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A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

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Abstract

Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications.

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References

  1. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996

    Google Scholar 

  2. Latifi N, Rashid ASA, Siddiqua S, Majid MZA (2016) Strength measurement and textural characteristics of tropical residual soil stabilised with liquid polymer. Measurement 91:46–54

    Google Scholar 

  3. Moayedi H, Huat BB, Kazemian S, Asadi A (2010) Optimization of tension absorption of geosynthetics through reinforced slope. Electron J Geotech Eng B 15:1–12

    Google Scholar 

  4. Marto A, Latifi N, Janbaz M, Kholghifard M, Khari M, Alimohammadi P, Banadaki AD (2012) Foundation size effect on modulus of subgrade reaction on sandy soils. Electron J Geotech Eng 17:2523–2530

    Google Scholar 

  5. Moayedi H, Huat BK, Kazemian S, Asadi A (2010) Optimization of shear behavior of reinforcement through the reinforced slope. Electron J Geotech Eng 15:93–104

    Google Scholar 

  6. Raftari M, Kassim KA, Rashid ASA, Moayedi H (2013) Settlement of shallow foundations near reinforced slopes. Electron J Geotech Eng 18:797–808

    Google Scholar 

  7. Nazir R, Ghareh S, Mosallanezhad M, Moayedi H (2016) The influence of rainfall intensity on soil loss mass from cellular confined slopes. Measurement 81:13–25

    Google Scholar 

  8. Javankhoshdel S, Bathurst RJ (2014) Simplified probabilistic slope stability design charts for cohesive and cohesive-frictional (c − ϕ) soils. Can Geotech J 51:1033–1045

    Google Scholar 

  9. Duncan JM (1996) State of the art: limit equilibrium and finite-element analysis of slopes. J Geotech Eng 122:577–596

    Google Scholar 

  10. Kang F, Xu B, Li J, Zhao S (2017) Slope stability evaluation using Gaussian processes with various covariance functions. Appl Soft Comput 60:387–396

    Google Scholar 

  11. Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136

    Google Scholar 

  12. Hervás-Martínez C, Gutiérrez PA, Peñá-Barragán JM, Jurado-Expósito M, López-Granados F (2010) A logistic radial basis function regression method for discrimination of cover crops in olive orchards. Expert Syst Appl 37:8432–8444

    Google Scholar 

  13. Basarir H, Kumral M, Karpuz C, Tutluoglu L (2010) Geostatistical modeling of spatial variability of SPT data for a borax stockpile site. Eng Geol 114:154–163

    Google Scholar 

  14. Secci R, Foddis ML, Mazzella A, Montisci A, Uras G (2015) Artificial neural networks and kriging method for slope geomechanical characterization, engineering, geology for society and territory-volume 2. Springer, Switzerland , pp 1357–1361

    Google Scholar 

  15. Zhang Y, Dai M, Ju Z (2015) Preliminary discussion regarding SVM kernel function selection in the twofold rock slope prediction model. J Comput Civil Eng 30:04015031

    Google Scholar 

  16. Moayedi H, Nazir R, Mosallanezhad M, Noor RBM, Khalilpour M (2018) Lateral deflection of piles in a multilayer soil medium. Case study: the terengganu seaside platform. Measurement 123:185–192

    Google Scholar 

  17. Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10:479

    Google Scholar 

  18. Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34:339–345

    Google Scholar 

  19. Gao W, Karbasi M, Derakhsh AM, Jalili A (2019) Development of a novel soft-computing framework for the simulation aims: a case study. Eng Comput 35:315–322

    Google Scholar 

  20. Youssef AM, Pradhan B, Al-Harthi SG (2015) Assessment of rock slope stability and structurally controlled failures along Samma escarpment road, Asir Region (Saudi Arabia). Arab J Geosci 8:6835–6852

    Google Scholar 

  21. Acharyya R, Dey A (2018) Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput Appl 31:1–14

    Google Scholar 

  22. 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

    Google Scholar 

  23. Singh J, Banka H, Verma AK (2019) A BBO-based algorithm for slope stability analysis by locating critical failure surface. Neural Comput Appl 31:1–18

    Google Scholar 

  24. Jellali B, Frikha W (2017) Constrained particle swarm optimization algorithm applied to slope stability. Int J Geomech 17:06017022

    Google Scholar 

  25. Pei H, Zhang S, Borana L, Zhao Y, Yin J (2019) Slope stability analysis based on real-time displacement measurements. Measurement 131:686–693

    Google Scholar 

  26. Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10:385

    Google Scholar 

  27. Ließ M, Glaser B, Huwe B (2011) Functional soil-landscape modelling to estimate slope stability in a steep Andean mountain forest region. Geomorphology 132:287–299

    Google Scholar 

  28. Gao W, Raftari M, Rashid ASA, Mu’azu MA, Jusoh WAW (2019) A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes. Eng Comput 35:1–20

    Google Scholar 

  29. Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984

    Google Scholar 

  30. Yuan C, 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 36:1–10

    Google Scholar 

  31. Nguyen H, Mehrabi M, Kalantar B, Moayedi H, MaM Abdullahi (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomat Na Hazards Risk 10:1667–1693

    Google Scholar 

  32. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    MathSciNet  MATH  Google Scholar 

  33. Committee AT (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5:124–137

    Google Scholar 

  34. Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK (2019) Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manag 183:137–148

    Google Scholar 

  35. Xi W, Li G, Moayedi H, Nguyen H (2019) A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China. Geomat Nat Hazards Risk 10:1750–1771

    Google Scholar 

  36. Yuan C, Moayedi H (2019) Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Eng Comput 36:1–11

    Google Scholar 

  37. Wang B, Moayedi H, Nguyen H, Foong LK, Rashid ASA (2019) Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Eng Comput 36:1–10

    Google Scholar 

  38. Liu L, Moayedi H, Rashid ASA, Rahman SSA, Nguyen H (2019) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput 35:1–13

    Google Scholar 

  39. Moayedi H, Huat BB, Mohammad Ali TA, Asadi A, Moayedi F, Mokhberi M (2011) Preventing landslides in times of rainfall: case study and FEM analyses. Disaster Prevent Manag Int J 20:115–124

    Google Scholar 

  40. Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 31:327–336

    Google Scholar 

  41. Moayedi H, Hayati S (2018) Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int J Geomech 18:06018009

    Google Scholar 

  42. Seyedashraf O, Mehrabi M, Akhtari AA (2018) Novel approach for dam break flow modeling using computational intelligence. J Hydrol 559:1028–1038

    Google Scholar 

  43. Gao W, Dimitrov D, Abdo H (2018) Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discrete Contin Dyn Syst S 12:711–721

    MathSciNet  MATH  Google Scholar 

  44. Gao W, Guirao JLG, Abdel-Aty M, Xi W (2019) An independent set degree condition for fractional critical deleted graphs. Discrete Contin Dyn Syst S 12:877–886

    MathSciNet  MATH  Google Scholar 

  45. Gao W, Guirao JLG, Basavanagoud B, Wu J (2018) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58

    MathSciNet  MATH  Google Scholar 

  46. Gao W, Wang W, Dimitrov D, Wang Y (2018) Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem 11:793–801

    Google Scholar 

  47. Gao W, Wu H, Siddiqui MK, Baig AQ (2018) Study of biological networks using graph theory. Saudi J Biol Sci 25:1212–1219

    Google Scholar 

  48. Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in land cover classification. Int J Remote Sens 24:4907–4938

    Google Scholar 

  49. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Google Scholar 

  50. Bao X, Jia H, Lang C (2019) A novel hybrid harris hawks optimization for color image multilevel thresholding segmentation. IEEE Access 7:76529–76546

    Google Scholar 

  51. Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens 11:1421

    Google Scholar 

  52. Du P, Wang J, Hao Y, Niu T, Yang W (2019) A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2. 5 and PM10 forecasting. arXiv preprint arXiv:1905.13550

  53. Krabbenhoft K, Lyamin A, Krabbenhoft J (2015) Optum computational engineering (Optum G2). Available on: www.optumce.com. Accessed 2018

  54. Allayear SM, Sarker K, Ara SJF (2018) Prediction model for prevalence of type-2 diabetes complications with ANN approach combining with K-fold cross validation and K-means clustering. Advances in information and communication networks: proceedings of the 2018 future of information and communication conference (FICC), San Francisco, United States

  55. Nandi GC, Agarwal P, Gupta P, Singh A (2018) Deep learning based intelligent robot grasping strategy. 14th International conference on control and automation (ICCA), Anchorage, AK, USA

  56. Abas M, Zubir N, Ismail N, Yassin I, Ali N, Rahiman M, Saiful N, Taib M (2017) Agarwood oil quality classifier using machine learning. J Fund Appl Sci 9:62–76

    Google Scholar 

  57. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303–314

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

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Authors and Affiliations

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

    Hossein Moayedi

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

    Hossein Moayedi

  3. Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA

    Abdolreza Osouli

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

    Hoang Nguyen

  5. Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ahmad Safuan A. Rashid

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  1. Hossein Moayedi
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  2. Abdolreza Osouli
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  4. Ahmad Safuan A. Rashid
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Correspondence to Hossein Moayedi.

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Moayedi, H., Osouli, A., Nguyen, H. et al. A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Engineering with Computers 37, 369–379 (2021). https://doi.org/10.1007/s00366-019-00828-8

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