki, 2019. "Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: A hybrid genetic algorithm based approach," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 546-566.
    5. Lins, Isis Didier & Droguett, Enrique López & Moura, Márcio das Chagas & Zio, Enrico & Jacinto, Carlos Magno, 2015. "Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 120-128.
    6. Liu, Wenli & Chen, Elton J. & Yao, Erlei & Wang, Yanyu & Chen, Yangyang, 2021. "Reliability analysis of face stability for tunnel excavation in a dependent system," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    7. Liu, Di & Wang, Shaoping, 2021. "An artificial neural network supported stochastic process for degradation modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
    10. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.
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    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Pan, Yue & Qin, Jianjun & Hou, Yongmao & Chen, Jin-Jian, 2024. "Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Huang, Peng & Gu, Yingkui & Li, He & Yazdi, Mohammad & Qiu, Guangqi, 2023. "An Optimal Tolerance Design Approach of Robot Manipulators for Positioning Accuracy Reliability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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