Skip to main content
SpringerLink
Log in

Slope stability evaluation using neural network optimized by equilibrium optimization and vortex search algorithm

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

Abstract

A dependable evaluation of the stability of slopes is a prerequisite in many construction projects. Although machine learning models have been satisfactorily used for this purpose, combining them with metaheuristic optimizers has resulted in a larger accuracy. This study, therefore, suggests the use of equilibrium optimization (EO) and vortex search algorithm (VSA) for optimizing a multi-layer perceptron neural network (MLPNN) employed to anticipate the factor of safety of a single-layer soil slope. Two hybrid models, as well as the regular MLPNN, are fed by a total of 630 data acquired from finite element simulations. The results, first, showed the applicability of artificial intelligence in this field. Next, reducing the training root mean square error (RMSE) of the MLPNN (from 0.4715 to 0.3891 and 0.4383 by the EO and VSA, respectively) revealed the efficiency of the used algorithms in remedying the computational weaknesses of this model. Moreover, the testing RMSE declined from 0.5397 to 0.4129 and 0.5155, which indicates a higher generalization ability of the hybrid models. Furthermore, due to the larger accuracy of the EO-based ensemble, this algorithm outperformed the VSA in optimizing the MLPNN.

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

Similar content being viewed by others

References

  1. 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

    Article  Google Scholar 

  2. Zhang K, Wang Q, Chao L, Ye J, Li Z, Yu Z, Yang T, Ju Q (2019) Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. J Hydrol 574:903–914. https://doi.org/10.1016/j.jhydrol.2019.04.087

    Article  Google Scholar 

  3. Zhang S, Zhang J, Ma Y, Pak RYS (2020) Vertical dynamic interactions of poroelastic soils and embedded piles considering the effects of pile-soil radial deformations. Soil Found. https://doi.org/10.1016/j.sandf.2020.10.003

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Eberhardt E (2003) Rock slope stability analysis–utilization of advanced numerical techniques. University of British Columbia, Canada

  6. Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64:201–210

    Article  Google Scholar 

  7. Abdalla JA, Attom MF, Hawileh R (2015) Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environ Earth Sci 73:5463–5477

    Article  Google Scholar 

  8. Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73:787–804

    Article  Google Scholar 

  9. Piotrowski AP, Osuch M, Napiorkowski MJ, Rowinski PM, Napiorkowski JJ (2014) Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river. Comput Geosci 64:136–151

    Article  Google Scholar 

  10. Liu J, Wu C, Wu G, Wang X (2015) A novel differential search algorithm and applications for structure design. Appl Math Comput 268:246–269

    MATH  Google Scholar 

  11. Feng S, Lu H, Tian P, Xue Y, Lu J, Tang M, Feng W (2020) Analysis of microplastics in a remote region of the Tibetan Plateau: implications for natural environmental response to human activities. Sci Total Environ 739:140087. https://doi.org/10.1016/j.scitotenv.2020.140087

    Article  Google Scholar 

  12. Fu X, Fortino G, Pace P, Aloi G, Li W (2020) Environment-fusion multipath routing protocol for wireless sensor networks. Inf Fusion 53:4–19. https://doi.org/10.1016/j.inffus.2019.06.001

    Article  Google Scholar 

  13. Han X, Zhang D, Yan J, Zhao S, Liu J (2020) Process development of flue gas desulphurization wastewater treatment in coal-fired power plants towards zero liquid discharge: energetic, economic and environmental analyses. J Clean Prod 261:121144. https://doi.org/10.1016/j.jclepro.2020.121144

    Article  Google Scholar 

  14. He L, Chen Y, Zhao H, Tian P, Xue Y, Chen L (2018) Game-based analysis of energy-water nexus for identifying environmental impacts during shale gas operations under stochastic input. Sci Total Environ 627:1585–1601. https://doi.org/10.1016/j.scitotenv.2018.02.004

    Article  Google Scholar 

  15. He L, Shen J, Zhang Y (2018) Ecological vulnerability assessment for ecological conservation and environmental management. J Environ Manag 206:1115–1125. https://doi.org/10.1016/j.jenvman.2017.11.059

    Article  Google Scholar 

  16. Liu J, Liu Y, Wang X (2020) An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou. Environ Sci Pollut Res 27:37237–37259. https://doi.org/10.1007/s11356-019-07107-5

    Article  Google Scholar 

  17. Liu Y, Yang C, Sun Q (2020) Thresholds based image extraction schemes in big data environment in intelligent traffic management. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2994386

    Article  Google Scholar 

  18. Liu L, Li J, Yue F, Yan X, Wang F, Bloszies S, Wang Y (2018) Effects of arbuscular mycorrhizal inoculation and biochar amendment on maize growth, cadmium uptake and soil cadmium speciation in Cd-contaminated soil. Chemosphere 194:495–503. https://doi.org/10.1016/j.chemosphere.2017.12.025

    Article  Google Scholar 

  19. Yang Y, Liu J, Yao J, Kou J, Li Z, Wu T, Zhang K, Zhang L, Sun H (2020) Adsorption behaviors of shale oil in kerogen slit by molecular simulation. Chem Eng J 387:124054. https://doi.org/10.1016/j.cej.2020.124054

    Article  Google Scholar 

  20. Hu X, Chong H-Y, Wang X (2019) Sustainability perceptions of off-site manufacturing stakeholders in Australia. J Clean Prod 227:346–354. https://doi.org/10.1016/j.jclepro.2019.03.258

    Article  Google Scholar 

  21. Ghaemi A, Rezaie-Balf M, Adamowski J, Kisi O, Quilty J (2019) On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric For Meteorol 278:107647

    Article  Google Scholar 

  22. Keshtegar B, Heddam S, Sebbar A, Zhu S-P, Trung N-T (2019) SVR–RSM: a hybrid heuristic method for modeling monthly pan evaporation. Environ Sci Pollut Res 26:35807–35826

    Article  Google Scholar 

  23. Kisi O, Heddam S (2019) Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol Sci J 64:653–672

    Article  Google Scholar 

  24. Roy DK, Barzegar R, Quilty J, Adamowski J (2020) Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. J Hydrol 591:125509. https://doi.org/10.1016/j.jhydrol.2020.125509

    Article  Google Scholar 

  25. Zhang B, Xu D, Liu Y, Li F, Cai J, Du L (2016) Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agric For Meteorol 216:1–12. https://doi.org/10.1016/j.agrformet.2015.09.015

    Article  Google Scholar 

  26. Chao L, Zhang K, Li Z, Zhu Y, Wang J, Yu Z (2018) Geographically weighted regression based methods for merging satellite and gauge precipitation. J Hydrol 558:275–289. https://doi.org/10.1016/j.jhydrol.2018.01.042

    Article  Google Scholar 

  27. Chen Y, He L, Guan Y, Lu H, Li J (2017) Life cycle assessment of greenhouse gas emissions and water-energy optimization for shale gas supply chain planning based on multi-level approach: case study in Barnett, Marcellus, Fayetteville, and Haynesville shales. Energy Convers Manag 134:382–398. https://doi.org/10.1016/j.enconman.2016.12.019

    Article  Google Scholar 

  28. He L, Chen Y, Li J (2018) A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains. Resour Conserv Recycl 133:206–228. https://doi.org/10.1016/j.resconrec.2018.02.015

    Article  Google Scholar 

  29. Lu H, Tian P, He L (2019) Evaluating the global potential of aquifer thermal energy storage and determining the potential worldwide hotspots driven by socio-economic, geo-hydrologic and climatic conditions. Renew Sustain Energy Rev 112:788–796. https://doi.org/10.1016/j.rser.2019.06.013

    Article  Google Scholar 

  30. Wang Y, Yao M, Ma R, Yuan Q, Yang D, Cui B, Ma C, Liu M, Hu D (2020) Design strategy of barium titanate/polyvinylidene fluoride-based nanocomposite films for high energy storage. J Mater Chem A 8:884–917. https://doi.org/10.1039/C9TA11527G

    Article  Google Scholar 

  31. Zhang W (2020) Parameter adjustment strategy and experimental development of hydraulic system for wave energy power generation. Symmetry 12:711

    Article  Google Scholar 

  32. Zhao X, Ye Y, Ma J, Shi P, Chen H (2020) Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-09094-4

    Article  Google Scholar 

  33. Zhu L, Kong L, Zhang C (2020) Numerical study on hysteretic behaviour of horizontal-connection and energy-dissipation structures developed for prefabricated shear walls. Appl Sci 10:1240. https://doi.org/10.3390/app10041240

    Article  Google Scholar 

  34. Deng Y, Zhang T, Sharma BK, Nie H (2019) Optimization and mechanism studies on cell disruption and phosphorus recovery from microalgae with magnesium modified hydrochar in assisted hydrothermal system. Sci Total Environ 646:1140–1154. https://doi.org/10.1016/j.scitotenv.2018.07.369

    Article  Google Scholar 

  35. Zhang T, Wu X, Fan X, Tsang DCW, Li G, Shen Y (2019) Corn waste valorization to generate activated hydrochar to recover ammonium nitrogen from compost leachate by hydrothermal assisted pretreatment. J Environ Manag 236:108–117. https://doi.org/10.1016/j.jenvman.2019.01.018

    Article  Google Scholar 

  36. Yang C, Gao F, Dong M (2020) Energy efficiency modeling of integrated energy system in coastal areas. J Coast Res 103:995–1001. https://doi.org/10.2112/SI103-207.1

    Article  Google Scholar 

  37. Li Z-G, Cheng H, Gu T-Y (2019) Research on dynamic relationship between natural gas consumption and economic growth in China. Struct Change Econ Dyn 49:334–339. https://doi.org/10.1016/j.strueco.2018.11.006

    Article  Google Scholar 

  38. Liu E, Lv L, Yi Y, Xie P (2019) Research on the steady operation optimization model of natural gas pipeline considering the combined operation of air coolers and compressors. IEEE Access 7:83251–83265. https://doi.org/10.1109/ACCESS.2019.2924515

    Article  Google Scholar 

  39. Su Z, Liu E, Xu Y, Xie P, Shang C, Zhu Q (2019) Flow field and noise characteristics of manifold in natural gas transportation station. Oil Gas Sci Technol Revue d’IFP Energies Nouvelles 74:70. https://doi.org/10.2516/ogst/2019038

    Article  Google Scholar 

  40. Liu E, Guo B, Lv L, Qiao W, Azimi M (2020) Numerical simulation and simplified calculation method for heat exchange performance of dry air cooler in natural gas pipeline compressor station. Energy Sci Eng 8:2256–2270. https://doi.org/10.1002/ese3.661

    Article  Google Scholar 

  41. Liu E, Wang X, Zhao W, Su Z, Chen Q (2020) Analysis and research on pipeline vibration of a natural gas compressor station and vibration reduction measures. Energy Fuels. https://doi.org/10.1021/acs.energyfuels.0c03663

    Article  Google Scholar 

  42. Peng S, Chen Q, Zheng C, Liu E (2020) Analysis of particle deposition in a new-type rectifying plate system during shale gas extraction. Energy Sci Eng 8:702–717. https://doi.org/10.1002/ese3.543

    Article  Google Scholar 

  43. Chen Y, He L, Li J, Zhang S (2018) Multi-criteria design of shale-gas–water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty. Comput Chem Eng 109:216–235. https://doi.org/10.1016/j.compchemeng.2017.11.014

    Article  Google Scholar 

  44. Chen Y, Li J, Lu H, Yan P (2021) Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas–water supply chains. J Clean Prod 278:123209. https://doi.org/10.1016/j.jclepro.2020.123209

    Article  Google Scholar 

  45. Cheng X, He L, Lu H, Chen Y, Ren L (2016) Optimal water resources management and system benefit for the Marcellus shale-gas reservoir in Pennsylvania and West Virginia. J Hydrol 540:412–422. https://doi.org/10.1016/j.jhydrol.2016.06.041

    Article  Google Scholar 

  46. He L, Shao F, Ren L (2020) Sustainability appraisal of desired contaminated groundwater remediation strategies: an information-entropy-based stochastic multi-criteria preference model. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00650-z

    Article  Google Scholar 

  47. Li X, Zhang R, Zhang X, Zhu P, Yao T (2020) Silver-catalyzed decarboxylative allylation of difluoroarylacetic acids with allyl sulfones in water. Chem Asian J 15:1175–1179. https://doi.org/10.1002/asia.202000059

    Article  Google Scholar 

  48. Alam Z, Sun L, Zhang C, Su Z, Samali B (2021) Experimental and numerical investigation on the complex behaviour of the localised seismic response in a multi-storey plan-asymmetric structure. Struct Infrastruct Eng 17:86–102. https://doi.org/10.1080/15732479.2020.1730914

    Article  Google Scholar 

  49. Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24:6062–6071. https://doi.org/10.1109/TIP.2015.2491020

    Article  MathSciNet  MATH  Google Scholar 

  50. Yang W, Zhao Y, Wang D, Wu H, Lin A, He L (2020) Using principal components analysis and IDW interpolation to determine spatial and temporal changes of surface water quality of Xin’anjiang River in Huangshan, China. Int J Environ Res Public Health 17:2942

    Article  Google Scholar 

  51. Jia L, Liu B, Zhao Y, Chen W, Mou D, Fu J, Wang Y, Xin W, Zhao L (2020) Structure design of MoS2@Mo2C on nitrogen-doped carbon for enhanced alkaline hydrogen evolution reaction. J Mater Sci 55:16197–16210. https://doi.org/10.1007/s10853-020-05107-2

    Article  Google Scholar 

  52. Lyu Z, Chai J, Xu Z, Qin Y, Cao J (2019) A comprehensive review on reasons for tailings dam failures based on case history. Adv Civ Eng. https://doi.org/10.1155/2019/4159306

    Article  Google Scholar 

  53. Feng W, Lu H, Yao T, Yu Q (2020) Drought characteristics and its elevation dependence in the Qinghai–Tibet plateau during the last half-century. Sci Rep 10:14323. https://doi.org/10.1038/s41598-020-71295-1

    Article  Google Scholar 

  54. Xu M, Li C, Zhang S, Callet PL (2020) State-of-the-art in 360° video/image processing: perception, assessment and compression. IEEE J Sel Top Signal Process 14:5–26. https://doi.org/10.1109/JSTSP.2020.2966864

    Article  Google Scholar 

  55. Zenggang X, Zhiwen T, Xiaowen C, Xue-min Z, Kaibin Z, Conghuan Y (2019) Research on image retrieval algorithm based on combination of color and shape features. J Signal Process Syst. https://doi.org/10.1007/s11265-019-01508-y

    Article  Google Scholar 

  56. Zhu Q (2020) Research on road traffic situation awareness system based on image big data. IEEE Intell Syst 35:18–26. https://doi.org/10.1109/MIS.2019.2942836

    Article  Google Scholar 

  57. Xu S, Wang J, Shou W, Ngo T, Sadick A-M, Wang X (2020) Computer vision techniques in construction: a critical review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09504-3

    Article  Google Scholar 

  58. Yan J, Pu W, Zhou S, Liu H, Bao Z (2020) Collaborative detection and power allocation framework for target tracking in multiple radar system. Inf Fusion 55:173–183. https://doi.org/10.1016/j.inffus.2019.08.010

    Article  Google Scholar 

  59. Chao M, Kai C, Zhiwei Z (2020) Research on tobacco foreign body detection device based on machine vision. Trans Inst Meas Control 42:2857–2871. https://doi.org/10.1177/0142331220929816

    Article  Google Scholar 

  60. Liu D, Wang S, Huang D, Deng G, Zeng F, Chen H (2016) Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput Biol Med 72:185–200. https://doi.org/10.1016/j.compbiomed.2016.03.010

    Article  Google Scholar 

  61. Zhang X, Jiang R, Wang T, Wang J (2020) Recursive neural network for video deblurring. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2020.3035722

    Article  Google Scholar 

  62. Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I (2013) Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling. Theor Biol Med Model 10(1). https://doi.org/10.1186/1742-4682-10-57

  63. Zhang K, Ruben GB, Li X, Li Z, Yu Z, Xia J, Dong Z (2020) A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: a case study in a typical semi-arid North China basin. Environ Model Softw 128:104704. https://doi.org/10.1016/j.envsoft.2020.104704

    Article  Google Scholar 

  64. Qian J, Feng S, Li Y, Tao T, Han J, Chen Q, Zuo C (2020) Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt Lett 45:1842–1845

    Article  Google Scholar 

  65. Qian J, Feng S, Tao T, Hu Y, Li Y, Chen Q, Zuo C (2020) Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. APL Photonics 5:046105. https://doi.org/10.1063/5.0003217

    Article  Google Scholar 

  66. Yang W, Pudasainee D, Gupta R, Li W, Wang B, Sun L (2020) An overview of inorganic particulate matter emission from coal/biomass/MSW combustion: sampling and measurement, formation, distribution, inorganic composition and influencing factors. Fuel Process Technol. https://doi.org/10.1016/j.fuproc.2020.106657

    Article  Google Scholar 

  67. Zhang C-W, Ou J-P, Zhang J-Q (2006) Parameter optimization and analysis of a vehicle suspension system controlled by magnetorheological fluid dampers. Struct Control Health Monit 13:885–896. https://doi.org/10.1002/stc.63

    Article  Google Scholar 

  68. Yu H, Shen S, Qian G, Gong X (2020) Packing theory and volumetrics-based aggregate gradation design method. J Mater Civ Eng 32:04020110. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003192

    Article  Google Scholar 

  69. Zhang C, Wang H (2020) Swing vibration control of suspended structures using the active rotary inertia driver system: theoretical modeling and experimental verification. Struct Control Health Monit 27:e2543. https://doi.org/10.1002/stc.2543

    Article  Google Scholar 

  70. Sun Y, Wang J, Wu J, Shi W, Ji D, Wang X, Zhao X (2020) Constraints hindering the development of high-rise modular buildings. Appl Sci 10:7159. https://doi.org/10.3390/app10207159

    Article  Google Scholar 

  71. Abedini M, Mutalib AA, Zhang C, Mehrmashhadi J, Raman SN, Alipour R, Momeni T, Mussa MH (2020) Large deflection behavior effect in reinforced concrete columns exposed to extreme dynamic loads. Front Struct Civ Eng 14:532–553. https://doi.org/10.1007/s11709-020-0604-9

    Article  Google Scholar 

  72. Mou B, Li X, Bai Y, Wang L (2019) Shear behavior of panel zones in steel beam-to-column connections with unequal depth of outer annular stiffener. J Struct Eng 145:04018247. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002256

    Article  Google Scholar 

  73. Mou B, Zhao F, Qiao Q, Wang L, Li H, He B, Hao Z (2019) Flexural behavior of beam to column joints with or without an overlying concrete slab. Eng Struct 199:109616. https://doi.org/10.1016/j.engstruct.2019.109616

    Article  Google Scholar 

  74. Abedini M, Zhang C (2020) Performance assessment of concrete and steel material models in LS-DYNA for enhanced numerical simulation, a state of the art review. Archives of computational methods in engineering. Springer, New York. https://doi.org/10.1007/s11831-020-09483-5

    Book  Google Scholar 

  75. Gholipour G, Zhang C, Mousavi AA (2020) Numerical analysis of axially loaded RC columns subjected to the combination of impact and blast loads. Eng Struct 219:110924. https://doi.org/10.1016/j.engstruct.2020.110924

    Article  Google Scholar 

  76. Zhang C, Abedini M, Mehrmashhadi J (2020) Development of pressure-impulse models and residual capacity assessment of RC columns using high fidelity arbitrary Lagrangian–Eulerian simulation. Eng Struct 224:111219. https://doi.org/10.1016/j.engstruct.2020.111219

    Article  Google Scholar 

  77. Yue H, Wang H, Chen H, Cai K, Jin Y (2020) Automatic detection of feather defects using Lie group and fuzzy Fisher criterion for shuttlecock production. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2020.106690

    Article  Google Scholar 

  78. Zhu G, Wang S, Sun L, Ge W, Zhang X (2020) Output feedback adaptive dynamic surface sliding-mode control for quadrotor UAVs with tracking error constraints. Complexity. https://doi.org/10.1155/2020/8537198

    Article  MATH  Google Scholar 

  79. Xiong Q, Zhang X, Wang W-F, Gu Y (2020) A parallel algorithm framework for feature extraction of EEG signals on MPI. Comput Math Methods Med. https://doi.org/10.1155/2020/9812019

    Article  Google Scholar 

  80. Zhang H, Qu S, Li H, Luo J, Xu W (2020) A moving shadow elimination method based on fusion of multi-feature. IEEE Access 8:63971–63982. https://doi.org/10.1109/ACCESS.2020.2984680

    Article  Google Scholar 

  81. Zhang J, Liu B (2019) A review on the recent developments of sequence-based protein feature extraction methods. Curr Bioinform 14:190–199

    Article  Google Scholar 

  82. Zhang X, Fan M, Wang D, Zhou P, Tao D (2020) Top-k feature selection framework using robust 0–1 integer programming. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3009209

    Article  Google Scholar 

  83. Zhao X, Li D, Yang B, Chen H, Yang X, Yu C, Liu S (2015) A two-stage feature selection method with its application. Comput Electr Eng 47:114–125. https://doi.org/10.1016/j.compeleceng.2015.08.011

    Article  Google Scholar 

  84. Liu S, Chan FTS, Ran W (2016) Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl 55:37–47. https://doi.org/10.1016/j.eswa.2016.01.059

    Article  Google Scholar 

  85. Tian P, Lu H, Feng W, Guan Y, Xue Y (2020) Large decrease in streamflow and sediment load of Qinghai–Tibetan Plateau driven by future climate change: a case study in Lhasa River Basin. CATENA 187:104340. https://doi.org/10.1016/j.catena.2019.104340

    Article  Google Scholar 

  86. Wu C, Wu P, Wang J, Jiang R, Chen M, Wang X (2020) Critical review of data-driven decision-making in bridge operation and maintenance. Struct Infrastruct Eng. https://doi.org/10.1080/15732479.2020.1833946

    Article  Google Scholar 

  87. 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. https://doi.org/10.1007/s00521-018-3525-y

    Article  Google Scholar 

  88. 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

    Article  Google Scholar 

  89. Shi K, Wang J, Tang Y, Zhong S (2020) Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies. Fuzzy Sets Syst 381:1–25

    Article  MathSciNet  MATH  Google Scholar 

  90. Shi K, Wang J, Zhong S, Tang Y, Cheng J (2020) Non-fragile memory filtering of T–S fuzzy delayed neural networks based on switched fuzzy sampled-data control. Fuzzy Sets Syst 394:40–64. https://doi.org/10.1016/j.fss.2019.09.001

    Article  MathSciNet  MATH  Google Scholar 

  91. Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA (2020) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31:148–162. https://doi.org/10.1109/TNNLS.2019.2899936

    Article  Google Scholar 

  92. Adeli H (2001) Neural networks in civil engineering: 1989–2000. Computer Aided Civ Infrastruct Eng 16:126–142

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  94. Lv Z, Qiao L (2020) Deep belief network and linear perceptron based cognitive computing for collaborative robots. Appl Soft Comput 92:106300. https://doi.org/10.1016/j.asoc.2020.106300

    Article  Google Scholar 

  95. Xu M, Li T, Wang Z, Deng X, Yang R, Guan Z (2018) Reducing complexity of HEVC: a deep learning approach. IEEE Trans Image Process 27:5044–5059. https://doi.org/10.1109/TIP.2018.2847035

    Article  MathSciNet  Google Scholar 

  96. Li T, Xu M, Zhu C, Yang R, Wang Z, Guan Z (2019) A deep learning approach for multi-frame in-loop filter of HEVC. IEEE Trans Image Process 28:5663–5678. https://doi.org/10.1109/TIP.2019.2921877

    Article  MathSciNet  MATH  Google Scholar 

  97. Qiu T, Shi X, Wang J, Li Y, Qu S, Cheng Q, Cui T, Sui S (2019) Deep learning: a rapid and efficient route to automatic metasurface design. Adv Sci. https://doi.org/10.1002/advs.201900128

    Article  Google Scholar 

  98. Chen H, Chen A, Xu L, Xie H, Qiao H, Lin Q, Cai K (2020) A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manag 240:106303

    Article  Google Scholar 

  99. Zhang X, Wang T, Wang J, Tang G, Zhao L (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Underst. https://doi.org/10.1016/j.cviu.2020.103003

    Article  Google Scholar 

  100. Chen H-L, Wang G, Ma C, Cai Z-N, Liu W-B, Wang S-J (2016) An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184:131–144. https://doi.org/10.1016/j.neucom.2015.07.138

    Article  Google Scholar 

  101. Hu L, Hong G, Ma J, Wang X, Chen H (2015) An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Comput Biol Med 59:116–124. https://doi.org/10.1016/j.compbiomed.2015.02.003

    Article  Google Scholar 

  102. Wang S-J, Chen H-L, Yan W-J, Chen Y-H, Fu X (2014) Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process Lett 39:25–43. https://doi.org/10.1007/s11063-013-9288-7

    Article  Google Scholar 

  103. Xia J, Chen H, Li Q, Zhou M, Chen L, Cai Z, Fang Y, Zhou H (2017) Ultrasound-based differentiation of malignant and benign thyroid nodules: an extreme learning machine approach. Comput Methods Programs Biomed 147:37–49. https://doi.org/10.1016/j.cmpb.2017.06.005

    Article  Google Scholar 

  104. Ray R, Kumar D, Samui P, Roy LB, Goh A, Zhang W (2020) Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. Geosci Front 12:375–383. https://doi.org/10.1016/j.gsf.2020.05.003

    Article  Google Scholar 

  105. Yang J, Li S, Wang Z, Dong H, Wang J, Tang S (2020) Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13. https://doi.org/10.3390/ma13245755

  106. Lv Q, Liu H, Yang D, Liu H (2019) Effects of urbanization on freight transport carbon emissions in China: common characteristics and regional disparity. J Clean Prod 211:481–489. https://doi.org/10.1016/j.jclepro.2018.11.182

    Article  Google Scholar 

  107. Zhao C, Li J (2020) Equilibrium selection under the Bayes-based strategy updating rules. Symmetry 12:739

    Article  Google Scholar 

  108. Feng X, Li S, Yuan C, Zeng P, Sun Y (2018) Prediction of slope stability using naive Bayes classifier. KSCE J Civ Eng 22:941–950

    Article  Google Scholar 

  109. Suman S, Khan S, Das S, Chand S (2016) Slope stability analysis using artificial intelligence techniques. Nat Hazards 84:727–748

    Article  Google Scholar 

  110. Chakraborty A, Goswami D (2017) Slope stability prediction using artificial neural network (ANN). Int J Eng Comput Sci 6:21845–21848

    Google Scholar 

  111. Verma A, Singh T, Chauhan NK, Sarkar K (2016) A hybrid FEM–ANN approach for slope instability prediction. J Inst Eng (India) Ser A 97:171–180

    Article  Google Scholar 

  112. Fu X, Pace P, Aloi G, Yang L, Fortino G (2020) Topology Optimization Against Cascading Failures on Wireless Sensor Networks Using a Memetic Algorithm. Comput Netw 177:107327. https://doi.org/10.1016/j.comnet.2020.107327

    Article  Google Scholar 

  113. Qu S, Han Y, Wu Z, Raza H (2020) Consensus modeling with asymmetric cost based on data-driven robust optimization. Group Decis Negot. https://doi.org/10.1007/s10726-020-09707-w

    Article  Google Scholar 

  114. Mi C, Cao L, Zhang Z, Feng Y, Yao L, Wu Y (2020) A port container code recognition algorithm under natural conditions. J Coast Res 103:822–829. https://doi.org/10.2112/SI103-170.1

    Article  Google Scholar 

  115. 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. https://doi.org/10.1016/j.egyr.2020.02.035

    Article  Google Scholar 

  116. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.105946

    Article  Google Scholar 

  117. Chen H, Heidari AA, Chen H, Wang M, Pan Z, Gandomi AH (2020) Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Future Gener Comput Syst 111:175–198. https://doi.org/10.1016/j.future.2020.04.008

    Article  Google Scholar 

  118. Zhang Y, Liu R, Wang X, Chen H, Li C (2020) Boosted binary Harris hawks optimizer and feature selection. Eng Comput. https://doi.org/10.1007/s00366-020-01028-5

    Article  Google Scholar 

  119. Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P (2020) Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans Fuzzy Syst 28:2702–2710

    Article  Google Scholar 

  120. Cao B, Wang X, Zhang W, Song H, Lv Z (2020) A Many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw 34:78–83

    Article  Google Scholar 

  121. Cao B, Zhao J, Gu Y, Fan S, Yang P (2020) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Ind Inf 16:5309–5316. https://doi.org/10.1109/TII.2019.2961340

    Article  Google Scholar 

  122. Cao B, Zhao J, Yang P, Gu Y, Muhammad K, Rodrigues JJPC, Albuquerque VHCD (2020) Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans Ind Inf 16:3597–3605. https://doi.org/10.1109/TII.2019.2952565

    Article  Google Scholar 

  123. 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. https://doi.org/10.1016/j.knosys.2016.01.002

    Article  Google Scholar 

  124. 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. https://doi.org/10.1016/j.neucom.2017.04.060

    Article  Google Scholar 

  125. 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. https://doi.org/10.1016/j.ins.2019.04.022

    Article  MathSciNet  Google Scholar 

  126. Li C, Hou L, Sharma BY, Li H, Chen C, Li Y, Zhao X, Huang H, Cai Z, Chen H (2018) Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput Methods Programs Biomed 153:211–225. https://doi.org/10.1016/j.cmpb.2017.10.022

    Article  Google Scholar 

  127. Xu X, Chen H-L (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18:797–807. https://doi.org/10.1007/s00500-013-1089-4

    Article  Google Scholar 

  128. Chen H, Qiao H, Xu L, Feng Q, Cai K (2019) A fuzzy optimization strategy for the implementation of RBF LSSVR model in Vis–NIR analysis of pomelo maturity. IEEE Trans Ind Inf 15:5971–5979. https://doi.org/10.1109/TII.2019.2933582

    Article  Google Scholar 

  129. Gao W (2015) Stability analysis of rock slope based on an abstraction ant colony clustering algorithm. Environ Earth Sci 73:7969–7982

    Article  Google Scholar 

  130. 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. https://doi.org/10.1016/j.asoc.2014.07.024

    Article  Google Scholar 

  131. Wang B, Moayedi H, Rashid SA, Nguyen HA (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. https://doi.org/10.1007/s00366-019-00764-7

    Article  Google Scholar 

  132. Moayedi H, Dieu Tien B, Gor 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. https://doi.org/10.3390/ijgi8090391

  133. Wang B, Zhang BF, Liu XW, Zou FC (2020) Novel infrared image enhancement optimization algorithm combined with DFOCS. Optik 224:165476

    Article  Google Scholar 

  134. 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. https://doi.org/10.1016/j.compbiolchem.2018.11.017

    Article  Google Scholar 

  135. Cao B, Fan S, Zhao J, Yang P, Muhammad K, Tanveer M (2020) Quantum-enhanced multiobjective large-scale optimization via parallelism. Swarm Evol Comput 57:100697. https://doi.org/10.1016/j.swevo.2020.100697

    Article  Google Scholar 

  136. Cao B, Zhao J, Gu Y, Ling Y, Ma X (2020) Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm Evol Comput 53:100626. https://doi.org/10.1016/j.swevo.2019.100626

    Article  Google Scholar 

  137. 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 35. https://doi.org/10.1007/s00366-019-00839-5

  138. Himanshu N, Kumar V, Burman A, Maity D, Gordan B (2020) Grey wolf optimization approach for searching critical failure surface in soil slopes. Eng Comput 34:1–14. https://doi.org/10.1007/s00366-019-00927-6

    Article  Google Scholar 

  139. Mishra M, Gunturi VR, Miranda TFDS (2019) Slope stability analysis using recent metaheuristic techniques: a comprehensive survey. SN Appl Sci 1:1674

    Article  Google Scholar 

  140. 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 

  141. Moayedi H, Mehrabi M, Kalantar B, Mu’azu MA, Rashid AS, Foong LK, Nguyen H (2019) Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide. Geomat Nat Hazards Risk 10:1879–1911

    Article  Google Scholar 

  142. Peng S, Zhang Z, Liu E, Liu W, Qiao W (2021) A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline. J Nat Gas Sci Eng 85:103716. https://doi.org/10.1016/j.jngse.2020.103716

    Article  Google Scholar 

  143. Rukhaiyar S, Alam M, Samadhiya N (2018) A PSO-ANN hybrid model for predicting factor of safety of slope. Int J Geotech Eng 12:556–566

    Google Scholar 

  144. Wang S, Zhang K, van Beek LPH, Tian X, Bogaard TA (2020) Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environ Model Softw 124:104607. https://doi.org/10.1016/j.envsoft.2019.104607

    Article  Google Scholar 

  145. Qi C, Tang X (2018) Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Comput Ind Eng 118:112–122

    Article  Google Scholar 

  146. Gao W, Raftari M, Rashid ASA, Mu’azu MA, Jusoh WAW (2020) A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes. Eng Comput 36:325–344

    Article  Google Scholar 

  147. Krabbenhoft K, Lyamin A, Krabbenhoft J (2015) Optum computational engineering (Optum G2). http://www.optumce.com

  148. Sall OA, Sarr D, Ba M, Cisse N, Ly A (2019) Numerical analysis of shallow foundations in a soil mass under various behavior laws. Am J Mech Eng 7:129–137

    Google Scholar 

  149. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190

    Article  Google Scholar 

  150. Menesy AS, Sultan HM, Kamel S (2020) Extracting model parameters of proton exchange membrane fuel cell using equilibrium optimizer algorithm. In: 2020 International youth conference on radio electronics, electrical and power engineering (REEPE)

  151. Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci 293:125–145

    Article  Google Scholar 

  152. Doğan B, Ölmez T (2015) Vortex search algorithm for the analog active filter component selection problem. AEU Int J Electron Commun 69:1243–1253

    Article  Google Scholar 

  153. Dogan B, Ölmez T (2015) Modified off-lattice AB model for protein folding problem using the vortex search algorithm. Int J Mach Learn Comput 5:329

    Article  Google Scholar 

  154. Altintasi C, Aydin O, Taplamacioglu MC, Salor O (2020) Power system harmonic and interharmonic estimation using vortex search algorithm. Electr Power Syst Res 182:106187

    Article  Google Scholar 

  155. Qyyum MA, Yasin M, Nawaz A, He T, Ali W, Haider J, Qadeer K, Nizami A-S, Moustakas K, Lee M (2020) Single-solution-based vortex search strategy for optimal design of offshore and onshore natural gas liquefaction processes. Energies 13:1732

    Article  Google Scholar 

  156. Ranganathan A (2004) The Levenberg–Marquardt algorithm. Tutoral LM Algorithm 11:101–110

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Loke Kok Foong

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

    Loke Kok Foong

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

    Hossein Moayedi

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

    Hossein Moayedi

Authors
  1. Loke Kok Foong
    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

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

Foong, L.K., Moayedi, H. Slope stability evaluation using neural network optimized by equilibrium optimization and vortex search algorithm. Engineering with Computers 38 (Suppl 2), 1269–1283 (2022). https://doi.org/10.1007/s00366-021-01282-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01282-1

Keywords

Search

Navigation