Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network
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DOI: 10.1016/j.ress.2022.108439
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Cited by:
- 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.
- 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).
- 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).
- 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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Keywords
Prediction intervals; Tunnel-induced settlement; Artificial intelligence;All these keywords.
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