Stability risk assessment of slopes using logistic model tree based on updated case histories

Feezan Ahmad; Xiao-Wei Tang; Mahmood Ahmad; Roberto Alonso González-Lezcano; Ali Majdi; Mohamed Moafak Arbili; Feezan Ahmad; Xiao-Wei Tang; Mahmood Ahmad; Roberto Alonso González-Lezcano; Ali Majdi; Mohamed Moafak Arbili

doi:10.3934/mbe.2023939

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21229-21245. doi: 10.3934/mbe.2023939

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Stability risk assessment of slopes using logistic model tree based on updated case histories

1.
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2.
Institute of Energy Infrastructure, University Tenaga Nasional, Kajang 43000, Malaysia
3.
Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
4.
Department of Architecture and Design, Escuela Politécnica Superior, Universidad San Pablo-CEU (Montepríncipe Campus), CEU Universities, Madrid 28668, Spain
5.
Department of Building and Construction Techniques Engineering, Al-Mustaqbal University, Hilla 51001, Iraq
6.
Department of Technical Civil Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil 44001, Iraq

Academic Editor: Jorge Bernardino

Received: 16 August 2023 Revised: 10 October 2023 Accepted: 13 October 2023 Published: 29 November 2023

A new logistic model tree (LMT) model is developed to predict slope stability status based on an updated database including 627 slope stability cases with input parameters of unit weight, cohesion, angle of internal friction, slope angle, slope height and pore pressure ratio. The performance of the LMT model was assessed using statistical metrics, including accuracy (Acc), Matthews correlation coefficient (Mcc), area under the receiver operating characteristic curve (AUC) and F-score. The analysis of the Acc together with Mcc, AUC and F-score values for the slope stability suggests that the proposed LMT achieved better prediction results (Acc = 85.6%, Mcc = 0.713, AUC = 0.907, F-score for stable state = 0.967 and F-score for failed state = 0.923) as compared to other methods previously employed in the literature. Two case studies with ten slope stability events were used to verify the proposed LMT. It was found that the prediction results are completely consistent with the actual situation at the site. Finally, risk analysis was carried out, and the result also agrees with the actual conditions. Such probability results can be incorporated into risk analysis with the corresponding failure cost assessment later.
- logistic model tree,
- machine learning,
- slope stability,
- risk analysis,
- performance metrics
Citation: Feezan Ahmad, Xiao-Wei Tang, Mahmood Ahmad, Roberto Alonso González-Lezcano, Ali Majdi, Mohamed Moafak Arbili. Stability risk assessment of slopes using logistic model tree based on updated case histories[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21229-21245. doi: 10.3934/mbe.2023939

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Abstract

A new logistic model tree (LMT) model is developed to predict slope stability status based on an updated database including 627 slope stability cases with input parameters of unit weight, cohesion, angle of internal friction, slope angle, slope height and pore pressure ratio. The performance of the LMT model was assessed using statistical metrics, including accuracy (Acc), Matthews correlation coefficient (Mcc), area under the receiver operating characteristic curve (AUC) and F-score. The analysis of the Acc together with Mcc, AUC and F-score values for the slope stability suggests that the proposed LMT achieved better prediction results (Acc = 85.6%, Mcc = 0.713, AUC = 0.907, F-score for stable state = 0.967 and F-score for failed state = 0.923) as compared to other methods previously employed in the literature. Two case studies with ten slope stability events were used to verify the proposed LMT. It was found that the prediction results are completely consistent with the actual situation at the site. Finally, risk analysis was carried out, and the result also agrees with the actual conditions. Such probability results can be incorporated into risk analysis with the corresponding failure cost assessment later.

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