Skip to main content

Advertisement

SpringerLink
Log in

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (Fy) and undrained shear strength of the cohesive soil (Cu) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R2) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. 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. https://doi.org/10.1007/s00366-019-00791-4

    Article  Google Scholar 

  2. Moayedi H, Osouli A, Nguyen H, Rashid ASA (2019) A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput 35:1–11. https://doi.org/10.1007/s00366-019-00828-8

    Article  Google Scholar 

  3. Fan J, Jiang D, Liu W, Wu F, Chen J, Daemen J (2019) Discontinuous fatigue of salt rock with low-stress intervals. Int J Rock Mech Min Sci 115:77–86. https://doi.org/10.1016/j.ijrmms.2019.01.013

    Article  Google Scholar 

  4. Liu W, Zhang Z, Chen J, Fan J, Jiang D, Jjk D, Li Y (2019) Physical simulation of construction and control of two butted-well horizontal cavern energy storage using large molded rock salt specimens. Energy 185:682–694. https://doi.org/10.1016/j.energy.2019.07.014

    Article  Google Scholar 

  5. Zhang Z, Jiang D, Liu W, Chen J, Li E, Fan J, Xie K (2019) Study on the mechanism of roof collapse and leakage of horizontal cavern in thinly bedded salt rocks. Environ Earth Sci 78:292. https://doi.org/10.1007/s12665-019-8292-2

    Article  Google Scholar 

  6. Liu W, Zhang ZX, Fan JY, Jiang DY, Daemen JJK (2020) Research on the stability and treatments of natural gas storage caverns with different shapes in bedded salt rocks. IEEE Access 8:000507. https://doi.org/10.1109/ACCESS.2020.2967078

    Article  Google Scholar 

  7. Mei DP (2017) Structural health monitoring-based dynamic behavior evaluation of a long-span high-speed railway bridge. Smart Struct Syst 20:197–205. https://doi.org/10.12989/sss.2017.20.2.197

    Article  Google Scholar 

  8. Luo Z, Bui X-N, Nguyen H, Moayedi H (2019) A novel artificial intelligence technique for analyzing slope stability using PSO-CA model. Eng Comput. https://doi.org/10.1007/s00366-019-00839-5

    Article  Google Scholar 

  9. Moayedi H, Tien Bui D, Kok Foong L (2019) Slope stability monitoring using novel remote sensing based fuzzy logic. Sensors 19:4636

    Article  Google Scholar 

  10. 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 

  11. Jellali B, Frikha W (2017) Constrained particle swarm optimization algorithm applied to slope stability. Int J Geomech 17:06017022. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001019

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  13. Qiao W, Lu H, Zhou G, Azimi M, Yang Q, Tian W (2020) A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer. J Clean Prod 244:118612. https://doi.org/10.1016/j.jclepro.2019.118612

    Article  Google Scholar 

  14. Chen J, Lu D, Liu W, Fan J, Jiang D, Yi L, Kang Y (2020) Stability study and optimization design of small-spacing two-well (SSTW) salt caverns for natural gas storages. J Energy Storage 27:101131. https://doi.org/10.1016/j.est.2019.101131

    Article  Google Scholar 

  15. Qiao W, Tian W, Tian Y, Yang Q, Wang Y, Zhang J (2019) The forecasting of PM2.5 using a hybrid model based on wavelet transform and an improved deep learning algorithm. IEEE Access 7:142814–142825. https://doi.org/10.1109/ACCESS.2019.2944755

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Qiao W, Yang Z (2019) Solving large-scale function optimization problem by using a new metaheuristic algorithm based on quantum dolphin swarm algorithm. IEEE Access 7:138972–138989. https://doi.org/10.1109/ACCESS.2019.2942169

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  19. Khosravi R, Rabiei S, Bahiraei M, Teymourtash AR (2019) Predicting entropy generation of a hybrid nanofluid containing graphene–platinum nanoparticles through a microchannel liquid block using neural networks. Int Commun Heat Mass Transf 109:104351. https://doi.org/10.1016/j.icheatmasstransfer.2019.104351

    Article  Google Scholar 

  20. Amani M, Amani P, Bahiraei M, Wongwises S (2019) Prediction of hydrothermal behavior of a non-Newtonian nanofluid in a square channel by modeling of thermophysical properties using neural network. J Therm Anal Calorim 135:901–910. https://doi.org/10.1007/s10973-018-7303-y

    Article  Google Scholar 

  21. Hemmat Esfe M, Bahiraei M, Mahian O (2018) Experimental study for developing an accurate model to predict viscosity of CuO–ethylene glycol nanofluid using genetic algorithm based neural network. Powder Technol 338:383–390. https://doi.org/10.1016/j.powtec.2018.07.013

    Article  Google Scholar 

  22. Qiao W, Yang Z (2020) An improved dolphin swarm algorithm based on Kernel Fuzzy C-means in the application of solving the optimal problems of large-scale function. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2958456

    Article  Google Scholar 

  23. Qiao W, Yang Z (2019) Modified dolphin swarm algorithm based on chaotic maps for solving high-dimensional function optimization problems. IEEE Access 7:110472–110486. https://doi.org/10.1109/ACCESS.2019.2931910

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Qiao W, Yang Z (2019) Forecast the electricity price of US using a wavelet transform-based hybrid model. Energy. https://doi.org/10.1016/j.energy.2019.116704

    Article  Google Scholar 

  26. Yin ZY, Jin YF, Shen JS, Hicher PY (2018) Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. Int J Numer Anal Meth Geomech 42:70–94. https://doi.org/10.1002/nag.2714

    Article  Google Scholar 

  27. 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. https://doi.org/10.1016/j.jclepro.2020.120082

    Article  Google Scholar 

  28. Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine. Vietnam Neural Comput Appl 31:1–17. https://doi.org/10.1007/s00521-018-3717-5

    Article  Google Scholar 

  29. Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2019) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput 35:1–12

    Article  Google Scholar 

  30. Mola-Abasi H, Eslami A, Shourijeh PT (2013) Shear wave velocity by polynomial neural networks and genetic algorithms based on geotechnical soil properties. Arab J Sci Eng 38:829–838. https://doi.org/10.1007/s13369-012-0525-6

    Article  Google Scholar 

  31. Song ZP, Ren SB, Guo ZC (2011) The tunnel surrounding rock parameters identification method based on PSO–ANN. In: Zhou XJ (ed), Advances in structural engineering, Pts 1–3. Trans Tech Publications Ltd, Durnten-Zurich, pp 637+

  32. Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO–ANN model for predicting surface settlement caused by tunneling. Eng Comput 32:705–715. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  34. Hebb D (1949) The organization of behavior: a neurophysiological approach. Wiley, Hoboken

    Google Scholar 

  35. Qiao W, Huang K, Azimi M, Han S (2019) A novel hybrid prediction model for hourly gas consumption in supply side based on improved whale optimization algorithm and relevance vector machine. IEEE Access 7:88218–88230. https://doi.org/10.1109/ACCESS.2019.2918156

    Article  Google Scholar 

  36. Moayedi H, Tien Bui D, Gör M, Pradhan B, Jaafari A (2019) 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-Inf 8:391

    Article  Google Scholar 

  37. Qiao W, Yang Z, Kang Z, Pan Z (2020) Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm. Eng Appl Artif Intell 87:103323. https://doi.org/10.1016/j.engappai.2019.103323

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  39. Bui DT, Moayedi H, Gör M, Jaafari A, Foong LK (2019) Predicting slope stability failure through machine learning paradigms. ISPRS Int J Geo-Inf 8:395

    Article  Google Scholar 

  40. Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085–1098

    Article  Google Scholar 

  41. Wang S-C (2003) Artificial neural network, interdisciplinary computing in java programming. Springer, New York, pp 81–100

    Book  Google Scholar 

  42. Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Van Phong T, Ly H-B, Le T-T, Trinh PT (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena 188:104451. https://doi.org/10.1016/j.catena.2019.104451

    Article  Google Scholar 

  43. Jaafari A (2018) LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process. Environ Earth Sci 77:42. https://doi.org/10.1007/s12665-017-7207-3

    Article  Google Scholar 

  44. Bayat M, Ghorbanpour M, Zare R, Jaafari A, Pham BT (2019) Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput Electron Agric 164:104929. https://doi.org/10.1016/j.compag.2019.104929

    Article  Google Scholar 

  45. 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 

  46. Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl 31:1–17. https://doi.org/10.1007/s00521-018-3555-5

    Article  Google Scholar 

  47. Qin S, Zhou Y-L, Cao H, Wahab MA (2018) Model updating in complex bridge structures using kriging model ensemble with genetic algorithm. KSCE J Civ Eng 22:3567–3578. https://doi.org/10.1007/s12205-017-1107-7

    Article  Google Scholar 

  48. 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. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  49. Moayedi H, Aghel B, Vaferi B, Foong LK, Bui DT (2019) The feasibility of Levenberg–Marquardt algorithm combined with imperialist competitive computational method predicting drag reduction in crude oil pipelines. J Petrol Sci Eng 185:106634. https://doi.org/10.1016/j.petrol.2019.106634

    Article  Google Scholar 

  50. Moayedi H, Mu’azu MA, Kok Foong L (2019) Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles. Eng Comput. https://doi.org/10.1007/s00366-019-00885-z

    Article  Google Scholar 

  51. Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2:311–319. https://doi.org/10.1007/s12517-009-0035-3

    Article  Google Scholar 

  52. Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10:11. https://doi.org/10.1007/s12517-017-3167-x

    Article  Google Scholar 

  53. Nazir R, Moayedi H (2014) Soil mass loss reduction during rainfalls by reinforcing the slopes with the surficial confinement.

  54. Abusharar SW, Han J (2011) Two-dimensional deep-seated slope stability analysis of embankments over stone column-improved soft clay. Eng Geol 120:103–110. https://doi.org/10.1016/j.enggeo.2011.04.002

    Article  Google Scholar 

  55. 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

    Article  Google Scholar 

  56. Latifi N, Marto A, Rashid ASA, Yii JLJ (2015) Strength and physico-chemical characteristics of fly ash-bottom ash mixture. Arab J Sci Eng 40:2447–2455. https://doi.org/10.1007/s13369-015-1647-4

    Article  Google Scholar 

  57. Latifi N, Rashid ASA, Siddiqua S, Abd Majid MZ (2016) Strength measurement and textural characteristics of tropical residual soil stabilised with liquid polymer. Measurement 91:46–54. https://doi.org/10.1016/j.measurement.2016.05.029

    Article  Google Scholar 

  58. Moayedi H, Armaghani DJ (2017) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:347–356. https://doi.org/10.1007/s00366-017-0545-7

    Article  Google Scholar 

  59. 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. https://doi.org/10.1007/s00366-018-0644-0

    Article  Google Scholar 

  60. Bunawan AR, Momeni E, Armaghani DJ, Rashid ASA (2018) Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil–cement columns. Measurement 124:529–538

    Article  Google Scholar 

  61. Alsarraf A, Moayedi H, Rashid ASA, Muazu MA, Shahsavar A (2019) Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system. Eng Comput 35:1–14. https://doi.org/10.1007/s00366-019-00721-4

    Article  Google Scholar 

  62. Moayedi H, Foong LK, Nguyen H, Bui DT, Jusoh WAW, Rashid ASA (2019) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput 35:1–16. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  63. 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

    Article  Google Scholar 

  64. Nguyen H, Bui X-N, Nguyen-Thoi T, Ragam P, Moayedi H (2019) Toward a state-of-the-art of fly-rock prediction technology in open-pit mines using EANNs model. Appl Sci 9:4554

    Article  Google Scholar 

  65. Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environ Earth Sci 78:479. https://doi.org/10.1007/s12665-019-8491-x

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Bilingual Teaching Programme of Hainan University (hndsyk 201909); the Key project of the National Social Science Foundation of the year 2018 (18AJY013); the 2017 National Social Science foundation project (17CJY072);the 2018 planning project of philosophy and social science of Zhejiang province (18NDJC086YB); the 2018 Fujian Social Science Planning Project (FJ2018B067); The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019 (19YJA790102).

Author information

Authors and Affiliations

  1. School of Tourism, Hainan University, Haikou, 570228, Hainan, China

    Hong Wang

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

    Hossein Moayedi

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

    Hossein Moayedi

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

    Loke Kok Foong

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

    Loke Kok Foong

Authors
  1. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Moayedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Loke Kok Foong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Moayedi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Moayedi, H. & Kok Foong, L. Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design. Engineering with Computers 37, 3067–3078 (2021). https://doi.org/10.1007/s00366-020-00957-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-00957-5

Keywords

Search

Navigation