Smart Structures and Systems

  • 제29권3호
  • /
  • Pages.513-522
  • /
  • 2022
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Analyzing the bearing capacity of shallow foundations on two-layered soil using two novel cosmology-based optimization techniques

  • Gor, Mesut (Firat University, Engineering Faculty, Civil Engineering Department, Division of Geotechnical Engineering)
  • 투고 : 2021.03.28
  • 심사 : 2021.12.31
  • 발행 : 2022.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

키워드

참고문헌

  1. Al-qaness, M.A., Abd Elaziz, M., Ewees, A.A. and Cui, X. (2019), "A modified adaptive neuro-fuzzy inference system using multiverse optimizer algorithm for oil consumption forecasting", Electronics, 8(10), 1071. https://doi.org/10.3390/electronics8101071
  2. Azan, D. and Haddad, A. (2019), "Simple equations for considering spatial variability on the bearing capacity of clay", Civil Eng. J., 5(1), 93-106. https://doi.org/10.28991/cej-2019-03091228
  3. Breiman, L. (1996), "Bagging predictors", Mach. Learn., 24(2), 123-140. https://doi.org/10.1007/BF00058655
  4. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Gor, M., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019), "Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility", Sensors, 19, 3590. https://doi.org/10.3390/s19163590
  5. Burse, K., Manoria, M. and Kirar, V.P.S. (2011), "Improved back propagation algorithm to avoid local minima in multiplicative neuron model", Proceedings of International Conference on Advances in Information Technology and Mobile Communication, pp. 67-73. ttps://doi.org/10.1007/978-3-642-20573-6_11
  6. Celik, E. and Gor, H. (2019), "Enhanced speed control of a DC servo system using PI+ DF controller tuned by stochastic fractal search technique", J. Franklin Inst., 356(3), 1333-1359. https://doi.org/10.1016/j.jfranklin.2018.11.020
  7. Celik, E., Gor, H., Ozturk, N. and Kurt, E. (2017), "Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator", Int. J. Hydrogen Energy, 42(28), 17692-17699. https://doi.org/10.1016/j.ijhydene.2017.01.168
  8. Chen, L.Y., Liu, X., Wang, S.L. and Qin, C.Y. (2010), "Overexpression of the Endocan gene in endothelial cells from hepatocellular carcinoma is associated with angiogenesis and tumour invasion", J. Int. Med. Res., 38(2), 498-510. https://doi.org/10.1177/147323001003800213
  9. Das, M. and Dey, A.K. (2018), "Prediction of bearing capacity of stone columns placed in soft clay using ANN model", Geotech. Geol. Eng., 36(3), 1845-1861. https://doi.org/10.1007/s10706-017-0436-0
  10. Dede, T., Kankal, M., Vosoughi, A.R., Grzywinski, M. and Kripka, M. (2019), "Artificial intelligence applications in civil engineering", Adv. Civil Eng., 2019. https://doi.org/10.1155/2019/8384523
  11. Deeb, H., Sarangi, A., Mishra, D. and Sarangi, S.K. (2020), "Improved Black Hole optimization algorithm for data clustering", J. King Saud Univ.-Comput. Info. Sci. https://doi.org/10.1016/j.jksuci.2020.12.013
  12. Faris, H., Aljarah, I. and Mirjalili, S. (2016), "Training feedforward neural networks using multi-verse optimizer for binary classification problems", Appl. Intell., 45(2), 322-332. https://doi.org/10.1007/s10489-016-0767-1
  13. Faris, H., Hassonah, M.A., Al-Zoubi, A.M., Mirjalili, S. and Aljarah, I. (2018), "A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture", Neural Comput. Applicat., 30(8), 2355-2369. https://doi.org/10.1007/s00521-016-2818-2
  14. Fathizadeh, S.F., Vosoughi, A.R. and Banan, M.R. (2021), "Considering soil-structure interaction effects on performance-based design optimization of moment-resisting steel frames by an engineered cluster-based genetic algorithm", Eng. Optimiz., 53(3), 440-460. https://doi.org/10.1080/0305215X.2020.1739278
  15. Feng, Y., Zhang, B., Liu, Y., Niu, Z., Dai, B., Fan, Y. and Chen, X. (2021), "A 200-225-GHz manifold-coupled multiplexer utilizing metal waveguides", IEEE Transact. Microw. Theory Techniques, 69(2), 5327-5333. https://doi.org/10.1109/TMTT.2021.3119316
  16. Gharehpasha, S., Masdari, M. and Jafarian, A. (2021), "Virtual machine placement in cloud data centers using a hybrid multiverse optimization algorithm", Artif. Intell. Rev., 54(3), 2221-2257. https://doi.org/10.1007/s10462-020-09903-9
  17. Hans, R. and Kaur, H. (2020), "Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection", Int. J. Interact. Multimedia Artif. Intell., 6(1). https://doi.org/10.9781/ijimai.2019.07.004
  18. Hansen, J.B. (1970), A revised and extended formula for bearing capacity.
  19. Harandizadeh, H., Jahed Armaghani, D. and Khari, M. (2019), "A new development of ANFIS-GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets", Eng. Comput., 1-16. https://doi.org/10.1007/s00366-019-00849-3
  20. Hatamlou, A. (2013), "Black hole: A new heuristic optimization approach for data clustering", Info. Sci., 222, 175-184. https://doi.org/10.1016/j.ins.2012.08.023
  21. Hecht-Nielsen, R. (1992), Neural Networks for Perception, Elsevier, pp. 65-93. https://doi.org/10.1016/B978-0-12-741252-8.50010-8
  22. Heidari, A.A. and Abbaspour, R.A. (2014), "A gravitational black hole algorithm for autonomous UCAV mission planning in 3D realistic environments", Int. J. Comput. Applicat., 95(9).
  23. Hornik, K. (1991), "Approximation capabilities of multilayer feedforward networks", Neural Networks, 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
  24. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  25. Jabbar, S.F., Hamed, R.I. and Alwan, A.H. (2018), "The potential of nonparametric model in foundation bearing capacity prediction", Neural Comput. Applicat., 30(10), 3235-3241. https://doi.org/10.1007/s00521-017-2916-9
  26. Jamali, A. (2021), "Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function", Egypt. J. Remote Sensing Space Sci., 24(3), 373-390. https://doi.org/10.1016/j.ejrs.2020.07.001
  27. Khalili, A. and Vosoughi, A.R. (2018), "An approach for the Pasternak elastic foundation parameters estimation of beams using simulated frequencies", Inverse Probl. Sci. Eng., 26(8), 1079-1093. https://doi.org/10.1080/17415977.2017.1377707
  28. Kalinli, A., Acar, M.C. and Gunduz, Z. (2011), "New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization", Eng. Geol., 117(1-2), 29-38. https://doi.org/10.1016/j.enggeo.2010.10.002
  29. Khare, A., Gupta, R. and Shukla, P.K. (2022), IoT and Analytics for Sensor Networks, Springer, pp. 333-343. https://doi.org/10.1007/978-981-16-2919-8_3
  30. Kuo, Y.L., Jaksa, M.B., Lyamin, A.V. and Kaggwa, W.S. (2009), "ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil", Comput. Geotech., 36(3), 503-516. https://doi.org/10.1016/j.compgeo.2008.07.002
  31. Kurt, E. and Gor, H. (2014), "Electromagnetic design of a new axial flux generator", Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 39-42. https://doi.org/10.1109/ECAI.2014.7090195
  32. Li, X., Yang, H., Zhang, J., Qian, G., Yu, H. and Cai, J. (2021), "Time-domain analysis of tamper displacement during dynamic compaction based on automatic control", Coatings, 11(9), 1092. https://doi.org/10.3390/coatings11091092
  33. Liang, S., Foong, L.K. and Lyu, Z. (2020), "Determination of the friction capacity of driven piles using three sophisticated search schemes", Eng. Comput., 1-13. https://doi.org/10.1007/s00366-020-01118-4
  34. Liu, W., Moayedi, H., Nguyen, H., Lyu, Z. and Bui, D.T. (2019), "Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil", Eng. Comput., 1-11. https://doi.org/10.1007/s00366-019-00897-9
  35. Lu, N., Wang, H., Wang, K. and Liu, Y. (2021), "Maximum Probabilistic and Dynamic Traffic Load Effects on Short-to-Medium Span Bridges", Comput. Model. Eng. Sci., 127(1), 345-360. https://doi.org/10.32604/cmes.2021.013792
  36. Luo, Y., Zheng, H., Zhang, H. and Liu, Y. (2021), "Fatigue reliability evaluation of aging prestressed concrete bridge accounting for stochastic traffic loading and resistance degradation", Adv. Struct. Eng., 24(13), 3021-3029. https://doi.org/10.1177/13694332211017995
  37. Malekzadeh, P. and Vosoughi, A.R. (2009), "DQM large amplitude vibration of composite beams on nonlinear elastic foundations with restrained edges", Commun. Nonlinear Sci. Numer. Simul., 14(3), 906-915. https://doi.org/10.1016/j.cnsns.2007.10.014
  38. Meyerhof, G.G. (1963), "Some recent research on the bearing capacity of foundations", Can. Geotech. J., 1(1), 16-26. https://doi.org/10.1139/t63-003
  39. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: a nature-inspired algorithm for global optimization", Neural Comput. Applicat., 27(2), 495-513. https://doi.org/10.1007/s00521-015-1870-7
  40. Moayedi, H. and Mosavi, A. (2021), "A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation", Eng. Comput., 1-14. https://doi.org/10.1007/s00366-021-01289-8
  41. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2018), "Modification of landslide susceptibility mapping using optimized PSO-ANN technique", Eng. Comput., 1-18. https://doi.org/10.1007/s00366-018-0644-0
  42. Moayedi, H., Bui, D.T. and Thi Ngo, P.T. (2019a), "Neural computing improvement using four metaheuristic optimizers in bearing capacity analysis of footings settled on two-layer soils", Appl. Sci., 9(23), 5264. https://doi.org/10.3390/app9235264
  43. Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu'azu, M., A. Rashid, A.S., Foong, L.K. and Nguyen, H. (2019b), "Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide", Geomat. Natural Hazards Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126
  44. Moayedi, H., Gor, M., Khari, M., Foong, L.K., Bahiraei, M. and Bui, D.T. (2020), "Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength", Measurement, 107576. https://doi.org/10.1016/j.measurement.2020.107576
  45. Moayedi, H., Abdullahi, M.A.M., Nguyen, H. and Rashid, A.S.A. (2021), "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., 37(1), 437-447. https://doi.org/10.1007/s00366-019-00834-w
  46. More, J.J. (1978), Numerical Analysis, Springer, pp. 105-116. https://doi.org/10.1007/BFb0067700
  47. Munoz, R., Olivares, R., Taramasco, C., Villarroel, R., Soto, R., Barcelos, T.S., Merino, E. and Alonso-Sanchez, M.F. (2018), "Using black hole algorithm to improve eeg-based emotion recognition", Computat. Intell. Neurosci., 2018. https://doi.org/10.1155/2018/3050214
  48. Onat, O. and Gul, M. (2018), "Application of artificial neural networks to the prediction of out-of-plane response of infill walls subjected to shake table", Smart Struct. Syst., Int. J., 21(4), 521-535. https://doi.org/10.12989/sss.2018.21.4.521
  49. Ornek, M. (2014), "Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils", Neural Comput. Applicat., 25(1), 39-54. https://doi.org/10.1007/s00521-013-1444-5
  50. Padmini, D., Ilamparuthi, K. and Sudheer, K.P. (2008), "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models", Comput. Geotech., 35(1), 33-46. https://doi.org/10.1016/j.compgeo.2007.03.001
  51. Pashaei, E. and Pashaei, E. (2021), "Training feedforward neural network using enhanced black hole algorithm: a case study on COVID-19 related ACE2 gene expression classification", Arab. J. Sci. Eng., 46(4), 3807-3828. https://doi.org/10.1007/s13369-020-05217-8
  52. Pohjankukka, J., Riihimaki, H., Nevalainen, P., Pahikkala, T., AlaIlomaki, J., Hyvonen, E., Varjo, J. and Heikkonen, J. (2016), "Predictability of boreal forest soil bearing capacity by machine learning", J. Terramech., 68, 1-8. https://doi.org/10.1016/j.jterra.2016.09.001
  53. Sadrossadat, E., Ghorbani, B., Oskooei, R. and Kaboutari, M. (2018), "Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations", Eng. Computat. https://doi.org/10.1108/EC-07-2017-0258
  54. Salih, S.Q. (2019), "A new training method based on black hole algorithm for convolutional neural network", J. Southwest Jiaotong Univ., 54(3). https://doi.org/10.35741/issn.0258-2724.54.3.22
  55. Seyedashraf, O., Mehrabi, M. and Akhtari, A.A. (2018), "Novel approach for dam break flow modeling using computational intelligence", J. Hydrol., 559, 1028-1038. https://doi.org/10.1016/j.jhydrol.2018.03.001
  56. Shahnazari, H. and Tutunchian, M.A. (2012), "Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach", KSCE J. Civil Eng., 16(6), 950-957. https://doi.org/10.1007/s12205-012-1651-0
  57. Soleimanbeigi, A. and Hataf, N. (2005), "Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks", Geosynth. Int., 12(6), 321-332. https://doi.org/10.1680/gein.2005.12.6.321
  58. Sultana, P. and Dey, A.K. (2019), "Estimation of ultimate bearing capacity of footings on soft clay from plate load test data considering variability", Indian Geotech. J., 49(2), 170-183. https://doi.org/10.1007/s40098-018-0311-9
  59. Sun, L., Li, C., Zhang, C., Su, Z. and Chen, C. (2018), "Early monitoring of rebar corrosion evolution based on FBG sensor", Int. J. Struct. Stabil. Dyn., 18(08), 1840001. https://doi.org/10.1142/S0219455418400011
  60. Sundaram, A. (2020), "Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems", Appl. Soft Comput., 91, 106195. https://doi.org/10.1016/j.asoc.2020.106195
  61. Swets, J.A. (1988), "Measuring the accuracy of diagnostic systems", Science, 240(4857), 1285-1293. https://doi.org/10.1126/science.3287615
  62. Terzaghi, K. (1943), "Earth pressure and shearing resistance of plastic clay: a symposium: liner-plate tunnels on the Chicago (IL) subway", Transact. Am. Soc. Civil Engs., 108(1), 970-1007. https://doi.org/10.1061/TACEAT.0005664
  63. Tsai, H.C., Tyan, Y.Y., Wu, Y.W. and Lin, Y.H. (2013), "Determining ultimate bearing capacity of shallow foundations using a genetic programming system", Neural Comput. Applicat., 23(7-8), 2073-2084. https://doi.org/10.1007/s00521-012-1150-8
  64. Vosoughi, A.R. (2016), "Nonlinear free vibration of functionally graded nanobeams on nonlinear elastic foundation", Iran. J. Sci. Technol. Transact. Civil Eng., 40(1), 23-32. https://doi.org/10.1007/s40996-016-0012-5
  65. Vosoughi, A.R. and Darabi, A. (2016), "A hybrid inverse method for small scale parameter estimation of FG nanobeams", Steel Compos. Struct., Int. J., 20(5), 1119-1131. https://doi.org/10.12989/scs.2016.20.5.1119
  66. Vosoughi, A.R. and Darabi, A. (2017), "A new hybrid CG-GAs approach for high sensitive optimization problems: with application for parameters estimation of FG nanobeams", Appl. Soft Comput., 52, 220-230. https://doi.org/10.1016/j.asoc.2016.12.016
  67. Vosoughi, A.R., Banan, M.R., Banan, M.R. and Malekzadeh, P. (2014), "Hybrid FE-IDQ-CG method for dynamic parameters estimation of multilayered half-space", Compos. Part B: Eng., 56, 74-82. https://doi.org/10.1016/j.compositesb.2013.08.001
  68. Vosoughi, A.R., Malekzadeh, P., Topal, U. and Dede, T. (2018), "A hybrid DQ-TLBO technique for maximizing first frequency of laminated composite skew plates", Steel Compos. Struct., Int. J., 28(4), 509-516. https://doi.org/10.12989/scs.2018.28.4.509
  69. Wang, L., Jin, X., Xu, W. and Xu, G. (2021), "A black hole particle swarm optimization method for the source parameters inversion: application to the 2015 Calbuco eruption, Chile", J. Geodyn., 146, 101849. https://doi.org/10.1016/j.jog.2021.101849
  70. Xu, J., Lan, W., Ren, C., Zhou, X., Wang, S. and Yuan, J. (2021), "Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization", Cold Regions Sci. Technol., 189, 103335. https://doi.org/10.1016/j.coldregions.2021.103335