Integrated machine learning for modeling bearing capacity of shallow foundations (2024)

1. Wang J, Tian J, Zhang X, Yang B, Liu S, Yin L, Zheng W. Control of time delay force feedback teleoperation system with finite time convergence. Front. Neurorobot. 2022;16:877069. doi:10.3389/fnbot.2022.877069. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Cao J, Quek S-T, Xiong H, Yang Z. Comparison of constrained unscented and cubature kalman filters for nonlinear system parameter identification. J. Eng. Mech. 2023;149:04023088. doi:10.1061/JENMDT.EMENG-7091. [CrossRef] [Google Scholar]

3. Cui W, Zhao L, Ge Y. Wind-induced buffeting vibration of long-span bridge considering geometric and aerodynamic nonlinearity based on reduced-order modeling. J Struct Eng. 2023;149:04023160. doi:10.1061/JSENDH.STENG-11543. [CrossRef] [Google Scholar]

4. Yu J, Zhu Y, Yao W, Liu X, Ren C, Cai Y, Tang X. Stress relaxation behaviour of marble under cyclic weak disturbance and confining pressures. Measurement. 2021;182:109777. doi:10.1016/j.measurement.2021.109777. [CrossRef] [Google Scholar]

5. Zhang X, Wang S, Liu H, Cui J, Liu C, Meng X. Assessing the impact of inertial load on the buckling behavior of piles with large slenderness ratios in liquefiable deposits. Soil Dyn Earthq Eng. 2024;176:108322. doi:10.1016/j.soildyn.2023.108322. [CrossRef] [Google Scholar]

6. Hu D, Li Y, Yang X, Liang X, Zhang K, Liang X. Experiment and application of NATM tunnel deformation monitoring based on 3D laser scanning. Struct. Control Health Monit. 2023;2023:1–13. doi:10.1155/2023/3341788. [CrossRef] [Google Scholar]

7. Jia S, Dai Z, Zhou Z, Ling H, Yang Z, Qi L, Wang Z, Zhang X, Thanh HV, Soltanian MR. Upscaling dispersivity for conservative solute transport in naturally fractured media. Water Res. 2023;235:119844. doi:10.1016/j.watres.2023.119844. [PubMed] [CrossRef] [Google Scholar]

8. Zhang X, Zhou G, Liu X, Fan Y, Meng E, Yang J, Huang Y. Experimental and numerical analysis of seismic behaviour for recycled aggregate concrete filled circular steel tube frames. Comput Concr. 2023;31:537. [Google Scholar]

9. Li J, Zhang Y, Lin L, Zhou Y. Study on the shear mechanics of gas hydrate-bearing sand-well interface with different roughness and dissociation. Bull. Eng. Geol. Environ. 2023;82:404. doi:10.1007/s10064-023-03432-9. [CrossRef] [Google Scholar]

10. Yao Y, Huang H, Zhang W, Ye Y, Xin L, Liu Y. Seismic performance of steel-PEC spliced frame beam. J. Constr Steel Res. 2022;197:107456. doi:10.1016/j.jcsr.2022.107456. [CrossRef] [Google Scholar]

11. Jafarzadeh E, Kabiri-Samani A, Boroomand B, Bohluly A. Analytical modeling of flexible circular submerged mound motion in gravity waves. J. Ocean Eng. Mar. Energy. 2023;9:181–190. doi:10.1007/s40722-022-00248-9. [CrossRef] [Google Scholar]

12. Cao J, Bu F, Wang J, Bao C, Chen W, Dai K. Reconstruction of full-field dynamic responses for large-scale structures using optimal sensor placement. J. Sound Vib. 2023;554:117693. doi:10.1016/j.jsv.2023.117693. [CrossRef] [Google Scholar]

13. Li D, Nie J-H, Wang H, Ren W-X. Loading condition monitoring of high-strength bolt connections based on physics-guided deep learning of acoustic emission data. Mech. Syst. Signal Process. 2024;206:110908. doi:10.1016/j.ymssp.2023.110908. [CrossRef] [Google Scholar]

14. Li J, Liu Y, Lin G. Implementation of a coupled FEM-SBFEM for soil-structure interaction analysis of large-scale 3D base-isolated nuclear structures. Comput. Geotech. 2023;162:105669. doi:10.1016/j.compgeo.2023.105669. [CrossRef] [Google Scholar]

15. Ren C, Yu J, Zhang C, Liu X, Zhu Y, Yao W. Micro–macro approach of anisotropic damage: A semi-analytical constitutive model of porous cracked rock. Eng. Fract. Mech. 2023;290:109483. doi:10.1016/j.engfracmech.2023.109483. [CrossRef] [Google Scholar]

16. Guo M, Huang H, Zhang W, Xue C, Huang M. Assessment of RC frame capacity subjected to a loss of corner column. J. Struct. Eng. 2022;148:04022122. doi:10.1061/(ASCE)ST.1943-541X.0003423. [CrossRef] [Google Scholar]

17. Cui W, Caracoglia L, Zhao L, Ge Y. Examination of occurrence probability of vortex-induced vibration of long-span bridge decks by Fokker–Planck–Kolmogorov equation. Struct. Saf. 2023;105:102369. doi:10.1016/j.strusafe.2023.102369. [CrossRef] [Google Scholar]

18. Liang F, Wang R, Pang Q, Hu Z. Design and optimization of press slider with steel-aluminum composite bionic sandwich structure for energy saving. J. Clean. Prod. 2023;428:139341. doi:10.1016/j.jclepro.2023.139341. [CrossRef] [Google Scholar]

19. Zhang C. The active rotary inertia driver system for flutter vibration control of bridges and various promising applications. Sci. China Technol. Sci. 2023;66:390–405. doi:10.1007/s11431-022-2228-0. [CrossRef] [Google Scholar]

20. Ren C, Yu J, Liu S, Yao W, Zhu Y, Liu X. A plastic strain-induced damage model of porous rock suitable for different stress paths. Rock Mech. Rock Eng. 2022;55:1887–1906. doi:10.1007/s00603-022-02775-1. [CrossRef] [Google Scholar]

21. Chen Y, Zhu L, Hu Z, Chen S, Zheng X. Risk propagation in multilayer heterogeneous network of coupled system of large engineering project. J. Manag. Eng. 2022;38:04022003. doi:10.1061/(ASCE)ME.1943-5479.0001022. [CrossRef] [Google Scholar]

22. Huang H, Huang M, Zhang W, Guo M, Chen Z, Li M. Progressive collapse resistance of multistory RC frame strengthened with HPFL-BSP. J. Build. Eng. 2021;43:103123. doi:10.1016/j.jobe.2021.103123. [CrossRef] [Google Scholar]

23. Wang, Y., Peng, J., Wang, L., Xu, C. & Dai, B. (2023) Micro-macro evolution of mechanical behaviors of thermally damaged rock: A state-of-the-art review. J. Rock Mech. Geotech. Eng.

24. Zhang X, Liu X, Zhang S, Wang J, Fu L, Yang J, Huang Y. Analysis on displacement-based seismic design method of recycled aggregate concrete-filled square steel tube frame structures. Struct. Concr. 2023;16:4268. [Google Scholar]

25. Deng E-F, Wang Y-H, Zong L, Zhang Z, Zhang J-F. Seismic behavior of a novel liftable connection for modular steel buildings: Experimental and numerical studies. Thin-Wall. Struct. 2024;197:111563. doi:10.1016/j.tws.2024.111563. [CrossRef] [Google Scholar]

26. Yan, T., Xu, R., Sun, S-H., Hou, Z-K. & Feng, J-Y. (2023) A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm. Petrol. Sci.

27. Li X, Zhu H, Yuan Q. Dilatancy equation based on the property-dependent plastic potential theory for geomaterials. Fractal and Fractional. 2023;7:824. doi:10.3390/fractalfract7110824. [CrossRef] [Google Scholar]

28. He H, Wang S, Shen W, Zhang W. The influence of pipe-jacking tunneling on deformation of existing tunnels in soft soils and the effectiveness of protection measures. Transp. Geotech. 2023;42:101061. doi:10.1016/j.trgeo.2023.101061. [CrossRef] [Google Scholar]

29. Liu W, Liang J, Xu T. Tunnelling-induced ground deformation subjected to the behavior of tail grouting materials. Tunnel. Undergr. Space Technol. 2023;140:105253. doi:10.1016/j.tust.2023.105253. [CrossRef] [Google Scholar]

30. Shi Y, Hou X, Na Z, Zhou J, Yu N, Liu S, Xin L, Gao G, Liu Y. Bio-inspired attachment mechanism of dynastes Hercules: Vertical climbing for on-orbit assembly legged robots. J. Bionic Eng. 2023;21:137–148. doi:10.1007/s42235-023-00423-0. [CrossRef] [Google Scholar]

31. She A, Wang L, Peng Y, Li J. Structural reliability analysis based on improved wolf pack algorithm AK-SS. Elsevier; 2023. [Google Scholar]

32. Khalil MA, Sadeghiamirshahidi M, Joeckel R, Santos FM, Riahi A. Mapping a hazardous abandoned gypsum mine using self-potential, electrical resistivity tomography, and frequency domain electromagnetic methods. J. Appl. Geophys. 2022;205:104771. doi:10.1016/j.jappgeo.2022.104771. [CrossRef] [Google Scholar]

33. Khoei A, Mousavi S, Hosseini N. Modeling density-driven flow and solute transport in heterogeneous reservoirs with micro/macro fractures. Adv. Water Resour. 2023;182:104571. doi:10.1016/j.advwatres.2023.104571. [CrossRef] [Google Scholar]

34. Jafarzadeh E, Kabiri-Samani A, Mansourzadeh S, Bohluly A. Experimental modeling of the interaction between waves and submerged flexible mound breakwaters. Proc. Inst. Mech. Eng. Part M J. Eng. Maritime Environ. 2021;235:127–141. [Google Scholar]

35. Zhang G-x, Fu J-s. Upper bound solution for bearing capacity of circular shallow foundation based on limit analysis. Rock Soil Mech. 2010;31:3849–3854. [Google Scholar]

36. Vanapalli SK, Mohamed FM. Bearing capacity of model footings in unsaturated soils, experimental unsaturated soil mechanics. Springer; 2007. pp. 483–493. [Google Scholar]

37. Acharyya R, Dey A, Kumar B. Finite element and ANN-based prediction of bearing capacity of square footing resting on the crest of c-φ soil slope. Int. J. Geotechn. Eng. 2020;14:176–187. doi:10.1080/19386362.2018.1435022. [CrossRef] [Google Scholar]

38. Keskin MS, Laman M. Model studies of bearing capacity of strip footing on sand slope. Ksce J. Civ. Eng. 2013;17:699–711. doi:10.1007/s12205-013-0406-x. [CrossRef] [Google Scholar]

39. Bagińska M, Srokosz PE. The optimal ANN model for predicting bearing capacity of shallow foundations trained on scarce data. KSCE J. Civ. Eng. 2019;23:130–137. doi:10.1007/s12205-018-2636-4. [CrossRef] [Google Scholar]

40. Chen T, Xiao S. An upper bound solution to undrained bearing capacity of rigid strip footings near slopes. Int. J. Civ. Eng. 2020;18:475–485. doi:10.1007/s40999-019-00463-w. [CrossRef] [Google Scholar]

41. Chakraborty D, Kumar J. Bearing capacity of foundations on slopes. Geomech. Geoeng. 2013;8:274–285. doi:10.1080/17486025.2013.770172. [CrossRef] [Google Scholar]

42. Gajurel A, Chittoori B, Mukherjee PS, Sadegh M. Machine learning methods to map stabilizer effectiveness based on common soil properties. Transp. Geotech. 2021;27:100506. doi:10.1016/j.trgeo.2020.100506. [CrossRef] [Google Scholar]

43. Wang X, Dong X, Zhang Z, Zhang J, Ma G, Yang X. Compaction quality evaluation of subgrade based on soil characteristics assessment using machine learning. Transp. Geotech. 2021;32:100703. doi:10.1016/j.trgeo.2021.100703. [CrossRef] [Google Scholar]

44. Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Abdulhamid SN, Salim SG, Ali HFH, Majeed MK. Artificial intelligence forecasting models of uniaxial compressive strength. Transp. Geotech. 2021;27:100499. doi:10.1016/j.trgeo.2020.100499. [CrossRef] [Google Scholar]

45. Mehrabi M, Asadi Nalivan O, Scaioni M, Karvarinasab M, Kornejady A, Moayedi H. Spatial mapping of gully erosion susceptibility using an efficient metaheuristic neural network. Environ. Earth Sci. 2023;82:459. doi:10.1007/s12665-023-11106-8. [CrossRef] [Google Scholar]

46. Asteris PG, Skentou AD, Bardhan A, Samui P, Lourenço PB. Soft computing techniques for the prediction of concrete compressive strength using non-destructive tests. Constr. Build. Mater. 2021;303:124450. doi:10.1016/j.conbuildmat.2021.124450. [CrossRef] [Google Scholar]

47. Armaghani DJ, Mamou A, Maraveas C, Roussis PC, Siorikis VG, Skentou AD, Asteris PG. Predicting the unconfined compressive strength of granite using only two non-destructive test indexes. Geomech. Eng. 2021;25:317–330. [Google Scholar]

48. Shi M-L, Lv L, Xu L. A multi-fidelity surrogate model based on extreme support vector regression: Fusing different fidelity data for engineering design. Eng. Comput. 2023;40:473–493. doi:10.1108/EC-10-2021-0583. [CrossRef] [Google Scholar]

49. Shi M, Hu W, Li M, Zhang J, Song X, Sun W. Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine. Mech. Syst. Signal Process. 2023;188:110022. doi:10.1016/j.ymssp.2022.110022. [CrossRef] [Google Scholar]

50. Al-Shaaby A, Aljamaan H, Alshayeb M. Bad smell detection using machine learning techniques: A systematic literature review. Arab. J. Sci. Eng. 2020;45:2341–2369. doi:10.1007/s13369-019-04311-w. [CrossRef] [Google Scholar]

51. Bektur G. An NSGA-II-based memetic algorithm for an energy-efficient unrelated parallel machine scheduling problem with machine-sequence dependent setup times and learning effect. Arab. J. Sci. Eng. 2021;47(3):3773–3788. doi:10.1007/s13369-021-06114-4. [CrossRef] [Google Scholar]

52. Liu L, Liu J, Zhou Q, Qu M. An SVR-based machine learning model depicting the propagation of gas explosion disaster hazards. Arab. J. Sci. Eng. 2021;46:10205–10216. doi:10.1007/s13369-021-05616-5. [CrossRef] [Google Scholar]

53. Sun G, Hasanipanah M, Amnieh HB, Foong LK. Feasibility of indirect measurement of bearing capacity of driven piles based on a computational intelligence technique. Measurement. 2020;156:107577. doi:10.1016/j.measurement.2020.107577. [CrossRef] [Google Scholar]

54. Demetgul M, Yildiz K, Taskin S, Tansel I, Yazicioglu O. Fault diagnosis on material handling system using feature selection and data mining techniques. Measurement. 2014;55:15–24. doi:10.1016/j.measurement.2014.04.037. [CrossRef] [Google Scholar]

55. Gupta R, Goyal K, Yadav N. Prediction of safe bearing capacity of noncohesive soil in arid zone using artificial neural networks. Int. J. Geomech. 2016;16:04015044. doi:10.1061/(ASCE)GM.1943-5622.0000514. [CrossRef] [Google Scholar]

56. Shahin MA, Maier HR, Jaksa MB. Predicting settlement of shallow foundations using neural networks. J. Geotech. Geoenviron. Eng. 2002;128:785–793. doi:10.1061/(ASCE)1090-0241(2002)128:9(785). [CrossRef] [Google Scholar]

57. Debnath P, Dey AK. Prediction of bearing capacity of geogrid-reinforced stone columns using support vector regression. Int. J. Geomech. 2018;18:04017147. doi:10.1061/(ASCE)GM.1943-5622.0001067. [CrossRef] [Google Scholar]

58. Pham TA, Ly H-B, Tran VQ, Giap LV, Vu H-LT, Duong H-AT. Prediction of pile axial bearing capacity using artificial neural network and random forest. Appl. Sci. 2020;10:1871. doi:10.3390/app10051871. [CrossRef] [Google Scholar]

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

60. Padmini D, Ilamparuthi K, Sudheer KP. Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Comput. Geotech. 2008;35:33–46. doi:10.1016/j.compgeo.2007.03.001. [CrossRef] [Google Scholar]

61. Moayedi H, Hayati S. Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl. Soft Comput. 2018;66:208–219. doi:10.1016/j.asoc.2018.02.027. [CrossRef] [Google Scholar]

62. Kohestani VR, Vosoghi M, Hassanlourad M, Fallahnia M. Bearing capacity of shallow foundations on cohesionless soils: A random forest based approach. Civ. Eng. Infrastruct. J. 2017;50:35–49. [Google Scholar]

63. Dutta R, Rao TG, Khatri VN. Application of soft computing techniques in predicting the ultimate bearing capacity of strip footing subjected to eccentric inclined load and resting on sand. J. Soft Comput. Civ. Eng. 2019;3:30–43. [Google Scholar]

64. Özdemir E. A new predictive model for uniaxial compressive strength of rock using machine learning method: Artificial intelligence-based age-layered population structure genetic programming (ALPS-GP) Arab. J. Sci. Eng. 2021;47(1):629–639. doi:10.1007/s13369-021-05761-x. [CrossRef] [Google Scholar]

65. Kamal M, Inel M. Optimum design of reinforced concrete continuous foundation using differential evolution algorithm. Arab. J. Sci. Eng. 2019;44:8401–8415. doi:10.1007/s13369-019-03889-5. [CrossRef] [Google Scholar]

66. Alzabeebee S, Zuhaira AA, Al-Hamd RKS. Development of an optimized model to compute the undrained shaft friction adhesion factor of bored piles. Geomech. Eng. 2022;28:397–404. [Google Scholar]

67. Ghanizadeh AR, Ghanizadeh A, Asteris PG, Fakharian P, Armaghani DJ. Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method. Transp. Geotech. 2023;38:100906. doi:10.1016/j.trgeo.2022.100906. [CrossRef] [Google Scholar]

68. Alzabeebee S. Explicit soft computing model to predict the undrained bearing capacity of footing resting on aggregate pier reinforced cohesive ground. Innov. Infrastruct. Sol. 2022;7:105. doi:10.1007/s41062-021-00706-7. [CrossRef] [Google Scholar]

69. Moayedi H, Bui DT, Ngo PTT. Neural computing improvement using four metaheuristic optimizers in bearing capacity analysis of footings settled on two-layer soils. Appl. Sci. Basel. 2019;9:5264. doi:10.3390/app9235264. [CrossRef] [Google Scholar]

70. Moayedi H, Nguyen H, Rashid ASA. Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils. Eng. Comput. 2019;37:437–447. doi:10.1007/s00366-019-00834-w. [CrossRef] [Google Scholar]

71. Harandizadeh H, Toufigh V. Application of developed new artificial intelligence approaches in civil engineering for ultimate pile bearing capacity prediction in soil based on experimental datasets. Iran. J. Sci. Technol. Trans. Civ. Eng. 2020;44:545–559. doi:10.1007/s40996-019-00332-5. [CrossRef] [Google Scholar]

72. Marto A, Hajihassani M, Momeni E. Bearing capacity of shallow foundation's prediction through hybrid artificial neural networks. Trans Tech Publ; 2014. [Google Scholar]

73. Kalinli A, Acar MC, Gunduz Z. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng. Geol. 2011;117:29–38. doi:10.1016/j.enggeo.2010.10.002. [CrossRef] [Google Scholar]

74. Einolvand R. Prediction of ultimate bearing capacity of shallow foundation on granular soils using imperialist competitive algorithm based ANN. Soil Struct. Interact. J. 2019;4:1–11. [Google Scholar]

75. Bardhan A, Manna P, Kumar V, Burman A, Žlender B, Samui P. Reliability analysis of piled raft foundation using a novel hybrid approach of ANN and equilibrium optimizer. CMES-Comput. Model. Eng. Sci. 2021;128:1033–1067. [Google Scholar]

76. Moayedi H, Dehrashid AA. A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping. Environ. Sci. Pollut. Res. 2023;30:82964–82989. doi:10.1007/s11356-023-28133-4. [CrossRef] [Google Scholar]

77. Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 2013;219:8121–8144. [Google Scholar]

78. Bhattacharjee K, Bhattacharya A, Dey SHN. Backtracking search optimization based economic environmental power dispatch problems. Int. J. Electric. Power Energy Syst. 2015;73:830–842. doi:10.1016/j.ijepes.2015.06.018. [CrossRef] [Google Scholar]

79. Guney K, Durmus A, Basbug S. Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays. Int. J. Antennas Propag. 2014;2014:1–11. [Google Scholar]

80. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowl. Based Syst. 2020;191:105190. doi:10.1016/j.knosys.2019.105190. [CrossRef] [Google Scholar]

81. Menesy, A.S., Sultan, H.M. & Kamel, S (2020) Extracting model parameters of proton exchange membrane fuel cell using equilibrium optimizer algorithm. IEEE.

82. Abdel-Basset M, Chang V, Mohamed R. A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput. Appl. 2020;33:10685–10718. doi:10.1007/s00521-020-04820-y. [CrossRef] [Google Scholar]

83. Khoei A, Saeedmonir S, Hosseini N, Mousavi S. An X-FEM technique for numerical simulation of variable-density flow in fractured porous media. MethodsX. 2023;10:102137. doi:10.1016/j.mex.2023.102137. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

84. Ghazavi M, Eghbali AH. A simple limit equilibrium approach for calculation of ultimate bearing capacity of shallow foundations on two-layered granular soils. Geotech. Geol. Eng. 2008;26:535–542. doi:10.1007/s10706-008-9187-2. [CrossRef] [Google Scholar]

85. Gor M. Analyzing the bearing capacity of shallow foundations on two-layered soil using two novel cosmology-based optimization techniques. Smart Struct. Syst. 2022;29:513–522. [Google Scholar]

86. Moayedi H, Nguyen H, Rashid ASA. Novel metaheuristic classification approach in developing mathematical model-based solutions predicting failure in shallow footing. Eng. Comput. 2021;37:223–230. doi:10.1007/s00366-019-00819-9. [CrossRef] [Google Scholar]

87. Hecht-Nielsen R. Theory of the backpropagation neural network, neural networks for perception. Elsevier; 1992. pp. 65–93. [Google Scholar]

88. Lambert J, Halfon P, Penaranda G, Bedossa P, Cacoub P, Carrat F. How to measure the diagnostic accuracy of noninvasive liver fibrosis indices: The area under the ROC curve revisited. Clin. Chem. 2008;54:1372–1378. doi:10.1373/clinchem.2007.097923. [PubMed] [CrossRef] [Google Scholar]

89. Vilar del Hoyo L, Martín Isabel MP, Martínez Vega FJ. Logistic regression models for human-caused wildfire risk estimation: Analysing the effect of the spatial accuracy in fire occurrence data. Eur. J. For. Res. 2011;130:983–996. doi:10.1007/s10342-011-0488-2. [CrossRef] [Google Scholar]

90. Moayedi H, Aghel B, Vaferi B, Foong LK, Bui DT. The feasibility of Levenberg-Marquardt algorithm combined with imperialist competitive computational method predicting drag reduction in crude oil pipelines. J. Petrol. Sci. Eng. 2020;185:106634. doi:10.1016/j.petrol.2019.106634. [CrossRef] [Google Scholar]

91. Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng. Comput. 2018;35:967–984. doi:10.1007/s00366-018-0644-0. [CrossRef] [Google Scholar]

92. Asteris PG, Lemonis ME, Le T-T, Tsavdaridis KD. Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling. Eng. Struct. 2021;248:113297. doi:10.1016/j.engstruct.2021.113297. [CrossRef] [Google Scholar]

93. Skentou AD, Bardhan A, Mamou A, Lemonis ME, Kumar G, Samui P, Armaghani DJ, Asteris PG. Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models. Rock Mech. Rock Eng. 2023;56:487–514. doi:10.1007/s00603-022-03046-9. [CrossRef] [Google Scholar]

94. Le T-T, Skentou AD, Mamou A, Asteris PG. Correlating the unconfined compressive strength of rock with the compressional wave velocity effective porosity and schmidt hammer rebound number using artificial neural networks. Rock Mech. Rock Eng. 2022;55:6805–6840. doi:10.1007/s00603-022-02992-8. [CrossRef] [Google Scholar]

95. Cui W, Zhao L, Ge Y, Xu K. A generalized van der Pol nonlinear model of vortex-induced vibrations of bridge decks with multistability. Nonlinear Dyn. 2023;112(1):259–272. doi:10.1007/s11071-023-09047-9. [CrossRef] [Google Scholar]

96. Zhou C, Wang J, Shao X, Li L, Sun J, Wang X. The feasibility of using ultra-high performance concrete (UHPC) to strengthen RC beams in torsion. J. Mater. Res. Technol. 2023;24:9961–9983. doi:10.1016/j.jmrt.2023.05.185. [CrossRef] [Google Scholar]

97. Huang H, Guo M, Zhang W, Zeng J, Yang K, Bai H. Numerical investigation on the bearing capacity of RC columns strengthened by HPFL-BSP under combined loadings. J. Build. Eng. 2021;39:102266. doi:10.1016/j.jobe.2021.102266. [CrossRef] [Google Scholar]

98. Ren C, Yu J, Liu X, Zhang Z, Cai Y. Cyclic constitutive equations of rock with coupled damage induced by compaction and cracking. Int. J. Min. Sci. Technol. 2022;32:1153–1165. doi:10.1016/j.ijmst.2022.06.010. [CrossRef] [Google Scholar]

99. Liu C, Cui J, Zhang Z, Liu H, Huang X, Zhang C. The role of TBM asymmetric tail-grouting on surface settlement in coarse-grained soils of urban area: Field tests and FEA modelling. Tunnel. Undergr. Space Technol. 2021;111:103857. doi:10.1016/j.tust.2021.103857. [CrossRef] [Google Scholar]

100. Adarsh, S., Dhanya, R., Krishna, G., Merlin, R. & Tina, J. (2012) Prediction of ultimate bearing capacity of cohesionless soils using soft computing techniques. International Scholarly Research Notices 2012

101. Alzabeebee S, Alshkane YM, Keawsawasvong S. New model to predict bearing capacity of shallow foundations resting on cohesionless soil. Geotech. Geol. Eng. 2023;41:3531–3547. doi:10.1007/s10706-023-02472-y. [CrossRef] [Google Scholar]

102. Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem. Concr. Res. 2021;145:106449. doi:10.1016/j.cemconres.2021.106449. [CrossRef] [Google Scholar]

103. Armaghani DJ, Asteris PG. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput. Appl. 2021;33:4501–4532. doi:10.1007/s00521-020-05244-4. [CrossRef] [Google Scholar]

104. Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip. REV. Comput. Stat. 2010;2:433–459. doi:10.1002/wics.101. [CrossRef] [Google Scholar]

105. Cao J, He H, Zhang Y, Zhao W, Yan Z, Zhu H. Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domain. Struct. Health Monit. 2023;23(2):1013–1024. doi:10.1177/14759217231178457. [CrossRef] [Google Scholar]

106. Xu T, Sabzalian MH, Hammoud A, Tahami H, Gholami A, Lee S. An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance. Sci. Rep. 2024;14:2170. doi:10.1038/s41598-024-52462-0. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

107. Apostolopoulou M, Asteris PG, Armaghani DJ, Douvika MG, Lourenço PB, Cavaleri L, Bakolas A, Moropoulou A. Mapping and holistic design of natural hydraulic lime mortars. Cem. Concr. Res. 2020;136:106167. doi:10.1016/j.cemconres.2020.106167. [CrossRef] [Google Scholar]

108. Alzabeebee S, Mohammed DA, Alshkane YM. Experimental study and soft computing modeling of the unconfined compressive strength of limestone rocks considering dry and saturation conditions. Rock Mech. Rock Eng. 2022;55:5535–5554. doi:10.1007/s00603-022-02948-y. [CrossRef] [Google Scholar]

Integrated machine learning for modeling bearing capacity of shallow foundations (2024)
Top Articles
Latest Posts
Article information

Author: Ms. Lucile Johns

Last Updated:

Views: 6075

Rating: 4 / 5 (41 voted)

Reviews: 88% of readers found this page helpful

Author information

Name: Ms. Lucile Johns

Birthday: 1999-11-16

Address: Suite 237 56046 Walsh Coves, West Enid, VT 46557

Phone: +59115435987187

Job: Education Supervisor

Hobby: Genealogy, Stone skipping, Skydiving, Nordic skating, Couponing, Coloring, Gardening

Introduction: My name is Ms. Lucile Johns, I am a successful, friendly, friendly, homely, adventurous, handsome, delightful person who loves writing and wants to share my knowledge and understanding with you.