Smart Structures and Systems

  • 제26권3호
  • /
  • Pages.263-275
  • /
  • 2020
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Metaheuristic-hybridized multilayer perceptron in slope stability analysis

  • Ye, Xinyu (School of civil engineering, Central South University) ;
  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Khari, Mahdy (Department of Civil Engineering, East Tehran Branch, Islamic Azad University) ;
  • Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University)
  • 투고 : 2019.12.13
  • 심사 : 2020.05.30
  • 발행 : 2020.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

키워드

참고문헌

  1. Akin, O. and Sahin, M. (2017), "Active neuro-adaptive vibration suppression of a smart beam", Smart Struct. Syst., Int. J., 20(6), 657-668. https://doi.org/10.12989/sss.2017.20.6.657
  2. Aljarah, I., Mafarja, M., Heidari, A.A., Faris, H., Zhang, Y. and Mirjalili, S. (2018), "Asynchronous accelerating multi-leader salp chains for feature selection", Appl. Soft Comput., 71, 964-979. https://doi.org/10.1016/j.asoc.2018.07.040
  3. Bagheri Sereshki, A. and Derakhshani, A. (2018), "Optimizing the Mechanical Stabilization of Earth Walls with Metal Strips: Applications of Swarm Algorithms", Arab. J. Sci. Eng., 445(5), 4653-4666. https://doi.org/10.1007/s13369-018-3492-8
  4. Balasubbareddy, M., Dwivedi, D. and Sathish, D. (2019), "Optimal Power Flow solution using Spotted Hyena Optimization Algorithm", Pramana Res. J., 9(3).
  5. Barari, A., Behnia, M. and Najafi, T. (2015), "Determination of the ultimate limit states of shallow foundations using gene expression programming (GEP) approach", Soils Found., 55(3), 650-659. https://doi.org/10.1016/j.sandf.2015.04.015
  6. Bozorg-Haddad, O. (2018), Advanced Optimization by Nature-Inspired Algorithms, Springer.
  7. Bui, D.T., Moayedi, H., Gor, M., Jaafari, A. and Foong, L.K. (2019a), "Predicting slope stability failure through machine learning paradigms", ISPRS Int. J. Geo-Inform., 8(9), 395. https://doi.org/10.3390/ijgi8090395
  8. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Gor, M., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019b), "Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility", Sensors, 19, 3590. https://doi.org/10.3390/s19163590
  9. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019c), "A novel swarm intelligence-Harris hawks optimization for spatial assessment of landslide susceptibility", Sensors, 19(16), 3590. https://doi.org/10.3390/s19163590
  10. Cai, F. and Ugai, K. (2004), "Numerical analysis of rainfall effects on slope stability", Int. J. Geomech., 4(2), 69-78. https://doi.org/10.1061/(ASCE)1532-3641(2004)4:2(69)
  11. Camargo, J., Velloso, R.Q. and Vargas, E.A. (2016), "Numerical limit analysis of three-dimensional slope stability problems in catchment areas", Acta Geotechnica, 11(6), 1369-1383. https://doi.org/10.1007/s11440-016-0459-3
  12. Chahnasir, E.S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamad, E.T., Shariati, A., Safa, M., Wakil, K. and Khorami, M. (2018), "Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors", Smart Struct. Syst., Int. J., 22(4), 413-424. https://doi.org/10.12989/sss.2018.22.4.413
  13. Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Li, S., Jaafari, A. and Ahmad, B.B. (2019), "Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility", Catena, 172, 212-231. https://doi.org/10.1016/j.catena.2018.08.025
  14. Chen, H., Zhang, Q., Luo, J., Xu, Y. and Zhang, X. (2020), "An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine", Appl. Soft Computi., 86, 105884. https://doi.org/10.1016/j.asoc.2019.105884
  15. Choobbasti, A.J., Farrokhzad, F. and Barari, A. (2009), "Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)", Arab. J. Geosci., 2(4), 311-319. https://doi.org/10.1007/s12517-009-0035-3
  16. Dai, Z., Liu, Z., Liu, C. and He, M. (2008), "Numerical analysis of soil slope stability considering tension and shear failures", Chinese J. Rock Mech. Eng., 2.
  17. Dhiman, G. (2019), "ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems", Eng. Comput., 1-31. https://doi.org/10.1007/s00366-019-00826-w
  18. Dhiman, G. and Kumar, V. (2018), "Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems", Knowledge-Based Syst., 150, 175-197. https://doi.org/10.1016/j.knosys.2018.03.011
  19. Divya, S., Kiran, E.L., Rao, M.S. and Vemulapati, P. (2020), Data Communication and Networks, Springer, pp. 59-67.
  20. Eusuff, M.M. and Lansey, K.E. (2003), "Optimization of water distribution network design using the shuffled frog leaping algorithm", J. Water Resour. Plann. Manage., 129(3), 210-225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)
  21. Fallahian, M., Khoshnoudian, F. and Talaei, S. (2018), "Application of couple sparse coding ensemble on structural damage detection", Smart Struct. Syst., Int. J., 21(1), 1-14. 10.12989/sss.2018.21.1.001
  22. Faris, H., Mafarja, M.M., Heidari, A.A., Aljarah, I., Ala'M, A.Z., Mirjalili, S. and Fujita, H. (2018), "An efficient binary salp swarm algorithm with crossover scheme for feature selection problems", Knowledge-Based Syst., 154, 43-67. https://doi.org/10.1016/j.knosys.2018.05.009
  23. Gandomi, A.H., Kashani, A.R., Mousavi, M. and Jalalvandi, M. (2015), "Slope stability analyzing using recent swarm intelligence techniques", Int. J. Numer. Anal. Methods Geomech., 39(3), 295-309. https://doi.org/10.1002/nag.2308
  24. Gandomi, A.H., Kashani, A.R., Mousavi, M. and Jalalvandi, M. (2017), "Slope stability analysis using evolutionary optimization techniques", Int. J. Numer. Anal. Methods Geomech., 41(2), 251-264. https://doi.org/10.1002/nag.2554
  25. Ghiasi, R. and Ghasemi, M.R. (2018), "Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study", Smart Struct. Syst., Int. J., 22(5), 561-574. https://doi.org/10.12989/sss.2018.22.5.561
  26. Gordan, B., Armaghani, D.J., Hajihassani, M. and Monjezi, M. (2016), "Prediction of seismic slope stability through combination of particle swarm optimization and neural network", Eng. Comput., 32(1), 85-97. https://doi.org/10.1007/s00366-015-0400-7
  27. Hecht-Nielsen, R. (1992), Neural Networks for Perception, Elsevier, pp. 65-93.
  28. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  29. Hussien, A.G., Hassanien, A.E. and Houssein, E.H. (2017), "Swarming behaviour of salps algorithm for predicting chemical compound activities", Proceedings of 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, January, pp. 315-320. https://doi.org/10.1109/INTELCIS.2017.8260072
  30. Ibrahim, R.A., Ewees, A.A., Oliva, D., Elaziz, M.A. and Lu, S. (2019), "Improved salp swarm algorithm based on particle swarm optimization for feature selection", Journal of Ambient Intelligence and Humanized Computing, 10(8), 3155-3169. https://doi.org/10.1007/s12652-018-1031-9
  31. Jalili, S., Kashan, A.H. and Hosseinzadeh, Y. (2016), "League championship algorithms for optimum design of pin-jointed structures", J. Comput. Civil Eng., 31(2), 04016048. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000617
  32. Jia, H., Li, J., Song, W., Peng, X., Lang, C. and Li, Y. (2019), "Spotted hyena optimization algorithm with simulated annealing for feature selection", IEEE Access, 7, 71943-71962. https://doi.org/10.1109/ACCESS.2019.2919991
  33. Kashan, A.H. (2009), "League championship algorithm: a new algorithm for numerical function optimization", Proceedings of 2009 International Conference of Soft Computing and Pattern Recognition, Malacca, Malaysia, December, pp. 43-48. https://doi.org/10.1109/SoCPaR.2009.21
  34. Kashan, A.H. (2011), "An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA)", Comput.-Aided Des., 43(12), 1769-1792. https://doi.org/10.1016/j.cad.2011.07.003
  35. Kashan, A.H. (2014), "League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships", Appl. Soft Comput., 16, 171-200. https://doi.org/10.1016/j.asoc.2013.12.005
  36. Keshavarz, Z. and Torkian, H. (2018), "Application of ANN and ANFIS models in determining compressive strength of concrete", Soft Comput. Civil Eng., 2(1), 62-70. https://doi.org/10.22115/SCCE.2018.51114
  37. Kimiyaghalam, A., Ashouri, A. and Beykverdi, M. (2012), "Application of IBSFLA and BSFLA approaches for locating of fault indicators in distribution networks", Proceedings of 17th Conference on Electrical Power Distribution, Tehran, Iran, August, pp. 1-7.
  38. Kohestani, V.R., Vosoghi, M., Hassanlourad, M. and Fallahnia, M. (2017), "Bearing capacity of shallow foundations on cohesionless soils: A random forest based approach", Civil Eng. Infrastruct. J., 50(1), 35-49. https://doi.org/10.7508/CEIJ.2017.01.003
  39. Krabbenhoft, K., Lyamin, A. and Krabbenhoft, J. (2015), "Optum Computational Engineering (Optum G2)". Available on: www.optumce.com
  40. Leshchinsky, B. and Ambauen, S. (2015), "Limit equilibrium and limit analysis: comparison of benchmark slope stability problems", J. Geotech. Geoenviron. Eng., 141(10), 04015043. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001347
  41. Li, B., Li, D., Zhang, Z., Yang, S. and Wang, F. (2015), "Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine", Appl. Mathe. Model., 39(17), 5253-5264. https://doi.org/10.1016/j.apm.2015.03.032
  42. Li, A.J., Khoo, S., Lyamin, A.V. and Wang, Y. (2016), "Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm", Automat. Constr., 65, 42-50. https://doi.org/10.1016/j.autcon.2016.02.004
  43. Li, A., Fang, Q., Zhang, D., Luo, J. and Hong, X. (2018), "Blast vibration of a large-span high-speed railway tunnel based on microseismic monitoring", Smart Struct. Syst., Int. J., 21(5), 561-569. https://doi.org/10.12989/sss.2018.21.5.561
  44. Lim, K., Li, A.J., Schmid, A. and Lyamin, A.V. (2016), "Slope-stability assessments using finite-element limit-analysis methods", Int. J. Geomech., 17(2), 06016017. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000715
  45. Liping, Z., Weiwei, W., Yi, H., Yefeng, X. and Yixian, C. (2012), "Application of shuffled frog leaping algorithm to an uncapacitated SLLS problem", AASRI Procedia, 1, 226-231. https://doi.org/10.1016/j.aasri.2012.06.035
  46. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathe. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259
  47. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H. and Mirjalili, S.M. (2017), "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems", Adv. Eng. Software, 114, 163-191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  48. Moayedi, H. and Hayati, S. (2018), "Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods", Appl. Soft Comput., 66, 208-219. https://doi.org/10.1016/j.asoc.2018.02.027
  49. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2018), "Modification of landslide susceptibility mapping using optimized PSO-ANN technique", Eng. Comput., 35, 967-984. https://doi.org/10.1007/s00366-018-0644-0
  50. Moayedi, H., Osouli, A., Nguyen, H. and Rashid, A.S.A. (2019a), "A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability", Eng. Comput., 1-11. https://doi.org/10.1007/s00366-019-00828-8
  51. Moayedi, H., Tien Bui, D., Dounis, A., Kok Foong, L. and Kalantar, B. (2019b), "Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength", Appl. Sci., 9(21), 4643. https://doi.org/10.3390/app9214643
  52. Moayedi, H., Tien Bui, D., Gor, M., Pradhan, B. and Jaafari, A. (2019c), "The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes", ISPRS Int. J. Geo-Inform., 8(9), 391. https://doi.org/10.3390/ijgi8090391
  53. Moayedi, H., Gor, M., Lyu, Z. and Bui, D.T. (2020a), "Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient", Measurement, 152, 107389. https://doi.org/10.1016/j.measurement.2019.107389
  54. Moayedi, H., Tien Bui, D., Dounis, A. and Ngo, P.T.T. (2020b), "A novel application of league championship optimization (LCA): hybridizing fuzzy logic for soil compression coefficient analysis", Appl. Sci., 10(1), 67. https://doi.org/10.3390/app10010067
  55. Mosallanezhad, M. and Moayedi, H. (2017), "Comparison analysis of bearing capacity approaches for the strip footing on layered soils", Arab. J. Sci. Eng., 42(9), 3711-3722. https://doi.org/10.1007/s13369-017-2490-6
  56. Ng, C. and Shi, Q. (1998), "A numerical investigation of the stability of unsaturated soil slopes subjected to transient seepage", Comput. Geotech., 22(1), 1-28. https://doi.org/10.1016/S0266-352X(97)00036-0
  57. Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (2019), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geomat. Natural Hazards Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782
  58. Onat, O. and Gul, M. (2018), "Application of Artificial Neural Networks to the prediction of out-of-plane response of infill walls subjected to shake table", Smart Struct. Syst., Int. J., 21(4), 521-535. https://doi.org/10.12989/sss.2018.21.4.521
  59. Pradatta, S.P., Kumar, S.S. and Alireza, M. (2018), "Analytical expressions for determining the stability of cohesionless soil slope under generalized seismic conditions", J. Mount. Sci., 15(7), 1559-1571. https://doi.org/10.1007/s11629-017-4780-6
  60. Qiao, W., Moayedi, H. and Foong, L.K. (2020), "Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption", Energy Build., 110023. [In press] https://doi.org/10.1016/j.enbuild.2020.110023
  61. Roy, P., Roy, P. and Chakrabarti, A. (2013), "Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect", Appl. Soft Comput., 13(11), 4244-4252. https://doi.org/10.1016/j.asoc.2013.07.006
  62. Sakellariou, M. and Ferentinou, M. (2005), "A study of slope stability prediction using neural networks", Geotech. Geol. Eng., 23(4), 419. https://doi.org/10.1007/s10706-004-8680-5
  63. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B. and Liu, D. (2016), "Evolving support vector machines using fruit fly optimization for medical data classification", Knowledge-Based Syst., 96, 61-75. https://doi.org/10.1016/j.knosys.2016.01.002
  64. Supraja, P. and Jayashri, S. (2017), "Optimized neural network for spectrum prediction scheme in cognitive radio", Wireless Personal Commun., 94(4), 2597-2611. https://doi.org/10.1007/s11277-016-3818-3
  65. Wang, M. and Chen, H. (2020), "Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis", Appl. Soft Comput., 88, 105946. https://doi.org/10.1016/j.asoc.2019.105946
  66. Wang, M., Chen, H., Yang, B., Zhao, X., Hu, L., Cai, Z., Huang, H. and Tong, C. (2017), "Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses", Neurocomputing, 267, 69-84. https://doi.org/10.1016/j.neucom.2017.04.060
  67. Wang, L., Chen, B. and Li, J. (2019), "Three-dimensional seismic stability of unsaturated soil slopes using a semi-analytical method", Comput. Geotech., 110, 296-307. https://doi.org/10.1016/j.compgeo.2019.02.008
  68. Xu, X. and Chen, H.L. (2014), "Adaptive computational chemotaxis based on field in bacterial foraging optimization", Soft Computing, 18(4), 797-807. https://doi.org/10.1007/s00500-013-1089-4
  69. Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S. and Zhang, X. (2019), "Enhanced Moth-flame optimizer with mutation strategy for global optimization", Inform. Sci., 492, 181-203. https://doi.org/10.1016/j.ins.2019.04.022
  70. Yuan, C. and Moayedi, H. (2019), "The performance of six neural-evolutionary classification techniques combined with multi-layer perception in two-layered cohesive slope stability analysis and failure recognition", Eng. Comput., 1-10. https://doi.org/10.1007/s00366-019-00791-4
  71. Zhang, X. and Zhou, X. (2018), "Analysis of the numerical stability of soil slope using virtual-bond general particle dynamics", Eng. Geol., 243, 101-110. https://doi.org/10.1016/j.enggeo.2018.06.018
  72. Zhang, X., Zhang, Y., Shi, Y., Zhao, L. and Zou, C. (2012), "Power control algorithm in cognitive radio system based on modified shuffled frog leaping algorithm", AEU-Int. J. Electron. Commun., 66(6), 448-454. https://doi.org/10.1016/j.aeue.2011.10.004
  73. Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y. and Chen, H. (2014), "Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton", Appl. Soft Comput., 24, 585-596. https://doi.org/10.1016/j.asoc.2014.07.024
  74. Zhao, X., Zhang, X., Cai, Z., Tian, X., Wang, X., Huang, Y., Chen, H. and Hu, L. (2019), "Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients", Computat. Biol. Chem., 78, 481-490. https://doi.org/10.1016/j.compbiolchem.2018.11.017
  75. Zhou, G., Moayedi, H., Bahiraei, M. and Lyu, Z. (2020), "Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings", J. Cleaner Prod., 254, 120082. https://doi.org/10.1016/j.jclepro.2020.120082
  76. Zounemat-Kermani, M., Kisi, O., Adamowski, J. and Ramezani-Charmahineh, A. (2016), "Evaluation of data driven models for river suspended sediment concentration modeling", J. Hydrol., 535, 457-472. https://doi.org/10.1016/j.jhydrol.2016.02.012

피인용 문헌

  1. Identification and evaluation of cracks in electrostatically actuated resonant gas sensors using Harris Hawk / Nelder Mead and perturbation methods vol.28, pp.1, 2020, https://doi.org/10.12989/sss.2021.28.1.121
  2. Machine learning-based prediction and performance study of transparent soil properties vol.28, pp.2, 2021, https://doi.org/10.12989/sss.2021.28.2.289