A novel artificial intelligence technique for analyzing slope stability using PSO-CA model

This study aims to develop a new artificial intelligence model for analyzing and evaluating slope stability in open-pit mines. Indeed, a novel hybrid intelligent technique based on an optimization of the cubist algorithm by an evolutionary method (i.e., PSO), namely PSO-CA technique, was developed for predicting the factor of safety (FS) in slope stability; 450 simulations from the Geostudio software for the FS of a quarry mine (Vietnam) were used as the datasets for this aim. Five factors include bench height, slope angle, angle of internal friction, cohesion, and unit weight were used as the input variables for estimating FS in this work. To clarify the performance of the proposed PSO-CA technique in slope stability analysis, SVM, CART, and kNN models were also developed and assessed. Three performance indices, such as mean absolute error (MAE), root-mean-squared error (RMSE), and determination coefficient ( R 2 ), were computed to evaluate the accuracy of the predictive models. The results clarified that the proposed PSO-CA technique was the most dominant accuracy with an MAE of 0.009, RMSE of 0.025, and R 2 of 0.981, in estimating the stability of slope. The remaining models (i.e., SVM, CART, kNN) obtained poorer performance with MAE from 0.014 to 0.038, RMSE 0.030–0.056, and R 2 0.917–0.974.

[1]  Y. Ozcelik,et al.  Prediction of mono-wire cutting machine performance parameters using artificial neural network and regression models , 2018 .

[2]  P. I. Lewin,et al.  UNDISTURBED SAMPLES OF LONDON CLAY FROM THE ASHFORD COMMON SHAFT' STRENGTH-EFFECTIVE STRESS RELATIONSHIPS , 1965 .

[3]  Hoang Nguyen,et al.  Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques , 2019, Natural Resources Research.

[4]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[5]  Hoang Nguyen,et al.  Novel metaheuristic classification approach in developing mathematical model-based solutions predicting failure in shallow footing , 2019, Engineering with Computers.

[6]  S. Sarma.  STABILITY ANALYSIS OF EMBANKMENTS AND SLOPES , 1973 .

[7]  B. Satyanarayana,et al.  A Method of Analysis of the Stability of Embankments Assuming Parallel Inter-Slice Forces , 1967 .

[8]  X. Bui,et al.  Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study , 2019, Acta Geophysica.

[9]  P. Zeng,et al.  Prediction of Slope Stability using Naive Bayes Classifier , 2018 .

[10]  Akbar A. Javadi,et al.  A new approach to modeling the behavior of frozen soils , 2018, Engineering Geology.

[11]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[12]  Yu WangY. Wang,et al.  Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet , 2011 .

[13]  Xiaolin Tang,et al.  Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study , 2018, Comput. Ind. Eng..

[14]  Nhat-Duc Hoang,et al.  Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis , 2016, Expert Syst. Appl..

[15]  Hoang Nguyen,et al.  Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system , 2019, Engineering with Computers.

[16]  P. Lu,et al.  Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability , 2003 .

[17]  Hoang Nguyen,et al.  A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms , 2019, Appl. Soft Comput..

[18]  Wei Gao,et al.  Partial multi-dividing ontology learning algorithm , 2018, Inf. Sci..

[19]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .

[20]  Rafael Baker,et al.  A relation between safety factors with respect to strength and height of slopes , 2006 .

[21]  A. Bishop.  The use of the Slip Circle in the Stability Analysis of Slopes , 1955 .

[22]  Haoran Xie,et al.  Top K representative: a method to select representative samples based on K nearest neighbors , 2019, Int. J. Mach. Learn. Cybern..

[23]  Wei Gao,et al.  Nano properties analysis via fourth multiplicative ABC indicator calculating , 2017, Arabian Journal of Chemistry.

[24]  Jie Dou,et al.  Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM , 2019, Remote. Sens..

[25]  D. C. Tobutt.  Monte Carlo Simulation methods for slope stability , 1982 .

[26]  Danial Jahed Armaghani,et al.  Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions , 2019, Soft Comput..

[27]  J. Chacón,et al.  Spatial stability of slope cuts in rock massifs using GIS technology and probabilistic analysis , 2012, Bulletin of Engineering Geology and the Environment.

[28]  D. Cruden,et al.  Probabilistic slope stability analysis for practice , 2002 .

[29]  Sarat Kumar Das,et al.  Slope stability analysis using artificial intelligence techniques , 2016, Natural Hazards.

[30]  Muhammad Kamran Siddiqui,et al.  Study of biological networks using graph theory , 2017, Saudi journal of biological sciences.

[31]  Pijush Samui,et al.  Utilization of a least square support vector machine (LSSVM) for slope stability analysis , 2011 .

[32]  Shahryar Rahnamayan,et al.  Majority voting for discrete population-based optimization algorithms , 2018, Soft Computing.

[33]  B. Pham,et al.  Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. , 2019, The Science of the total environment.

[34]  N. Morgenstern,et al.  The analysis of the stability of general slip surfaces , 1965 .

[35]  Danial Jahed Armaghani,et al.  Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil , 2018, Engineering with Computers.

[36]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[37]  Mohamed Cheriet,et al.  Support Vector Machine , 2009, Encyclopedia of Biometrics.

[38]  Hossein Moayedi,et al.  Artificial intelligence design charts for predicting friction capacity of driven pile in clay , 2018, Neural Computing and Applications.

[39]  Mehdi Mousavi,et al.  Slope stability analysis using evolutionary optimization techniques , 2017 .

[40]  Hossein Moayedi,et al.  Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods , 2018, Appl. Soft Comput..

[41]  Loke Kok Foong,et al.  Optimizing ANN models with PSO for predicting short building seismic response , 2019, Engineering with Computers.

[42]  Massimo Buscema,et al.  The contribution of Artificial Adaptive System to limit the influence of systematic errors in the definition of the kinematic behavior of an extremely-slow landslide , 2016 .

[43]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[44]  Manoj K. Arora,et al.  A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas , 2006 .

[45]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[46]  Yusuf Erzin,et al.  The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions , 2013, Comput. Geosci..

[47]  Hoang Nguyen,et al.  Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system , 2019, Engineering with Computers.

[48]  Wei Gao,et al.  An independent set degree condition for fractional critical deleted graphs , 2019, Discrete & Continuous Dynamical Systems - S.

[49]  Michael Sakellariou,et al.  A study of slope stability prediction using neural networks , 2005 .

[50]  H. Moayedi,et al.  Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile , 2018, International Journal of Geomechanics.

[51]  Anthony T. C. Goh,et al.  Performance based support design for horseshoe-shaped rock caverns using 2D numerical analysis , 2018, Engineering Geology.

[52]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[53]  Wei Gao,et al.  A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes , 2019, Engineering with Computers.

[54]  A Kamal Prakash,et al.  Artificial Bee Colony (ABC) algorithm , 2013 .

[55]  Carsten Drebenstedt,et al.  Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network , 2019, Natural Resources Research.

[56]  Jie Dou,et al.  Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm , 2015, Remote. Sens..

[57]  Leslie George Tham,et al.  Numerical modelling of soil nails in loose fill slope under surcharge loading , 2009 .

[58]  Sarath Das,et al.  Classification of slopes and prediction of factor of safety using differential evolution neural networks , 2011 .

[59]  X. Bui,et al.  Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest , 2018, Natural Resources Research.

[60]  Xuan-Nam Bui,et al.  Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost , 2019, Natural Resources Research.

[61]  H. Pourghasemi,et al.  An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan , 2015, Natural Hazards.

[62]  Hoang Nguyen,et al.  A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam , 2018, Neural Computing and Applications.

[63]  Maria Ferentinou,et al.  Integrating Rock Engineering Systems device and Artificial Neural Networks to predict stability conditions in an open pit , 2018, Engineering Geology.