Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

To test the impact of different mixture ratios on backfilling strength in Fankou lead–zinc mine, various mixture ratio designs have been conducted. Meanwhile, to improve the strength of ultra-fine tailings-based cement paste backfill (CPB), two kinds of fibers were utilized in this study, namely polypropylene (PP) fibers and straw fibers. To achieve these, a total of 144 CPB backfilling scenarios with different combinations of influenced factors were tested by uniaxial compressive tests. The test results indicated that polypropylene fibers improve the strength of CPB, while in some scenarios the addition of straw fibers decreases the strength of CPB. In this research, the support vector machine (SVM) technique coupled with three heuristic algorithms, namely genetic algorithms, particle swarm optimization and salp swarm algorithm (SSA), was developed to predict the strength of fiber-reinforced CPB. Also, the optimal performance of metaheuristic algorithms was compared with one fundamental search method, i.e., grid search (GS). The overall performance of four optimal algorithms was calculated by the ranking system. It can be found that these four approaches all presented satisfactory predictive capability. But the metaheuristic algorithms can capture better hyper-parameters for SVM prediction models compared with GS-SVM method. The robustness and generalization of SSA-SVM methods were the most prominent with the R 2 values of 0.9245 and 0.9475 for training sets and testing sets. Therefore, SSA-SVM will be recommended to model the complexity of interactions for fiber-reinforced CPB and predict fiber-reinforced CPB strength.

[1]  Jian Zhou,et al.  Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models , 2019, Engineering with Computers.

[2]  Mohammadreza Koopialipoor,et al.  Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples , 2019, Engineering with Computers.

[3]  Jian Zhou,et al.  Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression , 2019, Natural Resources Research.

[4]  Bayram Ercikdi,et al.  Strength and ultrasonic properties of cemented paste backfill. , 2014, Ultrasonics.

[5]  Ahmed Koubaa,et al.  Experimental investigation of mechanical and microstructural properties of cemented paste backfill containing maple-wood filler , 2016 .

[6]  T. Singh,et al.  A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength , 2008 .

[7]  Ebrahim Noroozi Ghaleini,et al.  Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN , 2019, Environmental Earth Sciences.

[8]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[9]  Weidong Song,et al.  Mechanical, flexural and microstructural properties of cement-tailings matrix composites: Effects of fiber type and dosage , 2019, Composites Part B: Engineering.

[10]  Xiuzhi Shi,et al.  Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines , 2012 .

[11]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[12]  Xianyu Kong,et al.  Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm. , 2017, Marine pollution bulletin.

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

[14]  Fei Zhang,et al.  Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model , 2020, International Journal of Remote Sensing.

[15]  Mamadou Fall,et al.  The use of artificial neural networks to predict the effect of sulphate attack on the strength of cemented paste backfill , 2010 .

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

[17]  Qianlong Li,et al.  Influence of temperature on compressive strength, microstructure properties and failure pattern of fiber-reinforced cemented tailings backfill , 2019, Construction and Building Materials.

[18]  Ahmadreza Hedayat,et al.  Application of deep neural networks in predicting the penetration rate of tunnel boring machines , 2019, Bulletin of Engineering Geology and the Environment.

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Mohammadreza Koopialipoor,et al.  A new approach for estimation of rock brittleness based on non-destructive tests , 2019, Nondestructive Testing and Evaluation.

[21]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[22]  Koohyar Faizi,et al.  Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles , 2017, Neural Computing and Applications.

[23]  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.

[24]  Bayram Ercikdi,et al.  Predicting the uniaxial compressive strength of cemented paste backfill from ultrasonic pulse velocity test , 2016 .

[25]  Edy Tonnizam Mohamad,et al.  Overbreak prediction and optimization in tunnel using neural network and bee colony techniques , 2018, Engineering with Computers.

[26]  Mahdi Hasanipanah,et al.  Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling , 2016, Engineering with Computers.

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

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

[29]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[30]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[31]  A. Marto,et al.  Application of several optimization techniques for estimating TBM advance rate in granitic rocks , 2019, Journal of Rock Mechanics and Geotechnical Engineering.

[32]  Xiuzhi Shi,et al.  Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm , 2020, Applied Sciences.

[33]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[34]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[35]  Ali Azadeh,et al.  An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine , 2016, Int. J. Comput. Integr. Manuf..

[36]  Jian Zhou,et al.  Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects , 2018 .

[37]  Jian Zhou,et al.  Optimal Charge Scheme Calculation for Multiring Blasting Using Modified Harries Mathematical Model , 2019, Journal of Performance of Constructed Facilities.

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

[39]  Dmitry Zelenko,et al.  Kernel Methods for Relation Extraction , 2002, J. Mach. Learn. Res..

[40]  Hua Zhang,et al.  SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence , 2009 .

[41]  Wenbin Xu,et al.  Assessment of hydration process and mechanical properties of cemented paste backfill by electrical resistivity measurement , 2018 .

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

[43]  Charles A. Micchelli,et al.  Learning Multiple Tasks with Kernel Methods , 2005, J. Mach. Learn. Res..

[44]  Andy Fourie,et al.  Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill , 2018 .

[45]  Tikou Belem,et al.  Experimental characterization of the stress–strain behaviour of cemented paste backfill in compression , 2007 .

[46]  Bhatawdekar Ramesh Murlidhar,et al.  A new hybrid method for predicting ripping production in different weathering zones through in situ tests , 2019 .

[47]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[48]  Andy Fourie,et al.  Cyclic shear response of fibre-reinforced cemented paste backfill , 2013 .

[49]  M. Grujicic,et al.  Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel , 2015 .

[50]  James G. Donovan,et al.  The Effects of Backfilling on Ground Control and Recovery in Thin-Seam Coal Mining , 1999 .

[51]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[52]  Jian Zhou,et al.  Determination of mechanical, flowability, and microstructural properties of cemented tailings backfill containing rice straw , 2020, Construction and Building Materials.

[53]  Hani S. Mitri,et al.  Evaluation method of rockburst: State-of-the-art literature review , 2018, Tunnelling and Underground Space Technology.

[54]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[55]  Danial Jahed Armaghani,et al.  Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm , 2020, Engineering with Computers.

[56]  Jian Zhou,et al.  Deep neural network and whale optimization algorithm to assess flyrock induced by blasting , 2021, Engineering with Computers.

[57]  Mamadou Fall,et al.  Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill , 2011 .

[58]  Masoud Monjezi,et al.  Evaluation of flyrock phenomenon due to blasting operation by support vector machine , 2012, Neural Computing and Applications.

[59]  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.

[60]  D H Zou,et al.  Suitability of mine tailings for shotcrete as a ground support , 2004 .

[61]  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 .

[62]  Jian Zhou,et al.  Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories , 2019, Journal of Performance of Constructed Facilities.

[63]  Jian Zhou,et al.  Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction , 2012 .

[64]  Dayou Liu,et al.  Evolving support vector machines using fruit fly optimization for medical data classification , 2016, Knowl. Based Syst..

[65]  Jian Zhou,et al.  Fiber-Reinforced Cemented Paste Backfill: The Effect of Fiber on Strength Properties and Estimation of Strength Using Nonlinear Models , 2020, Materials.

[66]  Mamadou Fall,et al.  Modeling the effect of sulphate on strength development of paste backfill and binder mixture optimization , 2005 .

[67]  Shunde Yin,et al.  Geomechanical parameters identification by particle swarm optimization and support vector machine , 2009 .

[68]  Michael Rabadi,et al.  Kernel Methods for Machine Learning , 2015 .

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

[70]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[71]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[72]  Ayhan Kesimal,et al.  Effect of properties of tailings and binder on the short-and long-term strength and stability of cemented paste backfill , 2005 .

[73]  Jian Zhou,et al.  Effect of overflow tailings properties on cemented paste backfill. , 2019, Journal of environmental management.

[74]  Reza Tavakkoli-Moghaddam,et al.  A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects , 2013 .

[75]  Danial Jahed Armaghani,et al.  Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique , 2019, IEEE Access.

[76]  Jian Zhou,et al.  Compressive behavior and microstructural properties of tailings polypropylene fibre-reinforced cemented paste backfill , 2018, Construction and Building Materials.

[77]  Guowei Ma,et al.  Compressive behaviour of fibre-reinforced cemented paste backfill , 2015 .

[78]  Xiaolei Li,et al.  Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm , 2016 .

[79]  Mingjun Wang,et al.  Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil , 2009 .

[80]  Yingjie Yang,et al.  A hierarchical analysis for rock engineering using artificial neural networks , 1997 .

[81]  R. J. Mitchell,et al.  Stability of reinforced cemented backfills , 1987 .

[82]  AbdiHervé,et al.  Principal Component Analysis , 2010, Essentials of Pattern Recognition.

[83]  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.

[84]  Hossam Faris,et al.  Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines , 2019, Nature-Inspired Optimizers.

[85]  Paulo Cortez,et al.  Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool , 2010, ICDM.

[86]  Jinchuan Ke,et al.  Empirical Analysis of Optimal Hidden Neurons in Neural Network Modeling for Stock Prediction , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[87]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[88]  Lennart Ljung,et al.  Kernel methods in system identification, machine learning and function estimation: A survey , 2014, Autom..

[89]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[90]  A. Fourie,et al.  A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill , 2018 .

[91]  Mohammadreza Koopialipoor,et al.  Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC) , 2021 .

[92]  Mamadou Fall,et al.  Yield stress and strength of saline cemented tailings in sub-zero environments: Portland cement paste backfill , 2017 .

[93]  Danial Jahed Armaghani,et al.  Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials , 2019, Applied Sciences.

[94]  Weidong Song,et al.  Fiber type effect on strength, toughness and microstructure of early age cemented tailings backfill , 2019, Construction and Building Materials.

[95]  Danial Jahed Armaghani,et al.  The use of new intelligent techniques in designing retaining walls , 2019, Engineering with Computers.

[96]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[97]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[98]  Paulo Cortez,et al.  Lamb Meat Quality Assessment by Support Vector Machines , 2006, Neural Processing Letters.

[99]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[100]  Jian Zhou,et al.  Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance , 2019, Natural Resources Research.

[101]  Jian Zhou,et al.  Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model , 2018, International Journal of Rock Mechanics and Mining Sciences.

[102]  H. Abdi,et al.  Principal component analysis , 2010 .

[103]  Candan Gokceoglu,et al.  Prediction of uniaxial compressive strength of sandstones using petrography-based models , 2008 .