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Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

Author

Listed:
  • Mingcheng Zhu

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

  • Shouqian Li

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

  • Xianglong Wei

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

  • Peng Wang

    (Taizhou Water Conservancy Bureau, Taizhou 225306, China)

Abstract

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

Suggested Citation

  • Mingcheng Zhu & Shouqian Li & Xianglong Wei & Peng Wang, 2021. "Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods," Sustainability, MDPI, vol. 13(7), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3744-:d:525275
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    Citations

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    Cited by:

    1. Liyang Wang & Taifeng Li & Pengcheng Wang & Zhenyu Liu & Qianli Zhang, 2023. "BiLSTM for Predicting Post-Construction Subsoil Settlement under Embankment: Advancing Sustainable Infrastructure," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    2. Zheng Jiang & Shuohua Zhang & Wei Li, 2022. "Exploration of Urban Emission Mitigation Pathway under the Carbon Neutrality Target: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    3. Shaniel Chotkan & Raymond van der Meij & Wouter Jan Klerk & Phil J. Vardon & Juan Pablo Aguilar-López, 2022. "A Data-Driven Method for Identifying Drought-Induced Crack-Prone Levees Based on Decision Trees," Sustainability, MDPI, vol. 14(11), pages 1-23, June.

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