Steel and Composite Structures

  • 제51권5호
  • /
  • Pages.509-527
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  • 2024
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Creation of regression analysis for estimation of carbon fiber reinforced polymer-steel bond strength

  • Xiaomei Sun (School of Civil Engineering, Xijing University) ;
  • Xiaolei Dong (School of Civil Engineering, Xijing University) ;
  • Weiling Teng (School of Civil Engineering, Xijing University) ;
  • Lili Wang (Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University) ;
  • Ebrahim Hassankhani (Dept. of Civil Engineering, Faculty of Geotechnical Engineering, Univ. of Tabriz)
  • 투고 : 2023.12.08
  • 심사 : 2024.05.09
  • 발행 : 2024.06.10
⟨ 이전 논문 다음 논문 ⟩

초록

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (PU) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the PU challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the PU of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was tc> Lc > EA > tA > Ec > bc > fc > fA from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R2 and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R2 equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

키워드

Acknowledgement

This paper is supported by Shaanxi Science and Technology Association Enterprise Innovation and Youth Talent Promotion Project (Program No. 20230612).

References

  1. Abellan-Garcia, J. and Guzman-Guzman, J.S. (2021), "Random forest-based optimization of UHPFRC under ductility requirements for seismic retrofitting applications", Construct. Build. Mater., 285, 122869. https://doi.org/10.1016/j.conbuildmat.2021.122869. 
  2. Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-qaness, M.A.A. and Gandomi, A.H. (2021), "Aquila optimizer: A novel meta-heuristic optimization algorithm", Comput. Ind. Eng., 157, 107250. https://doi.org/10.1016/j.cie.2021.107250. 
  3. Aghamohammadghasem, M., Azucena, J., Hashemian, F., Liao, H., Zhang, S. and Nachtmann, H. (2023), "System Simulation and Machine Learning-Based Maintenance Optimization for an Inland Waterway Transportation System," In Proceedings of the Winter Simulation Conference, 267-278. 
  4. Aghayari Hir, M., Zaheri, M. and Rahimzadeh, N. (2023), "Prediction of rural travel demand by spatial regression and artificial neural network methods (Tabriz County)", J. Transport. Res., 20(4), 367-386. 
  5. Akbar, I., Oehlers, D.J. and Ali, M.S.M. (2010), "Derivation of the bond-slip characteristics for FRP plated steel members", J. Construct. Steel Res., 66(8-9), 1047-1056. https://doi.org/10.1016/j.jcsr.2010.03.003. 
  6. Archer, K.J. and Kimes, R.V. (2008), "Empirical characterization of random forest variable importance measures", Comput. Statistics Data Anal., 52(4), 2249-2260. https://doi.org/10.1016/j.csda.2007.08.015. 
  7. Asteris, P.G., Lemois, M.E., Nguyen, T.-A., Van Le, H., Pham, B. T. and Structures, C. (2021), "Soft computing-based estimation of ultimate axial load of rectangular concrete-filled steel tubes", Steel Compos. Struct., 39(4), 471-491. https://doi.org/10.12989/scs.2021.39.4.471. 
  8. Behar, O., Khellaf, A. and Mohammedi, K. (2015), "Comparison of solar radiation models and their validation under Algerian climate-The case of direct irradiance", Energy Conversion Manage., 98, 236-251. https://doi.org/10.1016/j.enconman.2015.03.067. 
  9. Benemaran, R.S. (2023), "Application of extreme gradient boosting method for evaluating the properties of episodic failure of borehole breakout", Geoenergy Sci. Eng., 226, 211837. 
  10. Benemaran, R.S., Esmaeili-Falak, M. and Kordlar, M.S. (2023), "Improvement of recycled aggregate concrete using glass fiber and silica fume", Multiscale Multidiscipl. Modeling, Experiment. Des., 1-20. https://doi.org/10.1007/s41939-023-00313-2. 
  11. Bocciarelli, M. (2009), "Response of statically determined steel beams reinforced by CFRP plates in the elastic-plastic regime", Eng. Struct., 31(4), 956-967. https://doi.org/10.1016/j.engstruct.2008.12.005. 
  12. Bocciarelli, M., and Colombi, P. (2013), "On the elasto-plastic behavior of continuous steel beams reinforced by bonded CFRP lamina", Eng. Struct., 49, 756-766. https://doi.org/10.1016/j.engstruct.2012.12.018. 
  13. Breiman, L. (2001), "Random forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324. 
  14. Chen, S.-Z., Feng, D.-C., Han, W.-S. and Wu, G. (2021), "Development of data-driven prediction model for CFRP-steel bond strength by implementing ensemble learning algorithms", Construct. Build. Mater., 303, 124470. https://doi.org/10.1016/j.conbuildmat.2021.124470. 
  15. Chen, S.-Z., Wu, G. and Feng, D.-C. (2019), "Damage detection of highway bridges based on long-gauge strain response under stochastic traffic flow", Mech. Syst. Signal Processing, 127, 551-572. https://doi.org/10.1016/j.ymssp.2019.03.022. 
  16. Chen, S.-Z., Wu, G., Feng, D.-C., Wang, Z. and Cao, X.-Y. (2020), "Multi-cross-reference method for highway-bridge damage identification based on long-gauge fiber bragg-grating sensors", J. Bridge Eng., 25(6), 4020023. https://doi.org/10.1061/(ASCE)BE.1943-5592.000154. 
  17. Chen, S.-Z., Zhang, S.-Y., Han, W.-S. and Wu, G. (2021), "Ensemble learning based approach for FRP-concrete bond strength prediction", Construct. Build. Mater., 302, 124230. https://doi.org/10.1016/j.conbuildmat.2021.124230. 
  18. Cheng, M.-Y. and Cao, M.-T. (2014), "Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines", Appl. Soft Comput., 22, 178-188. https://doi.org/10.1016/j.asoc.2014.05.015. 
  19. Chuanqi, L.I., Zaheri, M., Ranjbarnia, M. and Daniel, D. (2023), "Calculating of the tunnel face deformations reinforced by longitudinal fiberglass dowels: From analytical method to artificial intelligence", Transport. Geotech., 43, 101152. 
  20. Daviran, M., Maghsoudi, A., Ghezelbash, R. and Pradhan, B. (2021), "A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach", Comput. Geosci., 148, 104688. https://doi.org/10.1016/j.cageo.2021.104688. 
  21. Dawei, Y., Bing, Z., Bingbing, G., Xibo, G. and Razzaghzadeh, B. (2023), "Predicting the CPT-based pile set-up parameters using HHO-RF and PSO-RF hybrid models", Struct. Eng. Mech., 86(5), 673-686. https://doi.org/10.12989/sem.2023.86.5.673. 
  22. Dehghani, E., Daneshjoo, F., Aghakouchak, A.A., and Khaji, N. (2012), "A new bond-slip model for adhesive in CFRP-steel composite systems", Eng. Struct., 34, 447-454. https://doi.org/10.1016/j.engstruct.2011.08.037. 
  23. Esmaeili-Falak, M. and Benemaran, R. S. (2023), "Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles", Geomech. Eng., 32(6), 583-600. https://doi.org/10.12989/gae.2023.32.6.583. 
  24. Esmaeili-Falak, M. and Benemaran, R.S. (2024), "Application of optimization-based regression analysis for evaluation of frost durability of recycled aggregate concrete", Struct. Concrete. https://doi.org/10.1002/suco.202300566. 
  25. Esmaeili-Falak, M., Katebi, H., Vadiati, M. and Adamowski, J. (2019), "Predicting triaxial compressive strength and Young's modulus of frozen sand using artificial intelligence methods", J. Cold Regions Eng., 33(3), 4019007. https://doi.org/https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188. 
  26. Farrar, D.E. and Glauber, R.R. (1967), "Multicollinearity in regression analysis: the problem revisited", Rev. Economic Statistics, 92-107. https://doi.org/10.2307/1937887. 
  27. Fawzia, S., Zhao, X.-L. and Al-Mahaidi, R. (2010), "Bond-slip models for double strap joints strengthened by CFRP", Compos. Struct., 92(9), 2137-2145. https://doi.org/10.1016/j.compstruct.2009.09.042. 
  28. Feng, D.-C., Chen, S.-Z., Azadi Kakavand, M.R. and Taciroglu, E. (2021), "Probabilistic model based on Bayesian model averaging for predicting the plastic hinge lengths of reinforced concrete columns", J. Eng. Mech., 147(10), 4021066. https://doi.org/10.1061/(ASCE)EM.1943-7889.00019. 
  29. Feng, D.-C., Liu, Z.-T., Wang, X.-D., Chen, Y., Chang, J.-Q., Wei, D.-F. and Jiang, Z.-M. (2020), "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach", Construct. Build. Mater., 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000. 
  30. Feng, D.-C., Wang, W.-J., Mangalathu, S., Hu, G. and Wu, T. (2021), "Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements", Eng. Struct., 235, 111979. https://doi.org/10.1016/j.engstruct.2021.111979. 
  31. Feng, T., Wang, C., Zhang, J., Wang, B. and Jin, Y.-F. (2022), "An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation", Underground Space, 7(4), 514-527. https://doi.org/10.1016/j.undsp.2021.11.004. 
  32. Fernando, N.D. (2010), Bond Behaviour and Debonding Failures in CFRP-Strengthened Steel Members.
  33. Friedman, J.H. (1991), "Multivariate adaptive regression splines", Annals Statistics, 1-67. 
  34. Gueymard, C.A. (2014), "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects", Renew. Sustain. Energy Rev., 39, 1024-1034. https://doi.org/10.1016/j.rser.2014.07.117. 
  35. Guo, K. and Yang, G. (2020), "Load-slip curves of shear connection in composite structures: prediction based on ANNs", Steel Compos. Struct., 36(5), 493-506. https://doi.org/10.12989/scs.2020.36.5.493. 
  36. Hashemi, A., Jang, J. and Beheshti, J. (2023), "A machine learning-based surrogate finite element model for estimating dynamic response of mechanical systems," IEEE Access. 11, 54509-54525, https://doi.org/10.1109/ACCESS.2023.3282453. 
  37. Hassankhani, E. and Esmaeili-Falak, M. (2024), "Soil-structure interaction for buried conduits influenced by the coupled effect of the protective layer and trench installation", J. Pipeline Syst. Eng. Practice. https://doi.org/10.1061/JPSEA2/PSENG-1547. 
  38. Hastie, T., Tibshirani, R. and Friedman, J. (2009), "The elements of statistical learnin. Cited On, 33. 
  39. He, J. and Xian, G. (2016), "Debonding of CFRP-to-steel joints with CFRP delamination", Compos. Struct., 153, 12-20. https://doi.org/10.1016/j.compstruct.2016.05.100. 
  40. He, J. and Xian, G. (2017), "Bond-slip behavior of fiber reinforced polymer strips-steel interface", Construct. Build. Mater., 155, 250-258. https://doi.org/10.1016/j.conbuildmat.2017.08.062. 
  41. Huo, W., Li, W., Zhang, Z., Sun, C., Zhou, F. and Gong, G. (2021), "Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection", Energy Conversion Manage., 243, 114367. https://doi.org/10.1016/j.enconman.2021.114367. 
  42. Iqbal, M., Zhang, D. and Jalal, F.E. (2021), "Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model", J. Ocean Eng. Sci., https://doi.org/10.1016/j.joes.2021.10.012. 
  43. Iskenderoglu, F.C., Baltacioglu, M.K., Demir, M.H., Baldinelli, A., Barelli, L. and Bidini, G. (2020), "Comparison of support vector regression and random forest algorithms for estimating the SOFC output voltage by considering hydrogen flow rates", Int. J. Hydrogen Energy, 45(60), 35023-35038. https://doi.org/10.1016/j.ijhydene.2020.07.265. 
  44. J., H. (2017), "Research on the effects of adhesive properties on the failure behaviors of CFRP-to-steel interface". 
  45. Khamees, A.K., Abdelaziz, A.Y., Eskaros, M.R., El-Shahat, A., and Attia, M.A. (2021), "Optimal power flow solution of wind-integrated power system using novel metaheuristic method", Energies, 14(19), 6117. https://doi.org/10.3390/en14196117. 
  46. Khan, M.A., Shah, M.I., Javed, M.F., Khan, M.I., Rasheed, S., El-Shorbagy, M.A., El-Zahar, E.R. and Malik, M.Y. (2022), "Application of random forest for modelling of surface water salinity", Ain Shams Eng. J., 13(4), 101635. https://doi.org/10.1016/j.asej.2021.11.004 
  47. Lewis, P.A.W. and Stevens, J.G. (1991), "Nonlinear modeling of time series using multivariate adaptive regression splines (MARS)", J. Amer. Statistic. Association, 86(416), 864-877. https://doi.org/10.1080/01621459.1991.10475126. 
  48. Li, M.-F., Tang, X.-P., Wu, W. and Liu, H.-B. (2013), "General models for estimating daily global solar radiation for different solar radiation zones in mainland China", Energy Conversion Manage., 70, 139-148. https://doi.org/10.1016/j.enconman.2013.03.004. 
  49. Liang, R. and Bayrami B. (2023), "Estimation of frost durability of recycled aggregate concrete by hybridized Random Forests algorithms, 49(1), 91-107. https://doi.org/10.12989/scs.2023.49.1.091. 
  50. Liaw, A. and Wiener, M. (2002), "Classification and regression by randomForest", R News, 2(3), 18-22. 
  51. Momeni, S., Kooban, F., Alipouri Niaz, S., Niyafard, S. and Soleimani, A. (2023), "Waste heat recovery, efficient lighting, and proper insulation: a comprehensive study of energy consumption and savings in the residential sector", Asian J. Civil Eng., 1-10. https://doi.org/10.1007/s42107-023-00923-8. 
  52. Moradi, G., Hassankhani, E. and Halabian, A.M. (2022), "Experimental and numerical analyses of buried box culverts in trenches using geofoam", Proceedings of Institution of Civil Engineers-Geotechnical Engineering, 175(3), 311-322. https://doi.org/10.1680/jgeen.19.00288 
  53. Nematirad, R., Pahwa, A., Natarajan, B. and Wu, H. (2023), "Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models", Front. Energy Res., 11, 1297356. 
  54. Nguyen, M.-S.T., Thai, D.K. and Kim, S.-E. (2020), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/scs.2020.35.3.415. 
  55. Nhu, V.-H., Hoang, N.-D., Duong, V.-B., Vu, H.-D. and Bui, D.T. (2020), "A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)", Eng. Comput., 36(2), 603-616. https://doi.org/10.1007/s00366-019-00718-z 
  56. Nozaka, K., Shield, C.K. and Hajjar, J.F. (2005), "Effective bond length of carbon-fiber-reinforced polymer strips bonded to fatigued steel bridge I-girders", J. Bridge Eng., 10(2), 195-205. https://doi.org/10.1061/(ASCE)1084-0702(2005)10:2(195. 
  57. Pang, Y.-Y., Wu, G., Su, Z.-L. and He, X.-Y. (2020), "Experimental study on the carbon-fiber-reinforced polymer-steel interfaces based on carbon-fiber-reinforced polymer delamination failures and hybrid failures", Adv. Struct. Eng., 23(11), 2247-2260. https://doi.org/10.1177/1369433220911167. 
  58. Pang, Y. (2019), "Study on the bond behavior of CFRP-steel interfaces under extreme service environment". 
  59. Pang, Y., Wu, G., Wang, H., Gao, D. and Zhang, P. (2021), "Bond-slip model of the CFRP-steel interface with the CFRP delamination failure", Compos. Struct., 256, 113015. https://doi.org/10.1016/j.compstruct.2020.113015. 
  60. Pang, Y., Wu, G., Wang, H., Su, Z. and He, X. (2019), "Experimental study on the bond behavior of CFRP-steel interfaces under quasi-static cyclic loading", Thin-Wall. Struct., 140, 426-437. https://doi.org/10.1016/j.tws.2019.03.060. 
  61. Pham, Q.B., Tran, D.A., Ha, N.T., Islam, A.R.M.T. and Salam, R. (2022), "Random forest and nature-inspired algorithms for mapping groundwater nitrate concentration in a coastal multilayer aquifer system", J. Cleaner Product., 343, 130900. https://doi.org/10.1016/j.jclepro.2022.130900. 
  62. Qi, C., Chen, Q., Fourie, A. and Zhang, Q. (2018), "An intelligent modelling framework for mechanical properties of cemented paste backfill", Minerals Eng., 123, 16-27. https://doi.org/10.1016/j.mineng.2018.04.010. 
  63. Rahimi, A. and Breuste, J. (2021), "Why is Lake Urmia drying up? Prognostic modeling with land-use data and artificial neural network", Front. Environ. Sci., 9, 603916. 
  64. Schnerch, D., Dawood, M., Rizkalla, S. and Sumner, E. (2007), "Proposed design guidelines for strengthening of steel bridges with FRP materials", Construct. Build. Mater., 21(5), 1001-1010. https://doi.org/10.1016/j.conbuildmat.2006.03.003. 
  65. Scornet, E., Biau, G. and Vert, J.-P. (2015), "Consistency of random forests", Annals Statistics, 43(4), 1716-1741. 
  66. Shi, X., Yu, X. and Esmaeili-Falak, M. (2023), "Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation", Compos. Struct., 306, 116599. 
  67. Song, C., Guo, J., Gholizadeh, F. and Zhuang, J. (2022), "Quantitative analysis of food safety policy-based on text mining methods", Foods, 11(21), 3421. https://doi.org/10.3390/foods11213421. 
  68. Stone, R.J. (1993), "Improved statistical procedure for the evaluation of solar radiation estimation models", Solar Energy, 51(4), 289-291. https://doi.org/10.1016/0038-092X(93)90124-7. 
  69. Sun, D., Shi, S., Wen, H., Xu, J., Zhou, X. and Wu, J. (2021), "A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping", Geomorphology, 379, 107623. https://doi.org/10.1016/j.geomorph.2021.107623. 
  70. Sun, D., Xu, J., Wen, H. and Wang, D. (2021), "Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest", Eng. Geology, 281, 105972. https://doi.org/10.1016/j.enggeo.2020.105972. 
  71. Teng, J.G., Yu, T. and Fernando, D. (2012), "Strengthening of steel structures with fiber-reinforced polymer composites", J. Construct. Steel Res., 78, 131-143. https://doi.org/10.1016/j.jcsr.2012.06.011. 
  72. Teng, R., Guo, Y., Wang, H., Zhao, Z. and Wang, X. (2021), "Experimental study of bending interface characteristics of CFRP-strengthened long-span steel main beams at different temperatures", Eng. Struct., 235, 111932. https://doi.org/10.1016/j.engstruct.2021.111932. 
  73. Toosi, G. and Ahmadi, M.M. (2023), "Robust process capability indices for multivariate linear profiles", 2023 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, pp. 165-170. https://doi.org/10.1109/SIEDS58326.2023.10137780. 
  74. Vahdatpour, M.S. and Zhang, Y. (2024), "Latency-based motion detection in spiking neural networks", Int. J. Cognitive Language Sci., 18(3), 150-155. 
  75. Wang, H.-T., Wu, G. and Jiang, J.-B. (2016), "Fatigue behavior of cracked steel plates strengthened with different CFRP systems and configurations", J. Compos. Constr, 20(3), 4015078. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000647. 
  76. Wang, H.-T., Wu, G., Dai, Y.-T. and He, X.-Y. (2016), "Determination of the bond-slip behavior of CFRP-to-steel bonded interfaces using digital image correlation", J. Reinforce. Plastics Compos., 35(18), 1353-1367. https://doi.org/10.1177/0731684416651342. 
  77. Wang, S., Jia, H., Abualigah, L., Liu, Q. and Zheng, R. (2021), "An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems", Processes, 9(9), 1551. https://doi.org/10.3390/pr9091551. 
  78. Wang, X., Liu, Y. and Xin, H. (2021), "Bond strength prediction of concrete-encased steel structures using hybrid machine learning method", Structures, 32, 2279-2292. https://doi.org/10.1016/j.istruc.2021.04.018. 
  79. Weiwei, H. (2022), "Classification of sport actions using principal component analysis and random forest based on three-dimensional data", Displays, 72, 102135. https://doi.org/10.1016/j.displa.2021.102135. 
  80. Xia, S. and Teng, J.G. (2005), "Behaviour of FRP-to-steel bonded joints", Proc Int Symp Bond Behav FRP Struct. 
  81. Xin, H., Mosallam, A., Liu, Y., Wang, C. and Zhang, Y. (2017), "Analytical and experimental evaluation of flexural behavior of FRP pultruded composite profiles for bridge deck structural design", Construct. Build. Mater., 150, 123-149. https://doi.org/10.1016/j.conbuildmat.2017.05.212. 
  82. Yu, T., Fernando, D., Teng, J.G. and Zhao, X.L. (2012), "Experimental study on CFRP-to-steel bonded interfaces", Compos. Part B: Eng., 43(5), 2279-2289. https://doi.org/10.1016/j.compositesb.2012.01.024. 
  83. Zaheri, M., Ranjbarnia, M. and Oreste, P. (2024), "Reliability analysis of deep pressurized tunnels excavated in the rock mass with rheological behavior", Transport. Geotech., 101212. 
  84. Zakariazadeh, A. (2022), "Smart meter data classification using optimized random forest algorithm", ISA Transactions, 126, 361-369.  https://doi.org/10.1016/j.isatra.2021.07.051
  85. Zandi, Y., Issakhov, A., Roco Videla, A., Wakil, K., Wang, Q., Cao, Y., Selmi, A., Agdas, A.S., Fu, L. and Qian, X. (2021), "A review study of application of artificial intelligence in construction management and composite beams", Steel Compos. Struct., 39(6), 685-700. https://doi.org/10.12989/scs.2021.39.6.685. 
  86. Zekic-Susac, M., Has, A. and Knezevic, M. (2021), "Predicting energy cost of public buildings by artificial neural networks, CART, and random forest", Neurocomputing, 439, 223-233. https://doi.org/10.1016/j.neucom.2020.01.124. 
  87. Zhang, K., Zhang, Y. and Razzaghzadeh, B., (2024), "Application of the optimal fuzzy-based system on bearing capacity of concrete pile", Steel Compos. Struct., 51(1), 25-41. https://doi.org/10.12989/scs.2024.51.1.025. 
  88. Zhang, W.G. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016. 
  89. Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Front., 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007. 
  90. Zhou, H., Fernando, D., Torero, J.L., Torres, J.P., Maluk, C. and Emberley, R. (2020), "Bond behavior of CFRP-to-steel bonded joints at mild temperatures: experimental study", J. Compos. Construct., 24(6). https://doi.org/10.1061/(ASCE)CC.1943-5614.0001073. 
  91. Zhou, H., Torres, J.P., Fernando, D., Law, A. and Emberley, R. (2019), "The bond behaviour of CFRP-to-steel bonded joints with varying bond properties at elevated temperatures", Eng. Struct., 183, 1121-1133. https://doi.org/10.1016/j.engstruct.2018.10.044. 
  92. Zhou, X., Wen, H., Zhang, Y., Xu, J. and Zhang, W. (2021), "Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization", Geosci. Front., 12(5), 101211. https://doi.org/10.1016/j.gsf.2021.101211. 
  93. Zhu, Y., Huang, L., Zhang, Z. and Bayrami, B. (2022), "Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms", Steel Compos. Struct., 44(3), 389-406. https://doi.org/10.12989/scs.2022.44.3.389