A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation

Abstract

Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called the water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

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References

  1. 1.

    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

    MathSciNet  Article  Google Scholar 

  2. 2.

    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

    MathSciNet  Article  MATH  Google Scholar 

  3. 3.

    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 6:1900128. https://doi.org/10.1002/advs.201900128

    Article  Google Scholar 

  4. 4.

    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 

  5. 5.

    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 

  6. 6.

    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 

  7. 7.

    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 

  8. 8.

    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 

  9. 9.

    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 

  10. 10.

    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 

  11. 11.

    Gao N, Wu J, Lu K, Zhong H (2021) Hybrid composite meta-porous structure for improving and broadening sound absorption. Mechanical Systems and Signal Processing 154:107504 https://doi.org/10.1016/j.ymssp.2020.107504.

  12. 12.

    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

    MathSciNet  Article  Google Scholar 

  13. 13.

    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

    MathSciNet  Article  Google Scholar 

  14. 14.

    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 

  15. 15.

    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 

  16. 16.

    Zhang T, Wu X, Li H, Tsang DCW, Li G, Ren H (2020) Struvite pyrolysate cycling technology assisted by thermal hydrolysis pretreatment to recover ammonium nitrogen from composting leachate. J Clean Prod 242:118442. https://doi.org/10.1016/j.jclepro.2019.118442

    Article  Google Scholar 

  17. 17.

    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 

  18. 18.

    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 

  19. 19.

    Wang Y, Yuan Y, Wang Q, Liu C, Zhi Q, Cao J (2020) Changes in air quality related to the control of coronavirus in China: implications for traffic and industrial emissions. Sci Total Environ 731:139133. https://doi.org/10.1016/j.scitotenv.2020.139133

    Article  Google Scholar 

  20. 20.

    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 

  21. 21.

    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 

  22. 22.

    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 

  23. 23.

    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 

  24. 24.

    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 

  25. 25.

    Zhang T, He X, Deng Y, Tsang DCW, Yuan H, Shen J, Zhang S (2020) Swine manure valorization for phosphorus and nitrogen recovery by catalytic–thermal hydrolysis and struvite crystallization. Sci Total Environ 729:138999. https://doi.org/10.1016/j.scitotenv.2020.138999

    Article  Google Scholar 

  26. 26.

    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 

  27. 27.

    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 

  28. 28.

    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 Manage 236:108–117. https://doi.org/10.1016/j.jenvman.2019.01.018

    Article  Google Scholar 

  29. 29.

    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 

  30. 30.

    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 

  31. 31.

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

    Article  Google Scholar 

  32. 32.

    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 

  33. 33.

    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 

  34. 34.

    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 

  35. 35.

    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 

  36. 36.

    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 

  37. 37.

    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 

  38. 38.

    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 

  39. 39.

    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

    MathSciNet  Article  MATH  Google Scholar 

  40. 40.

    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 

  41. 41.

    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 

  42. 42.

    Abedini A, Onsorynezhad S, Wang F (2017) Study of an impact driven frequency up-conversion piezoelectric harvester. Dynamic Systems and Control Conference https://doi.org/10.1115/DSCC2017-5396.

  43. 43.

    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 

  44. 44.

    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 

  45. 45.

    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 

  46. 46.

    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 

  47. 47.

    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 

  48. 48.

    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 

  49. 49.

    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 

  50. 50.

    Onsorynezhad S, Abedini A, Wang F (2018) Analytical Study of a Piezoelectric Frequency Up-Conversion Harvester Under Sawtooth Wave Excitation. https://doi.org/10.1115/DSCC2018-9173.

  51. 51.

    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

  52. 52.

    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.

  53. 53.

    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.

  54. 54.

    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 

  55. 55.

    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 

  56. 56.

    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 141:106690. https://doi.org/10.1016/j.ymssp.2020.106690

    Article  Google Scholar 

  57. 57.

    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 

  58. 58.

    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. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09483-5

    Article  Google Scholar 

  59. 59.

    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 

  60. 60.

    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 

  61. 61.

    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 

  62. 62.

    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 

  63. 63.

    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 

  64. 64.

    Wang J, Huang Y, Wang T, Zhang C, Liu Yh (2020) Fuzzy finite-time stable compensation control for a building structural vibration system with actuator failures. Appl Soft Comput 93:106372. https://doi.org/10.1016/j.asoc.2020.106372

    Article  Google Scholar 

  65. 65.

    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 

  66. 66.

    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 

  67. 67.

    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 

  68. 68.

    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 

  69. 69.

    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 2020:8537198. https://doi.org/10.1155/2020/8537198

    Article  MATH  Google Scholar 

  70. 70.

    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 2020:9812019. https://doi.org/10.1155/2020/9812019

    Article  Google Scholar 

  71. 71.

    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 

  72. 72.

    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 

  73. 73.

    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

    Article  Google Scholar 

  74. 74.

    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

    Article  Google Scholar 

  75. 75.

    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

    Article  Google Scholar 

  76. 76.

    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 

  77. 77.

    Zhang X, Wang T, Wang J, Tang G, Zhao L (2020) Pyramid Channel-based Feature Attention Network for image dehazing. Comput Vis Image Underst 197–198:103003. https://doi.org/10.1016/j.cviu.2020.103003

    Article  Google Scholar 

  78. 78.

    Chen Z, Wang J, Ma K, Huang X, Wang T (2020) Fuzzy adaptive two-bits-triggered control for nonlinear uncertain system with input saturation and output constraint. Int J Adapt Control Signal Process 34:543–559. https://doi.org/10.1002/acs.3098

    MathSciNet  Article  Google Scholar 

  79. 79.

    Huang Z, Zheng H, Guo L, Mo D (2020) Influence of the position of artificial boundary on computation accuracy of conjugated infinite element for a finite length cylindrical shell. Acoust Aust 48:287–294. https://doi.org/10.1007/s40857-020-00175-5

    Article  Google Scholar 

  80. 80.

    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 

  81. 81.

    Wang X, Liu Y, Choo K (2020) Fault tolerant, ulti-subset aggregation scheme for smart grid. IEEE Trans Ind Inform

  82. 82.

    Wu C, Wu P, Wang J, Jiang R, Chen M, Wang X (2021) Ontological knowledge base for concrete bridge rehabilitation project management. Autom Constr 121:103428. https://doi.org/10.1016/j.autcon.2020.103428

    Article  Google Scholar 

  83. 83.

    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

    Article  Google Scholar 

  84. 84.

    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

    Article  Google Scholar 

  85. 85.

    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 

  86. 86.

    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 Transactions on Industrial Informatics 15: 5971-5979 https://doi.org/10.1109/TII.2019.2933582.

  87. 87.

    Jalali A, Behrouzi MK, Salari N, Bazrafshan M-R, Rahmati M (2019) The effectiveness of group spiritual intervention on self-esteem and happiness among men undergoing methadone maintenance treatment. Curr Drug Res Rev Former Curr Drug Abuse Rev 11:67–72

    Google Scholar 

  88. 88.

    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 10:57. https://doi.org/10.1186/1742-4682-10-57

    Article  Google Scholar 

  89. 89.

    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

    Article  Google Scholar 

  90. 90.

    Mohammadi M, Raiegani AAV, Jalali R, Ghobadi A, Salari N (2019) The prevalence of retinopathy among type 2 diabetic patients in Iran: a systematic review and meta-analysis. Rev Endocr Metab Disord 20:79–88

    Article  Google Scholar 

  91. 91.

    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

    Article  Google Scholar 

  92. 92.

    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 

  93. 93.

    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 

  94. 94.

    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 

  95. 95.

    Tu J, Chen H, Liu J, Heidari AA, Zhang X, Wang M, Ruby R, Pham Q-VJK-BS (2021) Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowledge-Based Systems 212:106642 https://doi.org/10.1016/j.knosys.2020.106642

  96. 96.

    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 

  97. 97.

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

    Article  Google Scholar 

  98. 98.

    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 

  99. 99.

    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

    Article  Google Scholar 

  100. 100.

    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

    Article  Google Scholar 

  101. 101.

    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  Article  Google Scholar 

  102. 102.

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

    Article  Google Scholar 

  103. 103.

    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

    Article  Google Scholar 

  104. 104.

    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 

  105. 105.

    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

    Article  Google Scholar 

  106. 106.

    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 

  107. 107.

    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 

  108. 108.

    Veiskarami M, Habibagahi G (2013) Foundations bearing capacity subjected to seepage by the kinematic approach of the limit analysis. Front Struct Civ Eng 7:446–455

    Article  Google Scholar 

  109. 109.

    Merifield RS, Lyamin AV, Sloan S (2006) Limit analysis solutions for the bearing capacity of rock masses using the generalised Hoek-Brown criterion. Int J Rock Mech Min Sci 43:920–937

    Article  Google Scholar 

  110. 110.

    Salari-Rad H, Mohitazar M, Dizadji MR (2013) Distinct element simulation of ultimate bearing capacity in jointed rock foundations. Arab J Geosci 6:4427–4434

    Article  Google Scholar 

  111. 111.

    Khorrami R, Derakhshani A, Moayedi H (2020) New explicit formulation for shallow foundations’ ultimate bearing capacity rested on granular soil using M5’model tree. Measurement 108032

  112. 112.

    Khorrami R, Derakhshani A (2019) Estimation of ultimate bearing capacity of shallow foundations resting on cohesionless soils using a new hybrid M5’-GP model. Geomech Eng 19:127–139

    Google Scholar 

  113. 113.

    Sethy B, Patra C, Sivakugan N, Das B (2017) Application of ANN and ANFIS for predicting the ultimate bearing capacity of eccentrically loaded rectangular foundations. Int J Geosynth Ground Eng 3:35

    Article  Google Scholar 

  114. 114.

    Dutta RK, Khatri VN, Gnananandarao T (2019) Soft computing based prediction of ultimate bearing capacity of footings resting on rock masses. Int J Geol Geotech Eng 5:1–14

    Google Scholar 

  115. 115.

    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

    Article  Google Scholar 

  116. 116.

    Acharyya R, Dey A (2019) Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput Appl 31:8087–8100

    Article  Google Scholar 

  117. 117.

    Aouadj A, Bouafia A (2020) CPT-based method using hybrid artificial neural network and mathematical model to predict the load-settlement behaviour of shallow foundations. Geomech Geoeng 1–13

  118. 118.

    Acharyya R, Dey A, Kumar B (2018) Finite element and ANN-based prediction of bearing capacity of square footing resting on the crest of c-φ soil slope. Int J Geotech Eng 14:176–187

    Article  Google Scholar 

  119. 119.

    Bagińska M, Srokosz PE (2019) The optimal ANN model for predicting bearing capacity of shallow foundations trained on scarce data. KSCE J Civ Eng 23:130–137

    Article  Google Scholar 

  120. 120.

    Dutta RK, Rani R, Rao TG (2018) Prediction of ultimate bearing capacity of skirted footing resting on sand using artificial neural networks. J Soft Comput Civ Eng 2:34–46

    Google Scholar 

  121. 121.

    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 

  122. 122.

    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 Manage 134:382–398. https://doi.org/10.1016/j.enconman.2016.12.019

    Article  Google Scholar 

  123. 123.

    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 

  124. 124.

    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.

  125. 125.

    Zhu J, Wang X, Wang P, Wu Z, Kim MJ (2019) Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Automation in Construction 102:105–119. https://doi.org/10.1016/j.autcon.2019.02.014

    Article  Google Scholar 

  126. 126.

    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 

  127. 127.

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

    Article  Google Scholar 

  128. 128.

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

    Article  Google Scholar 

  129. 129.

    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 Industr Inf 16:3597–3605. https://doi.org/10.1109/TII.2019.2952565

    Article  Google Scholar 

  130. 130.

    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 107327

  131. 131.

    Zhang X, Wang D, Zhou Z, Ma Y (2019) Robust low-rank tensor recovery with rectification and alignment. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2929043

    Article  Google Scholar 

  132. 132.

    Hamrouni A, Sbartai B, Dias D (2018) Probabilistic analysis of ultimate seismic bearing capacity of strip foundations. J Rock Mech Geotech Eng 10:717–724

    Article  Google Scholar 

  133. 133.

    Saha A, Saha AK, Ghosh S (2018) Pseudodynamic bearing capacity analysis of shallow strip footing using the advanced optimization technique “hybrid symbiosis organisms search algorithm” with numerical validation. Adv Civ Eng 2018:1–18

    Article  Google Scholar 

  134. 134.

    Jin L, Zhang H, Feng Q (2020) Ultimate bearing capacity of strip footing on sands under inclined loading based on improved radial movement optimization. Eng Optim 53:1–23

    Google Scholar 

  135. 135.

    Kashani AR, Gandomi M, Camp CV, Gandomi AH (2019) Optimum design of shallow foundation using evolutionary algorithms. Soft Comput 24:1–25

    Google Scholar 

  136. 136.

    Gandomi AH, Kashani AR (2017) Construction cost minimization of shallow foundation using recent swarm intelligence techniques. IEEE Trans Industr Inf 14:1099–1106

    Article  Google Scholar 

  137. 137.

    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

    Article  Google Scholar 

  138. 138.

    Moayedi H, Bui DT, Ngo T, Thao P (2019) Neural computing improvement using four metaheuristic optimizers in bearing capacity analysis of footings settled on two-layer soils. Appl Sci 9:5264

    Article  Google Scholar 

  139. 139.

    Pakdel P, Jamshidi Chenari R, Veiskarami M (2019) An estimate of the bearing capacity of shallow foundations on anisotropic soil by limit equilibrium and soft computing technique. Geomech Geoeng 14:202–217

    Article  Google Scholar 

  140. 140.

    Andrab SG, Hekmat A, Yusop ZB (2017) A review: evolutionary computations (GA and PSO) in geotechnical engineering. Comput Water Energy Environ Eng 6:154–179

    Article  Google Scholar 

  141. 141.

    Foong LK, Moayedi H, Lyu Z (2020) Computational modification of neural systems using a novel stochastic search scheme, namely evaporation rate-based water cycle algorithm: an application in geotechnical issues. Eng Comput 1–12

  142. 142.

    Nasir M, Sadollah A, Choi YH, Kim JH (2020) A comprehensive review on water cycle algorithm and its applications. Neural Comput Appl 32:1–56

    Article  Google Scholar 

  143. 143.

    Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  144. 144.

    David S (1993) The water cycle (John Yates, Illus). Thomson Learning, New York

    Google Scholar 

  145. 145.

    Heidari AA, Abbaspour RA, Jordehi AR (2017) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 28:57–85

    Article  Google Scholar 

  146. 146.

    Luo Q, Wen C, Qiao S, Zhou Y (2016) Dual-system water cycle algorithm for constrained engineering optimization problems. In: International conference on intelligent computing

  147. 147.

    Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evolut Comput 26:8–22

    Article  Google Scholar 

  148. 148.

    Talebi B, Dehkordi MN (2018) Sensitive association rules hiding using electromagnetic field optimization algorithm. Expert Syst Appl 114:155–172

    Article  Google Scholar 

  149. 149.

    Bouchekara H, Zellagui M, Abido MA (2017) Optimal coordination of directional overcurrent relays using a modified electromagnetic field optimization algorithm. Appl Soft Comput 54:267–283

    Article  Google Scholar 

  150. 150.

    Song S, Jia H, Ma J (2019) A chaotic electromagnetic field optimization algorithm based on fuzzy entropy for multilevel thresholding color image segmentation. Entropy 21:398

    MathSciNet  Article  Google Scholar 

  151. 151.

    Duan Q, Gupta VK, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521

    MathSciNet  MATH  Article  Google Scholar 

  152. 152.

    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313

    MathSciNet  MATH  Article  Google Scholar 

  153. 153.

    Liong S-Y, Atiquzzaman M (2004) Optimal design of water distribution network using shuffled complex evolution. J Inst Eng Singap 44:93–107

    Google Scholar 

  154. 154.

    Majeed K, Qyyum MA, Nawaz A, Ahmad A, Naqvi M, He T, Lee M (2020) Shuffled complex evolution-based performance enhancement and analysis of cascade liquefaction process for large-scale LNG production. Energies 13:2511

    Article  Google Scholar 

  155. 155.

    Bayat P, Afrakhte H (2020) A purpose-oriented shuffled complex evolution optimization algorithm for energy management of multi-microgrid systems considering outage duration uncertainty. J Intell Fuzzy Syst 38:1–18

    Google Scholar 

  156. 156.

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

    Article  Google Scholar 

  157. 157.

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

    Article  Google Scholar 

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Moayedi, H., Mosavi, A. A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation. Engineering with Computers (2021). https://doi.org/10.1007/s00366-021-01289-8

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Keywords

  • Bearing capacity
  • Settlement measurement
  • Artificial neural network
  • Water cycle algorithm
  • Machine learning
  • Artificial intelligence