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Slope Stability Analysis Using Rf, Gbm, Cart, Bt and Xgboost

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Abstract

Slopes in geotechnical and mining engineering are the most crucial geo-structure. Predicting or forecasting the stability or instability of the slope and then classifying the slope accordingly helps in mitigating the risks and enhancing the design by maximizing the safety. Computing techniques have overpowered the analytical and statistical models used for predicting the stability of the slopes. To reduce the uncertainties and ambiguity of the previously used models, lately, researchers have come up with the novel techniques for Slope Stability Classification (SSC) which are Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, Boosted Trees and Classification and Regression Trees. These computational algorithms are employed in this research paper and the slope details are taken from a literature i.e. 221 input datasets are used and slopes are classified accordingly using the mentioned models. The relation between the inputs such as height (H), slope angle (β), cohesion (c), pore water pressure ratio (ru), unit weight (γ), angle of internal friction (φ) and slope stability (output) is established and slopes are categorized according to their failure and stability. Performance analysis is done thereafter to analyses and compare different models and let the readers and researchers know that which model sufficed and fitted best to the study.

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Data Availability

Data has been used from a literature work by Zhou et al. in 2019.

References

  • Breiman L (1996) Bagging predictors. Machine Learn 24(2):123–140

    Google Scholar 

  • Breiman L (2001) Random forests. Machine Learn 45(1):5–32

    Article  Google Scholar 

  • Breiman L, Friedman GH, Olshen RA (2017) Classification and regression trees Routledge

  • Breiman L, Friedman F, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont

    Google Scholar 

  • Chen T, Guestrin C (2016t) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794).

  • De’Ath G (1999) New statistical methods for modeling species-environment relationships, Ph.D. thesis, James Cook University, Townsville, Australia.

  • Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learn 40(2):139–157

    Article  Google Scholar 

  • Fan J, Upadhye S, Worster A (2006) Understanding receiver operating characteristic (ROC) curves. Canadian J Emerg Med 8(1):19–20

    Article  Google Scholar 

  • Ferentinou MD, Sakellariou MG (2007) Computational intelligence tools for the prediction of slope performance. Comput Geotech 34(5):362–384

    Article  Google Scholar 

  • Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285

    Article  Google Scholar 

  • Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. icml 96:148–156

    Google Scholar 

  • Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics 1189–1232.

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    Article  Google Scholar 

  • Friedman N, Getoor L, Koller D, Pfeffer A (1999a) Learning probabilistic relational models. Int Jt Conf Artif Intell 99:1300–1309

    Google Scholar 

  • Friedman N, Goldszmidt M, Wyner A (1999b) Data analysis with Bayesian networks: A bootstrap approach. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc 196–205.

  • Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Annal Stat 28(2):337–407

    Article  Google Scholar 

  • Grandvalet Y (2004) Bagging equalizes influence. Machine Learn 55(3):251–270

    Article  Google Scholar 

  • Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorpholog 259:105–118

    Article  Google Scholar 

  • Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A (2011) Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 4(1):299

    Article  Google Scholar 

  • Massart DL, Vandeginste BG, Buydens LMC, De Jong S, Lewi PJ, Smeyers-Verbeke J, Mann CK (1998) Handbook of chemometrics and qualimetrics: Part A. Appl Spectrosc 52:302A

    Article  Google Scholar 

  • Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemom: A J Chemometrics Soc 18(6):275–285

    Article  Google Scholar 

  • Schapire RE (1990) The strength of weak learnability. Machine Learn 5(2):197–227

    Google Scholar 

  • Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14(4):323

    Article  Google Scholar 

  • Terzaghi K (1950) Mechanism of landslides. Application of geology to engineering practice, Geol. Soc. Am 83–123.

  • Wang HB, Xu WY, Xu RC (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80(3–4):302–315

    Article  Google Scholar 

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong. China Geomorphol 101(4):572–582

    Article  Google Scholar 

  • Zhang W, Wu C, Li Y, Wang L, Samui P (2019) Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 1–14.

  • Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Patna, India

    Jayanti Prabha Bharti, Pratishtha Mishra & Pijush Samui

  2. School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India

    Usha moorthy

  3. Department of Information and Communication Engineering, Sunchon National University, Suncheon, Republic of Korea

    Yongyun Cho

  4. Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamilnadu, India

    V. E. Sathishkumar

Authors
  1. Jayanti Prabha Bharti
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  2. Pratishtha Mishra
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  3. Usha moorthy
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  4. V. E. Sathishkumar
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  5. Yongyun Cho
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  6. Pijush Samui
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Correspondence to Pratishtha Mishra.

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Conflict of interest

This manuscript deals with AI in Geotechnical structure classification and opens a tunnel for enhancement and improvement of the accuracy of implicit calculations involved in Civil Engineering. Software used: all the models used in this study were developed in R Studio.

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Bharti, J.P., Mishra, P., moorthy, U. et al. Slope Stability Analysis Using Rf, Gbm, Cart, Bt and Xgboost. Geotech Geol Eng 39, 3741–3752 (2021). https://doi.org/10.1007/s10706-021-01721-2

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  • DOI: https://doi.org/10.1007/s10706-021-01721-2

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