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A novel search scheme based on the social behavior of crow flock for feed-forward learning improvement in predicting the soil compression coefficient

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Abstract

Recent improvements achieved using nature-inspired optimizers encouraged the authors to employ a novel type of metaheuristic algorithms, namely crow search algorithm (CSA) in this study. The CSA is employed for optimizing a feed-forward artificial neural network (ANN) in predicting the soil compression coefficient (SCC). The SCC is one of the most crucial geotechnical parameters that the early prediction of it can increase the safety and cost-effectiveness of a project. For more reliability, the used data are collected from a real-world project. After developing the CSA–ANN hybrid, the most proper values for the algorithm parameters, including flock size, flight length, and awareness probability are found by sensitivity analysis (to be 400, 2, and 0.1, respectively). A comparison between the results of the typical ANN and the CSA-trained version revealed that the proposed algorithm can effectively reduce the mean absolute error (MAE) in both learning and predicting the SCC pattern (by 8.25 and 7.29%, respectively). Moreover, the increase of the coefficient of determination (R2) from 70.64 to 74.83% in the training phase, and from 73.74 to 76.19% in the testing phase proves the efficiency of the CSA in enhancing the ANN. The suggested CSA–ANN, therefore, can be an efficient model for the early prediction of the SCC in civil/geotechnical engineering projects.

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References

  1. Kim Y-T, Do T-H (2011) Experimental evaluation of leaching effects on the compressibility of marine clay and its strain rate dependency. Mar Georesour Geotechnol 29:16–29

    Google Scholar 

  2. Kurnaz TF, Dagdeviren U, Yildiz M, Ozkan O (2016) Prediction of compressibility parameters of the soils using artificial neural network. SpringerPlus 5:1801

    Google Scholar 

  3. Pang R, Xu B, Kong X, Zou D (2018) Seismic fragility for high CFRDs based on deformation and damage index through incremental dynamic analysis. Soil Dyn Earthq Eng 104:432–436

    Google Scholar 

  4. Pang R, Xu B, Zou D, Kong X (2018) Stochastic seismic performance assessment of high CFRDs based on generalized probability density evolution method. Comput Geotech 97:233–245

    Google Scholar 

  5. Tian X, Song Z, Wang B, Zhou G (2020) A theoretical calculation method of influence radius of settlement based on the slices method in tunnel construction. Math Probl Eng. https://doi.org/10.1155/2020/5804823

  6. Tian X, Song Z, Wang J (2019) Study on the propagation law of tunnel blasting vibration in stratum and blasting vibration reduction technology. Soil Dyn Earthq Eng 126:105813

    Google Scholar 

  7. Kang X, Onyejekwe S, Ge L, Stephenson R (2011) Spatial variation and correlation between undrained shear strength and plasticity index, Geo-Frontiers 2011: Advances in Geotechnical Engineering, pp 2631–2639

  8. Ameratunga J, Sivakugan N, Das BM (2016) Correlations of soil and rock properties in geotechnical engineering. Springer, New York

    Google Scholar 

  9. Moayedi H, Gör M, Khari M, Foong LK, Bahiraei M, Bui DT (2020) Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength. Measurement 156:107576

    Google Scholar 

  10. Moayedi H, Tien Bui D, Dounis A, Kok Foong L, Kalantar B (2019) Novel nature-inspired hybrids of neural computing for estimating soil shear strength. Appl Sci 9:4643

    Google Scholar 

  11. Gunduz Z, Arman H (2007) Possible relationships between compression and recompression indices of a low-plasticity clayey soil. Arab J Sci Eng 32:179

    Google Scholar 

  12. Ahadian J, EBN JR, Shafaei BM (2008) Determination of soil compression index, Cc, in Ahwaz region. J Fac Eng (University Of Tabriz) 35:75–80

  13. Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay-bearing soils. Bull Eng Geol Env 67:537–545

    Google Scholar 

  14. Onyejekwe S, Kang X, Ge L (2015) Assessment of empirical equations for the compression index of fine-grained soils in Missouri. Bull Eng Geol Environ 74:705–716

    Google Scholar 

  15. Namdarvand F, Jafarnejadi A, Sayyad G (2013) Estimation of soil compression coefficient using artificial neural network and multiple regressions. Int Res J Appl Basic Sci 4:3232–3236

    Google Scholar 

  16. Shahsavar A, Moayedi H, Al-Waeli AHA, Sopian K, Chelvanathan P (2020) Machine learning predictive models for optimal design of building-integrated photovoltaic-thermal collectors. Int J Energy Res. https://doi.org/10.1002/er.5323

    Article  Google Scholar 

  17. Liu W, Moayedi H, Nguyen H, Lyu Z, Bui DT (2019) Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil. Eng Comput. https://doi.org/10.1007/s00366-019-00897-9

    Article  Google Scholar 

  18. Moayedi H, Bui DT, Anastasios D, Kalantar B (2019) Spotted hyena optimizer and ant lion optimization in predicting the shear strength of soil. Appl Sci 9:4738

    Google Scholar 

  19. Liu W, Zhang X, Li H, Chen J (2020) Investigation on the Deformation and Strength Characteristics of Rock Salt Under Different Confining Pressures. Geotech Geol Eng 38:1–15. https://doi.org/10.1007/s10706-020-01388-1

  20. Cao B, Zhao J, Lv Z, Gu Y, Yang P, Halgamuge SK (2020) Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans Fuzzy Syst 28:939–952

    Google Scholar 

  21. Chen F, Yang Y, Tang B, Chen B, Xiao W, Zhong X (2020) Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. Measurement 151:107116

    Google Scholar 

  22. Sun G, Xu G, Jiang N (2020) A simple differential evolution with time-varying strategy for continuous optimization. Soft Comput 24:2727–2747

    Google Scholar 

  23. Xu X, Chen H-L (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18:797–807

    Google Scholar 

  24. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput 88:105946

    Google Scholar 

  25. Qu S, Zhao L, Xiong Z (2020) Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04758-1

  26. Yang L, Chen H (2019) Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Comput Appl 31:4463–4478

    Google Scholar 

  27. Zhao X, Li D, Yang B, Ma C, Zhu Y, 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

    Google Scholar 

  28. Chen H, Zhang Q, Luo J, Xu Y, Zhang X (2020) An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine. Appl Soft Comput 86:105884

    Google Scholar 

  29. Nguyen MD, Pham BT, Tuyen TT, Yen H, Phan H, Prakash I, Vu TT, Chapi K, Shirzadi A, Shahabi H (2019) Development of an artificial intelligence approach for prediction of consolidation coefficient of soft soil: a sensitivity analysis. Open Constr Build Technol J 13:178–188

  30. Nhu V-H, Samui P, Kumar D, Singh A, Hoang N-D, Bui DT (2019) Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study. Eng Comput. https://doi.org/10.1007/s00366-019-00772-7

  31. Pham BT, Nguyen MD, Van Dao D, Prakash I, Ly H-B, Le T-T, Ho LS, Nguyen KT, Ngo TQ, Hoang V (2019) Development of artificial intelligence models for the prediction of compression coefficient of soil: an application of monte carlo sensitivity analysis. Sci Total Environ 679:172–184

    Google Scholar 

  32. Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl-Based Syst 96:61–75

    Google Scholar 

  33. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, 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

    Google Scholar 

  34. Benbouras MA, Kettab Mitiche R, Zedira H, Petrisor A-I, Mezouar N, Debiche F (2019) A new approach to predict the compression index using artificial intelligence methods. Mar Georesour Geotechnol 37:704–720

    Google Scholar 

  35. Alam S, Khuntia S, Patra C (2014) Prediction of compression index of clay using artificial neural network. In: International conference on industrial engineering science and applications-NIT, Durgapur

  36. Park HI, Lee SR (2011) Evaluation of the compression index of soils using an artificial neural network. Comput Geotech 38:472–481

    Google Scholar 

  37. Zhou G, Moayedi H, Bahiraei M, 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 Clean Prod 254:120082

  38. Qiao W, Moayedi H, Foong KL (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. https://doi.org/10.1016/j.enbuild.2020.110023(in press)

    Article  Google Scholar 

  39. Moayedi H, Gör M, Lyu Z, Bui DT (2020) 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

    Article  Google Scholar 

  40. Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA (2019) A novel swarm intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility. Sensors 19:3590

    Google Scholar 

  41. Bahiraei M, Heshmatian S, Goodarzi M, Moayedi H (2019) CFD analysis of employing a novel ecofriendly nanofluid in a miniature pin fin heat sink for cooling of electronic components: effect of different configurations. Adv Powder Technol. https://doi.org/10.1016/j.apt.2019.07.029

    Article  Google Scholar 

  42. Chiroma H, Gital AYu, Rana N, Shafi’i MA, Muhammad AN, Umar AY, Abubakar AI (2019) Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective. In: Science and Information Conference

  43. Cao Y, Li Y, Zhang G, Jermsittiparsert K, Nasseri M (2020) An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Rep 6:530–542

    Google Scholar 

  44. Chen H, Fan DL, Fang L, Huang W, Huang J, Cao C, Yang L, He Y, Zeng L (2020) Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis. Int J Pattern Recognit Artif Intell. https://doi.org/10.1142/S0218001420580124

  45. Gu F, Ma B, Guo J, Summers PA, Hall P (2017) Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: an exploratory study. Waste Manag 68:434–448

    Google Scholar 

  46. Gao W, Alsarraf J, Moayedi H, Shahsavar A, Nguyen H (2019) Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Appl Soft Comput 84:105748. https://doi.org/10.1016/j.asoc.2019.105748

    Article  Google Scholar 

  47. Moayedi H, 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

    Article  Google Scholar 

  48. Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:10. https://doi.org/10.1007/s12517-017-3285-5

    Article  Google Scholar 

  49. Mosallanezhad M, Moayedi H (2017) Comparison analysis of bearing capacity approaches for the strip footing on layered soils. Arab J Sci Eng 42:3711–3722

  50. Bui X-N, Jaroonpattanapong P, Nguyen H, Tran Q-H, Long NQ (2019) A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle Swarm optimization. Sci Rep 9:1–14

    Google Scholar 

  51. Moayedi H, Kalantar B, Dounis A, Tien Bui D, Foong LK (2019) Development of two novel hybrid prediction models estimating ultimate bearing capacity of the shallow circular footing. Appl Sci 9:4594

    Google Scholar 

  52. Moayedi H, Tien Bui D, Dounis A, Ngo PTT (2020) A novel application of league championship optimization (LCA): hybridizing fuzzy logic for soil compression coefficient analysis. Appl Sci 10:67

    Google Scholar 

  53. Samui P, Hoang N-D, Nhu V-H, Nguyen M-L, Ngo PTT, Bui DT (2019) A new approach of hybrid bee colony optimized neural computing to estimate the soil compression coefficient for a housing construction project. Appl Sci 9:4912

    Google Scholar 

  54. Mohammadzadeh D, Bazaz JB, Alavi AH (2014) An evolutionary computational approach for formulation of compression index of fine-grained soils. Eng Appl Artif Intell 33:58–68

    Google Scholar 

  55. Bui DT, Nhu V-H, Hoang N-D (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Adv Eng Inform 38:593–604

    Google Scholar 

  56. Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203

    MathSciNet  Google Scholar 

  57. Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490

    Google Scholar 

  58. Naik B, Mishra D, Nayak J, Pelusi D, Abraham A (2017) Perturbation based efficient crow search optimized FLANN for system identification: a novel approach. In: International conference on health information science

  59. Rezaie-Balf M, Maleki N, Kim S, Ashrafian A, Babaie-Miri F, Kim NW, Chung I-M, Alaghmand S (2019) Forecasting daily solar radiation using CEEMDAN decomposition-based MARS model trained by crow search algorithm. Energies 12:1416

    Google Scholar 

  60. Sannasi Chakravarthy S, Rajaguru H (2019) Lung cancer detection using probabilistic neural network with modified crow-search algorithm. Asian Pac J Cancer Prev 20:2159

    Google Scholar 

  61. Wasserman PD (1993) Advanced methods in neural computing. Wiley, Hoboken

    MATH  Google Scholar 

  62. Anthony M, Bartlett PL (2009) Neural network learning: theoretical foundations. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  63. Moayedi H, Hayati S (2019) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl 31:7429–7445

    Google Scholar 

  64. Khotanzad A, Elragal H, Lu T-L (2000) Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Trans Neural Networks 11:464–473

    Google Scholar 

  65. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2:359–366

    MATH  Google Scholar 

  66. Hecht-Nielsen R (1992) Theory of the backpropagation neural network. Neural networks for perception. Elsevier, Amsterdam, pp 65–93

    Google Scholar 

  67. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Google Scholar 

  68. Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31:171–188

    Google Scholar 

  69. Arora S, Singh H, Sharma M, Sharma S, Anand P (2019) A new hybrid algorithm based on Grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access 7:26343–26361

    Google Scholar 

  70. Nhu V-H, Hoang N-D, Duong V-B, Vu H-D, Bui DT (2019) A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong City (Vietnam). Eng Comput 36:603–616

  71. Zhou G, Moayedi H, Foong LK (2020) Teaching–learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building. Eng Comput. https://doi.org/10.1007/s00366-020-00981-5

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Authors and Affiliations

  1. School of Civil Engineering, Central South University, Changsha, 410075, China

    Fang Xu

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Zongjie Lyu

  5. Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Zongjie Lyu

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  1. Fang Xu
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Xu, F., Foong, L.K. & Lyu, Z. A novel search scheme based on the social behavior of crow flock for feed-forward learning improvement in predicting the soil compression coefficient. Engineering with Computers 38, 1645–1658 (2022). https://doi.org/10.1007/s00366-020-01119-3

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