Slope safety, stability evaluation, and protective measures based on machine learning

WU Meng-ting; CHEN Qiu-song; QI Chong-chong

doi:10.13374/j.issn2095-9389.2021.06.02.008

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Chinese Journal of Engineering > 2022 > 44(2): 180-188

WU Meng-ting, CHEN Qiu-song, QI Chong-chong. Slope safety, stability evaluation, and protective measures based on machine learning[J]. Chinese Journal of Engineering, 2022, 44(2): 180-188. doi: 10.13374/j.issn2095-9389.2021.06.02.008

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WU Meng-ting, CHEN Qiu-song, QI Chong-chong. Slope safety, stability evaluation, and protective measures based on machine learning[J]. Chinese Journal of Engineering, 2022, 44(2): 180-188. doi: 10.13374/j.issn2095-9389.2021.06.02.008

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Slope safety, stability evaluation, and protective measures based on machine learning

doi: 10.13374/j.issn2095-9389.2021.06.02.008

School of Resources and Safety Engineering, Central South University, Changsha 410083, China

More Information

Corresponding author: E-mail: chongchong.qi@csu.edu.cn
Received Date: 2021-06-02
Available Online: 2021-08-12
Publish Date: 2022-02-15

Abstract

Abstract

In recent years, the slope instability has brought immeasurable costs to production and life of human. As a result, it is essential to correctly understand, analyze, and design the slope reasonably, and implement appropriate protective measures to minimize the loss and harm caused by its instability. By far, slope stability can be investigated using theoretical analysis, numerical modeling and machine learning prediction, among them machine learning prediction has been the most encouraging one. Many studies have been performed using machine learning algorithms to predict the slope stability. However, these methods suffers from poor accuracy and poor generalisation capbility, so its real-life application has been limited. In the current study, a machine learning-based slope safety and stability evaluation system is established by integrating principal component analysis, parameter adjustment, and influence factor weight analysis. It is shown that PCA can reduce the dimensions of the input variables from six to three while retaining 80% of the information; however, at the cost of the model’s effectiveness. The random forest and XGBoost (eXtreme Gradient Boosting) learning algorithms can both be employed to develop effective evaluation models for slope safety and stability. The comparative analysis of algorithms’ prediction effects established XGBoost as the best evaluation model, which can achieve the average accuracy of 92%, precision of 91%, recall of 96%, and the area under the receiver operating characteristic curve (AUC) of 0.95. In addition, this study employs three types of test methods: the chi-square test, F test correlation, and mutual information method, meanwhile by calculating and visualizing the importance of influencing factors, the influence of unit weight, slope height, internal friction angle and cohesion on slope stability is demonstrated. It has been shown that the unit weight is the most influencing factor for the slope stability. Finally, the slope safety protection measures are proposed by combining the evaluation results with the actual project.
- slope stability evaluation,
- machine learning,
- random forest,
- XGBoost,
- protective measures

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References(29)

References

[1]	Thai Pham B, Tien Bui D, Prakash I. Landslide susceptibility modelling using different advanced decision trees methods. Civ Eng Environ Syst, 2018, 35(1-4): 139 doi: 10.1080/10286608.2019.1568418
[2]	Balogun A L, Rezaie F, Pham Q B, et al. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geosci Front, 2021, 12(3): 101104 doi: 10.1016/j.gsf.2020.10.009
[3]	Peethambaran B, Anbalagan R, Kanungo D P, et al. A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas. CATENA, 2020, 195: 104751 doi: 10.1016/j.catena.2020.104751
[4]	Samui P. Slope stability analysis: A support vector machine approach. Environ Geol, 2008, 56(2): 255 doi: 10.1007/s00254-007-1161-4
[5]	蔣水華, 劉源, 章浩龍, 等. 先驗概率分布及似然函數模型的選擇對邊坡可靠度評價影響的定量評估. 巖土力學, 2020, 41(9):3087 Jiang S H, Liu Y, Zhang H L, et al. Quantitatively evaluating the effects of prior probability distribution and likelihood function models on slope reliability assessment. Rock Soil Mech, 2020, 41(9): 3087
[6]	Neuland H. A prediction model of landslips. CATENA, 1976, 3(2): 215 doi: 10.1016/0341-8162(76)90011-4
[7]	Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Software, 2010, 25(6): 747 doi: 10.1016/j.envsoft.2009.10.016
[8]	Alavi A H, Gandomi A H. A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput, 2011, 28(3): 242 doi: 10.1108/02644401111118132
[9]	Martins F F, Miranda T F S. Application of data mining techniques to the safety evaluation of slopes // Information Technology in Geo-Engineering: Proceedings of the 1st International Conference (ICITG). Shanghai, 2010: 84
[10]	趙洪波. 基于支持向量機的邊坡可靠性分析. 巖土工程學報, 2007, 29(6):819 doi: 10.3321/j.issn:1000-4548.2007.06.005 Zhao H B. Reliability analysis of slope based on support vector machine. Chin J Geotech Eng, 2007, 29(6): 819 doi: 10.3321/j.issn:1000-4548.2007.06.005
[11]	陳善攀. 土質邊坡穩定可靠度分析遺傳算法方法及程序設計[學位論文]. 長沙: 中南大學, 2008 Chen S P. Genetic Algorithm and Program Design for Reliability Analysis of Soil Slope Stability [Dissertation]. Changsha: Central South University, 2008
[12]	茍倩倩, 趙明生, 池恩安, 等. 基于PCA-BP神經網絡在爆破振動評價要素中的預測及應用. 礦業研究與開發, 2018, 38(12):97 Gou Q Q, Zhao M S, Chi E A, et al. Prediction and application of evaluation factors in blasting vibration based on PCA-BP neural network. Min Res Dev, 2018, 38(12): 97
[13]	林海明, 杜子芳. 主成分分析綜合評價應該注意的問題. 統計研究, 2013, 30(8):25 doi: 10.3969/j.issn.1002-4565.2013.08.004 Lin H M, Du Z F. Some problems in comprehensive evaluation in the principal component analysis. Stat Res, 2013, 30(8): 25 doi: 10.3969/j.issn.1002-4565.2013.08.004
[14]	陳高峰, 盧應發, 程圣國. 邊坡穩定性影響因素主成分分析. 金屬礦山, 2008(4):123 doi: 10.3321/j.issn:1001-1250.2008.04.034 Chen G F, Lu Y F, Cheng S G. Principal component analysis of influence factors of slope stability. Met Mine, 2008(4): 123 doi: 10.3321/j.issn:1001-1250.2008.04.034
[15]	毛宇昆. 基于機器學習的滑坡穩定性評價及系統研發[學位論文]. 成都: 電子科技大學, 2020 Mao Y K. Stability Evaluation of Landslide Based on Machine Learning and System Development [Dissertation]. Chengdu: University of Electronic Science and Technology of China, 2020
[16]	Qi C C, Tang X L. Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study. Comput Ind Eng, 2018, 118: 112 doi: 10.1016/j.cie.2018.02.028
[17]	張永峰, 陸志強. 基于集成神經網絡的剩余壽命預測. 工程科學學報, 2020, 42(10):1372 Zhang Y F, Lu Z Q. Remaining useful life prediction based on an integrated neural network. Chin J Eng, 2020, 42(10): 1372
[18]	叢明, 吳童, 劉冬, 等. 基于監督學習的前列腺MR/TRUS圖像分割和配準方法. 工程科學學報, 2020, 42(10):1362 Cong M, Wu T, Liu D, et al. Prostate MR/TRUS image segmentation and registration methods based on supervised learning. Chin J Eng, 2020, 42(10): 1362
[19]	Li J H, Wang F. Study on the forecasting models of slope stability under data mining // Proceedings of the Workshop on Biennial International Conference on Engineering. Honolulu, 2010: 765
[20]	Lu P, Rosenbaum M S. Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards, 2003, 30(3): 383 doi: 10.1023/B:NHAZ.0000007168.00673.27
[21]	Sah N K, Sheorey P R, Upadhyaya L N. Maximum likelihood estimation of slope stability. Int J Rock Mech Min Sci Geomech Abstr, 1994, 31(1): 47 doi: 10.1016/0148-9062(94)92314-0
[22]	Yan X M, Li X B. Bayes discriminant analysis method for predicting the stability of open pit slope // 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). Lushan, 2011: 147
[23]	Zhou K P, Chen Z Q. Stability prediction of tailing dam slope based on neural network pattern recognition // 2009 Second International Conference on Environmental and Computer Science. Dubai, 2009: 380
[24]	樊奇. 基于機器學習全流程的高速公路巖質邊坡穩定性快速評價方法研究[學位論文]. 成都: 成都理工大學, 2019 Fan Q. Rapid Evaluation Method Research of Rock Slope Stability for Highway Based on Machine Learning Process [Dissertation]. Chengdu: Chengdu University of Technology, 2019
[25]	武森, 劉露, 盧丹. 基于聚類欠采樣的集成不均衡數據分類算法. 工程科學學報, 2017, 39(8):1244 Wu S, Liu L, Lu D. Imbalanced data ensemble classification based on cluster-based under-sampling algorithm. Chin J Eng, 2017, 39(8): 1244
[26]	羅世林, 萬文, 唐勁舟, 等. 影響邊坡穩定性因素數值研究. 礦業工程研究, 2016, 31(4):37 Luo S L, Wan W, Tang J Z, et al. Numerical study of factors influencing slope stability. Miner Eng Res, 2016, 31(4): 37
[27]	肖東升. 植被增加邊坡抗剪強度的量化理論. 四川建筑, 2004, 24(1):63 doi: 10.3969/j.issn.1007-8983.2004.01.030 Xiao D S. Quantitative theory of slope shear strength increased by vegetation. Sichuan Archit, 2004, 24(1): 63 doi: 10.3969/j.issn.1007-8983.2004.01.030
[28]	許萬忠, 彭振斌, 袁海平. 節理裂隙邊坡錨注加固機制及特性研究. 水文地質工程地質, 2006, 33(5):30 doi: 10.3969/j.issn.1000-3665.2006.05.007 Xu W Z, Peng Z B, Yuan H P. Characteristic analysis of bolting grouting reinforcement in jointed and fractured slope. Hydrogeol Eng Geol, 2006, 33(5): 30 doi: 10.3969/j.issn.1000-3665.2006.05.007
[29]	鄭開歡, 羅周全, 羅成彥, 等. 持續暴雨作用下排土場層狀碎石土邊坡穩定性. 工程科學學報, 2016, 38(9):1204 Zheng K H, Luo Z Q, Luo C Y, et al. Layered gravel soil slope stability of a waste dump considering long-term hard rain. Chin J Eng, 2016, 38(9): 1204