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Foundation Settlement Prediction Based on a Novel NGM Model

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  • Peng-Yu Chen
  • Hong-Ming Yu

Abstract

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

Suggested Citation

  • Peng-Yu Chen & Hong-Ming Yu, 2014. "Foundation Settlement Prediction Based on a Novel NGM Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, March.
    • Handle: RePEc:hin:jnlmpe:242809
    DOI: 10.1155/2014/242809
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    1. Wang, Yong & Yang, Zhongsen & Wang, Li & Ma, Xin & Wu, Wenqing & Ye, Lingling & Zhou, Ying & Luo, Yongxian, 2022. "Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model," Energy, Elsevier, vol. 259(C).
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    3. Luo, Xilin & Duan, Huiming & Xu, Kai, 2021. "A novel grey model based on traditional Richards model and its application in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Wenqing Wu & Xin Ma & Bo Zeng & Yuanyuan Zhang & Wanpeng Li, 2021. "Forecasting short-term solar energy generation in Asia Pacific using a nonlinear grey Bernoulli model with time power term," Energy & Environment, , vol. 32(5), pages 759-783, August.
    5. Huiping Wang & Yi Wang, 2022. "Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    6. 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.
    7. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).

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