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A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm

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Abstract

In landslide susceptibility mapping or evaluating slope stability, the shear strength parameters of rocks and soils and their effectiveness are undeniable. However, they have not been studied for all-natural materials, as well as different locations. Therefore, this paper proposes a novel generalized artificial intelligence model for estimating the friction angle of clays from different areas/locations for evaluating slope stability or landslide susceptibility mapping, including the datasets from the UK, New Zealand, Indonesia, Venezuela, USA, Japan, and Italy. The robustness and consistency of the model’s prediction were checked by testing with various datasets having different geological and geomorphological setups. Accordingly, 162 observations from different areas/locations were collected from the locations and regions above for this aim. Subsequently, deep learning techniques were applied to develop the multiple layer perceptron (MLP) neural network model (i.e., DMLP model) with the goal of error reduction of the MLP model. Next, Harris Hawks optimization (HHO) algorithm was applied to boost the optimization of the DMLP model for predicting friction angle of clays aiming to get a better accuracy than those of the DMLP model, called HHO–DMLP model. A DMLP neural network without optimization of the HHO algorithm and two other conventional models (i.e., SVM and RF) were also employed to compare with the proposed HHO–DMLP model. The results showed that the proposed HHO–DMLP model predicted the friction angle of clays better than those of the other models. It can reflect the friction angle of clays with acceptable accuracy from different locations and regions (i.e., MSE = 12.042; RMSE = 3.470; R2 = 0.796; MAPE = 0.182; and VAF = 78.806). The DMLP model without optimization of the HHO algorithm provided slightly lower accuracy (i.e., MSE = 15.151; RMSE = 3.892; R2 = 0.738; MAPE = 0.202; and VAF = 73.431). Besides, two other conventional models (i.e., SVM and RF) provided low reliability, especially over-fitting happened with the RF model, and it was not recommended to be used to predict the friction angle of clays (i.e., RMSE = 6.325 and R2 = 0.377 on the training dataset, but RMSE = 1.669 and R2 = 0.961 on the testing dataset).

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Authors and Affiliations

  1. Department of Economics and Management, Changsha University, Changsha, China

    Hong Zhang

  2. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Vietnam

    Hoang Nguyen & Xuan-Nam Bui

  3. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Vietnam

    Xuan-Nam Bui

  4. The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology, Sydney, NSW, 2007, Australia

    Biswajeet Pradhan

  5. Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjingu, Seoul, 05006, South Korea

    Biswajeet Pradhan

  6. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121, Athens, Greece

    Panagiotis G. Asteris

  7. Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest, Romania

    Romulus Costache

  8. National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania

    Romulus Costache

  9. Melbourne School of Engineering, University of Melbourne, Victoria, Australia

    Jagannath Aryal

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Zhang, H., Nguyen, H., Bui, XN. et al. A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm. Engineering with Computers 38 (Suppl 5), 3901–3914 (2022). https://doi.org/10.1007/s00366-020-01272-9

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