Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms

Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.

[1]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[2]  Mahdi Hasanipanah,et al.  Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting , 2016, Neural Computing and Applications.

[3]  Helmut Schweiger,et al.  Slope stability analysis by means of finite element limit analysis and finite element strength reduction techniques. Part II: Back analyses of a case history , 2015 .

[4]  Andrei V. Lyamin,et al.  Three-dimensional slope stability assessment of two-layered undrained clay , 2015 .

[5]  Mahdi Hasanipanah,et al.  Application of PSO to develop a powerful equation for prediction of flyrock due to blasting , 2017, Neural Computing and Applications.

[6]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[7]  Jian Zhou,et al.  Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining , 2016 .

[8]  Ebrahim Fathi Salmi,et al.  Slope stability assessment using both empirical and numerical methods: a case study , 2015, Bulletin of Engineering Geology and the Environment.

[9]  Mahdi Hasanipanah,et al.  Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak , 2020, Engineering with Computers.

[10]  P. G. Asteris,et al.  Application of artificial neural networks for the predictionof the compressive strength of cement-based mortars , 2019 .

[11]  Pizhong Qiao,et al.  Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems , 2008, Neural Computing and Applications.

[12]  K. G. Sharma,et al.  Numerical modelling of seismic slope failure using MPM , 2016 .

[13]  Jian-Hua Yin,et al.  Upper bound limit analysis of slope stability using rigid finite elements and nonlinear programming , 2003 .

[14]  Guichen Li,et al.  Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes , 2019, International Journal for Numerical and Analytical Methods in Geomechanics.

[15]  Mahdi Hasanipanah,et al.  Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure , 2020, Engineering with Computers.

[16]  Debi Prasanna Kanungo,et al.  Effect of the combination characteristics of rock structural plane on the stability of a rock-mass slope , 2014, Bulletin of Engineering Geology and the Environment.

[17]  Danial Jahed Armaghani,et al.  Intelligent design of retaining wall structures under dynamic conditions , 2019 .

[18]  P. S. Lin,et al.  Investigation on the failure of a building constructed on a hillslope , 1990 .

[19]  Mahdi Hasanipanah,et al.  Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting , 2019, Engineering with Computers.

[20]  Panagiotis G. Asteris,et al.  A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength , 2020, Neural Computing and Applications.

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

[22]  Jiancong Xu.  Debris slope stability analysis using three-dimensional finite element method based on maximum shear stress theory , 2011 .

[23]  Qiang Xu,et al.  Application of the SSPC method in the stability assessment of highway rock slopes in the Yunnan province of China , 2016, Bulletin of Engineering Geology and the Environment.

[24]  Hadi Fattahi,et al.  Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems , 2017, Environmental Earth Sciences.

[25]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[26]  Stephen J. Martel,et al.  A Two-dimensional Boundary Element Method for Calculating Elastic Gravitational Stresses in Slopes , 2000 .

[27]  Tianfeng Gu,et al.  GIS and limit equilibrium in the assessment of regional slope stability and mapping of landslide susceptibility , 2015, Bulletin of Engineering Geology and the Environment.

[28]  Liborio Cavaleri,et al.  Mapping and holistic design of natural hydraulic lime mortars , 2020 .

[29]  Amin Barari,et al.  Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran) , 2009 .

[30]  Debiao Meng,et al.  Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength , 2020, Neural Computing and Applications.

[31]  Hani S. Mitri,et al.  Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction , 2015, Natural Hazards.

[32]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[33]  Mahdi Hasanipanah,et al.  Developing a least squares support vector machine for estimating the blast-induced flyrock , 2017, Engineering with Computers.

[34]  Hoang Nguyen,et al.  Optimizing Levenberg–Marquardt backpropagation technique in predicting factor of safety of slopes after two-dimensional OptumG2 analysis , 2019, Engineering with Computers.

[35]  Suzanne Lacasse,et al.  State-of-the-art review of soft computing applications in underground excavations , 2020, Geoscience Frontiers.

[36]  Yuantian Sun,et al.  Evaluation of workability and mechanical properties of asphalt binder and mixture modified with waste toner , 2021 .

[37]  Yuhang Wang,et al.  Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models , 2020 .

[38]  Mahdi Hasanipanah,et al.  A Fuzzy Rule-Based Approach to Address Uncertainty in Risk Assessment and Prediction of Blast-Induced Flyrock in a Quarry , 2020, Natural Resources Research.

[39]  Guichen Li,et al.  Development of ensemble learning models to evaluate the strength of coal-grout materials , 2020 .

[40]  Liborio Cavaleri,et al.  A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon , 2020, Computer Modeling in Engineering & Sciences.

[41]  Yu Zhang,et al.  Development of an adaptive relevance vector machine approach for slope stability inference , 2014, Neural Computing and Applications.

[42]  P. R. Sheorey,et al.  Maximum likelihood estimation of slope stability , 1994 .

[43]  Hani S. Mitri,et al.  Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel , 2017 .

[44]  Dian-Qing Li,et al.  Evaluating slope stability uncertainty using coupled Markov chain , 2016 .

[45]  Habib Shahnazari,et al.  Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability , 2015 .

[46]  Mahdi Hasanipanah,et al.  The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock , 2020, IEEE Access.

[47]  Yuhang Wang,et al.  Strength of ensemble learning in multiclass classification of rockburst intensity , 2020, International Journal for Numerical and Analytical Methods in Geomechanics.

[48]  Ayberk Kaya,et al.  Geotechnical assessment of a slope stability problem in the Citlakkale residential area (Giresun, NE Turkey) , 2017, Bulletin of Engineering Geology and the Environment.

[49]  Y. M. Cheng,et al.  Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods , 2007 .

[50]  Jianxin Li,et al.  Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups , 2020, Engineering with Computers.

[51]  Chris G. Karayannis,et al.  Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks , 2019 .

[52]  Guowei Ma,et al.  Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression , 2019, Construction and Building Materials.

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

[54]  Suzanne Lacasse,et al.  Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method , 2020 .

[55]  Edy Tonnizam Mohamad,et al.  A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls , 2018, Eng. Comput..

[56]  B. Keshtegar,et al.  An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting , 2020, Engineering with Computers.

[57]  Karlis Kaugars,et al.  Neural network modeling applications in active slope stability problems , 2010 .

[58]  Mahdi Hasanipanah,et al.  Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm , 2019, Engineering with Computers.

[59]  Mahdi Hasanipanah,et al.  Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by Optimization Algorithms , 2019, Natural Resources Research.

[60]  Menad Nait Amar,et al.  Modeling CO2 Solubility in Water at High Pressure and Temperature Conditions , 2020 .

[61]  Yuantian Sun,et al.  Developing Hybrid Machine Learning Models for Estimating the Unconfined Compressive Strength of Jet Grouting Composite: A Comparative Study , 2020, Applied Sciences.

[62]  Richard J. Bathurst,et al.  Probabilistic stability analysis of simple reinforced slopes by finite element method , 2016 .

[63]  Panagiotis G. Asteris,et al.  A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm , 2020, Engineering with Computers.

[64]  Liborio Cavaleri,et al.  On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength , 2020 .

[65]  Dale P. Bentz,et al.  Predicting the Permeability of Pervious Concrete , 2010 .

[66]  Yuhang Wang,et al.  An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study , 2020, Neural Computing and Applications.

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

[68]  Junfei Zhang,et al.  Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill , 2020 .

[69]  Jiandong Huang,et al.  Anti-rutting performance of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR) , 2021 .

[70]  A. Goh,et al.  Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils , 2020, Acta Geotechnica.

[71]  Amirmahdi Ghasemi,et al.  Parallelized numerical modeling of the interaction of a solid object with immiscible incompressible two-phase fluid flow , 2017 .

[72]  Jian Zhou,et al.  Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories , 2019, Safety Science.

[73]  P. G. Asteris,et al.  Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness , 2020, Sustainability.

[74]  Wengang Zhang,et al.  Soil Liquefaction Assessment Using Soft Computing Approaches Based on Capacity Energy Concept , 2020, Geosciences.

[75]  Dieu Tien Bui,et al.  A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil , 2019, CATENA.

[76]  Hani S. Mitri,et al.  Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods , 2016, J. Comput. Civ. Eng..

[77]  W. Zhou,et al.  Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm , 2020, Engineering with Computers.

[78]  Wengang Zhang,et al.  Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression , 2020 .

[79]  M. Hasanipanah,et al.  An integrated approach of ANFIS-grasshopper optimization algorithm to approximate flyrock distance in mine blasting , 2021, Eng. Comput..

[80]  Saurav Rukhaiyar,et al.  A PSO-ANN hybrid model for predicting factor of safety of slope , 2017 .

[81]  Karzan Wakil,et al.  Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques , 2020, Engineering with Computers.

[82]  Haiqing Yang,et al.  A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance , 2020, Engineering with Computers.

[83]  Wei Wei,et al.  Stability assessment of a high rock slope by strength reduction finite element method , 2015, Bulletin of Engineering Geology and the Environment.