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Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

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  • Thuy-Anh Nguyen
  • Hai-Bang Ly
  • Binh Thai Pham

Abstract

In the design process of foundations, pavements, retaining walls, and other geotechnical matters, estimation of soil strength-related parameters is crucial. In particular, the friction angle is a critical shear strength factor in assessing the stability and deformation of geotechnical structures. Practically, laboratory or field tests have been conducted to determine the friction angle of soil. However, these jobs are often time-consuming and quite expensive. Therefore, the prediction of geo-mechanical properties of soils using machine learning techniques has been widely applied in recent times. In this study, the Bayesian regularization backpropagation algorithm is built to predict the internal friction angle of the soil based on 145 data collected from experiments. The performance of the model is evaluated by three specific statistical criteria, such as the Pearson correlation coefficient ( R ), root mean square error (RMSE), and mean absolute error (MAE). The results show that the proposed algorithm performed well for the prediction of the friction angle of soil ( R = 0.8885, RMSE = 0.0442, and MAE = 0.0328). Therefore, it can be concluded that the backpropagation neural network-based machine learning model is a reasonably accurate and useful prediction tool for engineers in the predesign phase.

Suggested Citation

  • Thuy-Anh Nguyen & Hai-Bang Ly & Binh Thai Pham, 2020. "Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, December.
  • Handle: RePEc:hin:jnlmpe:8845768
    DOI: 10.1155/2020/8845768
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    Cited by:

    1. Po-Wen Hwang & Jia-Heng Wu & Yuan-Jen Chang, 2024. "Optimization Based on Computational Fluid Dynamics and Machine Learning for the Performance of Diffuser-Augmented Wind Turbines with Inlet Shrouds," Sustainability, MDPI, vol. 16(9), pages 1-31, April.
    2. Van Quan Tran & Hai-Van Thi Mai & Thuy-Anh Nguyen & Hai-Bang Ly, 2021. "Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-21, December.
    3. Maria Kofidou & Michael de Courcy Williams & Andreas Nearchou & Stavroula Veletza & Alexandra Gemitzi & Ioannis Karakasiliotis, 2021. "Applying Remotely Sensed Environmental Information to Model Mosquito Populations," Sustainability, MDPI, vol. 13(14), pages 1-17, July.

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