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Prediction of slope instability in open-pit mine waste dumps based on GA-BP neural network
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Zunxian XIE1, 2, Haohao MA1, Song JIANG1, Xiaoyun WU1
China Safety Science Journal | 2026, 36(3) : 81 - 88
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China Safety Science Journal | 2026, 36(3): 81-88
• Safety Technology and Engineering •
Prediction of slope instability in open-pit mine waste dumps based on GA-BP neural network
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Zunxian XIE1, 2, Haohao MA1, Song JIANG1, Xiaoyun WU1
Affiliations
  • 1School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
  • 2Institute of Higher Education, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
Published: 2026-03-28 doi: 10.16265/j.cnki.issn1003-3033.2026.03.0427
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To improve the prediction accuracy and reliability of slope instability in mine waste dumps, a hybrid GA-BP model was developed by integrating an improved GA with a BP neural network. The model employed GA to globally optimize the initial weights and thresholds of the BP network, and incorporated the Levenberg-Marquardt (LM) algorithm to enhance convergence speed. Ten key parameters—including bench slope angle, geotechnical internal stress, bench height, surface displacement, and pore water pressure—were selected as inputs, with the slope safety factor as the output. Training and validation was performed using 150 field case datasets. The results show that GA-BP model reduces the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 46.9%, 25.4%, and 5.38%, respectively, compared to the conventional BP model. Predictions are closer to the safety threshold (Fs = 1.2), indicating enhanced sensitivity and stability. Pearson correlation analysis confirms strong relationships between surface and internal displacement (0.98) and between pore water pressure and rainfall (0.75), supporting the rationality of the input indicators. The study demonstrates that GA-BP model effectively overcomes local optima and gradient vanishing issues in BP networks, providing a reliable tool for intelligent slope stability assessment.

genetic algorithm (GA)  /  backpropagation (BP) neural network  /  open-pit mine  /  waste dumps  /  slope instability prediction  /  safety factor
Zunxian XIE, Haohao MA, Song JIANG, Xiaoyun WU. Prediction of slope instability in open-pit mine waste dumps based on GA-BP neural network[J]. China Safety Science Journal, 2026 , 36 (3) : 81 -88 . DOI: 10.16265/j.cnki.issn1003-3033.2026.03.0427
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Year 2026 volume 36 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.03.0427
  • Receive Date：2025-09-10
  • Online Date：2026-07-08
  • Published：2026-03-28
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  • Received：2025-09-10
  • Revised：2025-12-05
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Affiliations
    1School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
    2Institute of Higher Education, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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