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Engineering with Computers
Published in: Engineering with Computers 4/2021

27-03-2020 | Original Article

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

Authors: Enming Li, Jian Zhou, Xiuzhi Shi, Danial Jahed Armaghani, Zhi Yu, Xin Chen, Peisheng Huang

Published in: Engineering with Computers | Issue 4/2021

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Abstract

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 R2 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.
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Literature
1.
Yılmaz T, Ercikdi B (2016) Predicting the uniaxial compressive strength of cemented paste backfill from ultrasonic pulse velocity test. Nondestruct Test Eval 31(3):247–266
2.
Fall M, Benzaazoua M (2005) Modeling the effect of sulphate on strength development of paste backfill and binder mixture optimization. Cem Concr Res 35(2):301–314
3.
Orejarena L, Fall M (2010) Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill. Can J Civ Eng 38(1):100–109
4.
Jiang H, Fall M (2017) Yield stress and strength of saline cemented tailings in sub-zero environments: portland cement paste backfill. Int J Miner Process 160:68–75
5.
Qi C, Fourie A, Chen Q (2018) Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill. Constr Build Mater 159:473–478
6.
Chen X, Shi X, Zhou J, Du X, Chen Q, Qiu X (2019) Effect of overflow tailings properties on cemented paste backfill. J Environ Manag 235:133–144
7.
Chen X, Shi X, Zhou J, Yu Z, Huang P (2020) Determination of mechanical, flowability, and microstructural properties of cemented tailings backfill containing rice straw. Constr Build Mater 246:118520
8.
Chen X, Shi X, Zhang S, Chen H, Zhou J, Yu Z, Huang P (2020) Fiber-reinforced cemented paste backfill: the effect of fiber on strength properties and estimation of strength using nonlinear models. Materials 13(3):718
9.
Orejarena L, Fall M (2010) The use of artificial neural networks to predict the effect of sulphate attack on the strength of cemented paste backfill. Bull Eng Geol Env 69(4):659–670
10.
Donovan JG (1999) The effects of backfilling on ground control and recovery in thin-seam coal mining. Doctoral dissertation, Virginia Tech
11.
Qi C, Fourie A, Chen Q, Zhang Q (2018) A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill. J Clean Prod 183:566–578
12.
Chen X, Shi X, Zhou J, Chen Q, Li E, Du X (2018) Compressive behavior and microstructural properties of tailings polypropylene fibre-reinforced cemented paste backfill. Constr Build Mater 190:211–221
13.
Cao S, Yilmaz E, Song W (2019) Fiber type effect on strength, toughness and microstructure of early age cemented tailings backfill. Constr Build Mater 223:44–54
14.
Xu W, Li Q, Zhang Y (2019) Influence of temperature on compressive strength, microstructure properties and failure pattern of fiber-reinforced cemented tailings backfill. Constr Build Mater 222:776–785
15.
Xue G, Yilmaz E, Song W, Cao S (2019) Mechanical, flexural and microstructural properties of cement-tailings matrix composites: effects of fiber type and dosage. Compos B Eng 172:131–142
16.
Kesimal A, Yilmaz E, Ercikdi B, Alp I, Deveci H (2005) Effect of properties of tailings and binder on the short-and long-term strength and stability of cemented paste backfill. Mater Lett 59(28):3703–3709
17.
Fall M, Belem T, Samb S, Benzaazoua M (2007) Experimental characterization of the stress–strain behaviour of cemented paste backfill in compression. J Mater Sci 42(11):3914–3922
18.
Xu W, Tian X, Cao P (2018) Assessment of hydration process and mechanical properties of cemented paste backfill by electrical resistivity measurement. Nondestruct Test Eval 33(2):198–212
19.
Sharma PK, Singh TN (2008) A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Env 67(1):17–22
20.
Ercikdi B, Yılmaz T, Külekci G (2014) Strength and ultrasonic properties of cemented paste backfill. Ultrasonics 54(1):195–204
21.
Wang M, Shi X, Zhou J (2019) Optimal charge scheme calculation for multiring blasting using modified harries mathematical model. J Perform Constr Facil 33(2):04019002
22.
Shi XZ, Zhou J, Wu BB, Huang D, Wei W (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22(2):432–441
23.
Koopialipoor M, Tootoonchi H, Armaghani DJ, Mohamad ET, Hedayat A (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Env 78(8):6347–6360
24.
25.
Armaghani DJ, Koopialipoor M, Marto A et al (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11(4):779–789
26.
Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50(12):2177–2191
27.
28.
29.
30.
Zhou J, Koopialipoor M, Murlidhar BR, Fatemi SA, Tahir MM, Armaghani DJ, Li C (2020) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Nat Resour Res 29:625–639
31.
Koopialipoor M, Murlidhar BR, Hedayat A, Armaghani DJ, Gordan B, Mohamad ET (2020) The use of new intelligent techniques in designing retaining walls. Eng Comput 36:283–294
32.
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
33.
Wang M, Shi X, Zhou J (2018) Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model. Int J Rock Mech Min Sci 110:199–209
34.
Xu C, Gordan B, Koopialipoor M, Armaghani DJ, Tahir MM, Zhang X (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700
35.
Yang H, Koopialipoor M, Armaghani DJ, Gordan B, Khorami M, Tahir MM (2019) Intelligent design of retaining wall structures under dynamic conditions. Steel Compos Struct 31(6):629–640
36.
Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30(5):04016003
37.
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659
38.
Mohamad ET, Koopialipoor M, Murlidhar BR, Rashiddel A, Hedayat A, Armaghani DJ (2019) A new hybrid method for predicting ripping production in different weathering zones through in situ tests. Measurement 147:106826
39.
Koopialipoor M, Noorbakhsh A, Noroozi Ghaleini E, Jahed Armaghani D, Yagiz S (2019) A new approach for estimation of rock brittleness based on non-destructive tests. Nondestruct Test Eval 34(4):354–375
40.
Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024
41.
Xue-jie D, Ji-xiong Z, Tao K, Dong-sheng W (2014) Prediction of cement illing materials performance using improved BP neural network. Electron J Geotech Eng 19:4537–4549
42.
Ke J, Liu X (2008) Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction. In: 2008 IEEE Pacific-Asia workshop on computational intelligence and industrial application, vol 2, pp 828–832. IEEE
43.
Vapnik V (1995) The nature of statistical learning theory. Springer, BerlinMATH
44.
Amini H, Gholami R, Monjezi M, Torabi SR, Zadhesh J (2012) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl 21(8):2077–2085
45.
Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79(1):291–316
46.
Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644
47.
Yu Z, Shi X, Zhou J, Chen X, Miao X, Teng B, Ipangelwa T (2020) Prediction of blast-induced rock movement during bench blasting: use of gray wolf optimizer and support vector regression. Nat Resour Res 29:843–865
48.
Mitchell RJ, Stone DM (1987) Stability of reinforced cemented backfills. Can Geotech J 24(2):189–197
49.
Zou DH, Sahito W (2004) Suitability of mine tailings for shotcrete as a ground support. Can J Civ Eng 31(4):632–636
50.
Festugato L, Fourie A, Consoli NC (2013) Cyclic shear response of fibre-reinforced cemented paste backfill. Géotech Lett 3(1):5–12
51.
Yi XW, Ma GW, Fourie A (2015) Compressive behaviour of fibre-reinforced cemented paste backfill. Geotext Geomembr 43(3):207–215
52.
Koohestani B, Koubaa A, Belem T, Bussière B, Bouzahzah H (2016) Experimental investigation of mechanical and microstructural properties of cemented paste backfill containing maple-wood filler. Constr Build Mater 121:222–228
53.
Fernandez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181MathSciNetMATH
54.
Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. ANALYST 135(2):230
55.
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
56.
Gunn SR (1998) Support vector machines for classification and regression. ISIS Techn Rep 14(1):5–16
57.
Cortez P (2010) Data mining with neural networks and support vector machines using the R/rminer tool. In: Industrial conference on data mining, pp 572–583. Springer, Berlin, Heidelberg
58.
Cortez P, Portelinha M, Rodrigues S, Cadavez V, Teixeira A (2006) Lamb meat quality assessment by support vector machines. Neural Process Lett 24(1):41–51
59.
Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126MATH
60.
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, CambridgeMATH
61.
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeMATH
62.
Pillonetto G, Dinuzzo F, Chen T, De Nicolao G, Ljung L (2014) Kernel methods in system identification, machine learning and function estimation: a survey. Automatica 50(3):657–682MathSciNetMATH
63.
Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 36:1171–1220MathSciNetMATH
64.
Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res 6(Apr):615–637MathSciNetMATH
65.
Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3(Feb):1083–1106MathSciNetMATH
66.
Zhang H, Wang YJ, Li YF (2009) SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence. Min Sci Technol (China) 19(3):385–388
67.
Min SH, Lee J, Han I (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl 31(3):652–660
68.
Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240
69.
Pai PF, Hong WC (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr Power Syst Res 74(3):417–425
70.
Zhao HB, Yin S (2009) Geomechanical parameters identification by particle swarm optimization and support vector machine. Appl Math Model 33(10):3997–4012MathSciNetMATH
71.
Fei SW, Wang MJ, Miao YB, Tu J, Liu CL (2009) Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil. Energy Convers Manag 50(6):1604–1609
72.
Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75
73.
Cong Y, Wang J, Li X (2016) Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Procedia Eng 137:59–68
74.
Mousavi SM, Tavakkoli-Moghaddam R, Vahdani B, Hashemi H, Sanjari MJ (2013) A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects. Robot Comput Integr Manuf 29(1):157–168
75.
Azadeh A, Seif J, Sheikhalishahi M, Yazdani M (2016) An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine. Int J Comput Integr Manuf 29(1):16–24
76.
Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948
77.
Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97
78.
Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715
79.
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57
80.
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge
81.
David L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York
82.
Wong F, Tan C (1994) Hybrid neural, genetic, and fuzzy systems. In: Deboek GJ (ed) Trading on the edge. Wiley, New York, pp 243–261
83.
Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the third international conference on Genetic algorithms, pp 2–9. Morgan Kaufmann Publishers
84.
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
85.
Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. In: Mirjalili S, Song Dong J, Lewis A (eds) Nature-inspired optimizers. Studies in computational intelligence, vol 811. Springer, Cham, pp 185–199
86.
Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459
87.
Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6(9):2812–2831
88.
Kuhn M, Johnson K (2013) Applied predictive modeling, vol 26. Springer, New YorkMATH
89.
Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn 48(9):2839–2846MATH
90.
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai 14(2):1137–1145
91.
Koopialipoor M, Armaghani DJ, Hedayat A, Marto A, Gordan B (2019) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput 23(14):5913–5929
92.
Wang X, Zhang F, Kung HT, Johnson VC, Latif A (2019) Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model. Int J Remote Sens 41(3):953–973
93.
Kong X, Sun Y, Su R, Shi X (2017) Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm. Mar Pollut Bull 119(1):307–319
94.
Yasin H, Caraka RE, Hoyyi A (2016) Prediction of crude oil prices using support vector regression (SVR) with grid search cross validation algorithm. Glob J Pure Appl Math 12(4):3009–3020
95.
Armaghani DJ, Raja RSNSB, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28(2):391–405
96.
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, vol 5, pp 4104–4108. IEEE
97.
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
98.
Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3–4):141–158
99.
Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30(4):207–222
100.
101.
102.
Yu Z, Shi X, Zhou J, Chen X, Qiu X (2020) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris hawks optimization algorithm. Appl Sci 10(4):1403
103.
Koopialipoor M, Ghaleini EN, Tootoonchi H, Armaghani DJ, Haghighi M, Hedayat A (2019) Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ Earth Sci 78(5):165
104.
Koopialipoor M, Ghaleini EN, Haghighi M, Kanagarajan S, Maarefvand P, Mohamad ET (2019) Overbreak prediction and optimization in tunnel using neural network and bee colony techniques. Eng Comput 35(4):1191–1202
105.
Zhou J, Li E, Wei H, Li C, Qiao Q, Armaghani DJ (2019) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Appl Sci 9(8):1621
106.
Zhou J, Shi X, Du K, Qiu X, Li X, Mitri HS (2017) Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. Int J Geomech 17(6):04016129
Metadata
Title
Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill
Authors
Enming Li
Jian Zhou
Xiuzhi Shi
Danial Jahed Armaghani
Zhi Yu
Xin Chen
Peisheng Huang
Publication date
27-03-2020
Publisher
Springer London
Published in
Engineering with Computers / Issue 4/2021
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-020-01014-x

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