Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Axial capacity of FRP reinforced concrete columns: Empirical, neural and tree based methods

  • Saha Dauji (Nuclear Recycle Board, Bhabha Atomic Research Center)
  • Received : 2021.09.05
  • Accepted : 2023.10.31
  • Published : 2024.02.10
⟨ Previous Next ⟩

Abstract

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

Keywords

Acknowledgement

The author takes this opportunity to express sincere gratitude to Raza et al. (2021) for publishing the collection of data from literature, which was used in this study. The author is thankful to the Editor/s and anonymous Reviewer/s for their critical review and helpful suggestions that helped to improve upon the manuscript.

References

  1. Abdellatif, S. and Raza, A. (2023), "Machine learning model for predicting ultimate capacity of FRP-reinforced normal strength concrete structural elements", Struct. Eng. Mech., 85(3), 315-335. https://doi.org/10.12989/sem.2023.85.3.315.
  2. ACI-318-08 (2008), Building Code Requirements for Structural Concrete-Code and Commentary, American Concrete Institute, Farmington Hills, MI, USA.
  3. ACI-318-11 (2011), Building Code Requirements for Structural Concrete-Code and Commentary, American Concrete Institute, Farmington Hills, MI, USA.
  4. ACI-318-19 (2019), Building Code Requirements for Structural Concrete-Code and Commentary, American Concrete Institute, Farmington Hills, MI, USA.
  5. ACI-440.11-22 (2022), Building Code Requirements for Structural Concrete Reinforced with Glass Fiber-Reinforced Polymer (GFRP) Bars-Code and Commentary, American Concrete Institute, Farmington Hills, MI, USA.
  6. ACI-440.1R-15 (2015), Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures, American Concrete Institute, Indianapolis, IN, USA.
  7. ACI-440.2R-17 (2017), Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures, American Concrete Institute, Indianapolis, IN, USA.
  8. Afifi, M.Z., Mohamed, H.M. and Benmokrane, B. (2014), "Axial capacity of circular concrete columns reinforced with GFRP bars and spirals", J. Compos. Constr., 18, 04013017. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000438.
  9. Ahmad, S. (2014), "An experimental study on correlation between concrete resistivity and reinforcement corrosion rate", Anti Corros. Meth. Mater., 61(3), 158-165. http://doi.org/10.1108/ACMM-07-2013-1285.
  10. Aissa, B. Rabia, B. and Tahar, H.D. (2023), "Predicting and analysis of interfacial stress distribution in RC beams strengthened with composite sheet using artificial neural network", Struct. Eng. Mech., 87(6), 517-527. https://doi.org/10.12989/sem.2023.87.6.517.
  11. Alhozaimy, A., Hussain, R.R., Al-Zaid, R. and Al-Negheimish, A. (2012), "A coupled effect of ambient high relative humidity and varying temperature marine environment on corrosion of reinforced concrete", Constr. Build. Mater., 28, 670-679. https://doi.org/10.1016/j.conbuildmat.2011.10.008.
  12. Al-Majidi, M.H., Lampropoulos, A.P., Cundy, A.B., Tsioulou, O.T. and Al-Rekabi, S. (2018), "A novel corrosion resistant repair technique for existing reinforced concrete (RC) elements using polyvinyl alcohol fibre reinforced geopolymer concrete (PVAFRGC)", Constr. Build. Mater., 164, 603-619. https://doi.org/10.1016/j.conbuildmat.2017.12.213.
  13. Amiri, M. and Hatami, F. (2022), "Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs)", Constr. Build. Mater., 325, 126839. https://doi.org/10.1016/j.conbuildmat.2022.126839.
  14. AS 3600: 2018 (2018), Australian Standard 3600: Concrete Structures, Standards Australia, Sydney, Australia.
  15. Asteris, P.G. and Mokos, V.G. (2020), "Concrete compressive strength using artificial neural networks", Neur. Comput. Appl., 32, 11807-11826. https://doi.org/10.1007/s00521-019-04663-2.
  16. Bashir, R. and Ashour, A.F. (2012), "Neural network modelling for shear strength of concrete members reinforced with FRP bars", Compos.: Part B: Eng., 43(8), 3198-3207. http://doi.org/10.1016/j.compositesb.2012.04.011.
  17. Bertolini, L. and Redaelli, E. (2009), "Throwing power of cathodic prevention applied by means of sacrificial anodes to partially submerged marine reinforced concrete piles: Results of numerical simulations", Corros. Sci., 51, 2218-2230. https://doi.org/10.1016/j.corsci.2009.06.012.
  18. Bertolini, L., Elsener, B., Pedeferri, P. and Polder, R. (2004), Corrosion of Steel in Concrete, Wiley-VCH Verlag GmbH & Co., Berlin.
  19. Bouras, Y. and Li, L. (2023), "Prediction of high-temperature creep in concrete using supervised machine learning algorithms", Constr. Build. Mater., 400, 132828. https://doi.org/10.1016/j.conbuildmat.2023.132828.
  20. Cao, Y., Zandi, Y., Agdas, A.S., Wang, Q., Qian, X., Fu, L., Wakil, K., Selmi, A., Issakhov, A. and Roco-Videla, A. (2021), "A review study of application of artificial intelligence in construction management and composite beams", Steel Compos. Struct., 39(6), 685-700. http://doi.org/10.12989/scs.2021.39.6.685.
  21. Cascardi, A., Micelli, F. and Aeillo, M.A. (2017), "An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns", Eng. Struct., 140, 199-208. http://doi.org/10.1016/j.engstruct.2017.02.047.
  22. Chabib, H.E., Nehdi, M. and Said, A. (2006), "Evaluation of shear capacity of FRP reinforced concrete beams using artificial neural networks", Smart Struct. Syst., 2(1), 81-100. http://doi.org/10.12989/sss.2006.2.1.082.
  23. Congro, M., Monteiro, V.M.D.A., Brandao, A.L.T., Santos, B.F.D., Roehl, D. and Silva, F.D.A. (2021), "Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks", Constr. Build. Mater., 303, 124502. https://doi.org/10.1016/j.conbuildmat.2021.124502.
  24. CSA S806-02 (2002), Design and Construction of Building Structures with Fibre-Reinforced Polymer, Canadian Standards Association CAN/CSA, Toronto, ON, Canada.
  25. CSA S806-12 (2012), Design and Construction of Building Structures with Fibre-Reinforced Polymer, Canadian Standards Association CAN/CSA, Toronto, ON, Canada.
  26. Dang, H.V., Raza, M., Tran-Ngoc, H., Bui-Tien, T. and Nguyen, X. (2021), "Connection stiffness reduction analysis in steel bridge via deep CNN and model experimental data", Struct. Eng. Mech., 77(4), 495-508. http://doi.org/10.12989/sem.2021.77.4.495.
  27. Darvishan, E. (2021), "The punching shear capacity estimation of FRP-strengthened RC slabs using artificial neural network and group method of data handling", J. Rehab. Civil Eng., 9(1), 102-113. https://doi.org/10.22075/JRCE.2020.20335.1407.
  28. Dauji, S. (2018b), "New approach for identification of suitable vibration attenuation relationship for underground blasts", Eng. J., 22(4), 147-159. https://doi.org/10.4186/ej.2018.22.4.147.
  29. Dauji, S. (2016), "Empirical model for evaluation of concrete corrosion current density", Concrete Res. Lett., 7(3), 104-112.
  30. Dauji, S. (2018a), "Neural prediction of concrete compressive strength", Int. J. Mater. Struct. Integr., 12(1/2/3), 17-35. https://doi.org/10.1504/IJMSI.2018.10014931.
  31. Dauji, S. (2019a), "Reinforcement corrosion in coastal and marine concrete: A review", Chall. J. Concrete Res. Lett., 9(2), 62-70. https://doi.org/10.20528/cjcrl.2018.02.003
  32. Dauji, S. (2019b), "Estimation of corrosion current density from resistivity of concrete with neural network", INAE Lett., 4, 111-121. https://doi.org/10.1007/s41403-019-00071-z.
  33. Dauji, S. (2019c), "Estimation of capacity of eccentrically loaded single angle struts with decision trees", Chall. J. Struct. Mech., 5(1), 1-8. https://doi.org/10.20528/cjsmec.2019.01.001.
  34. Dauji, S. (2020a), "Prediction of concrete spall damage under blast: Neural approach with synthetic data", Comput. Concrete, 26(6), 533-546. https://doi.org/10.12989/cac.2020.26.6.533.
  35. Dauji, S. (2020b), "Prediction accuracy of underground blast variables: Decision tree and artificial neural network", Int. J. Earthq. Impact Eng., 3(1), 40-59. https://doi.org/10.1504/IJEIE.2020.105382.
  36. Dauji, S. (2021a), "Bond strength of corroded reinforcement in concrete: Neural and tree based approaches", Struct. Monit. Mainten., 8(3), 235-255. https://doi.org/10.12989/smm.2021.8.3.235.
  37. Dauji, S. (2021b), "A re-look into modified scaled distance regression for prediction of blast-induced ground vibration", Int. J. Geotech. Earthq. Eng., 12(1), 22-39. https://doi.org/10.4018/IJGEE.2021010103.
  38. Dauji, S. and Deo, M.C. (2020), "Improving numerical current prediction with Model Tree", Ind. J. Geo Marine Sci., 49(08), 1350-1358.
  39. 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.
  40. Dong, S.G., Zhao, B., Lin, C.J., Du, R.G., Hu, R.G. and Zhang, G.X. (2012), "Corrosion behavior of epoxy/zinc duplex coated rebar embedded in concrete in ocean environment", Constr. Build. Mater., 28, 72-78. https://doi.org/10.1016/j.conbuildmat.2011.08.026.
  41. Duffo, G.S. and Farina, S.B. (2009), "Development of an embeddable sensor to monitor the corrosion process of new and existing reinforced concrete structures", Constr. Build. Mater., 23, 2746-2751. https://doi.org/10.1016/j.conbuildmat.2009.04.001.
  42. Eidgahee, D.R., Soleymani, A., Hasani, H., Kontoni, D.P.N. and Jahangir, H. (2023), "Flexural capacity estimation of FRP reinforced T-shaped concrete beams via soft computing techniques", Comput. Concrete, 32(1), 1-13. https://doi.org/10.12989/cac.2023.32.1.001.
  43. Elchalakani, M., Ma, G., Aslani, F. and Duan, W. (2017), "Design of GFRP-Reinforced rectangular concrete columns under eccentric axial loading", Mag. Concr. Res., 69, 865-877. https://doi.org/10.1680/jmacr.16.00437.
  44. Felix, E.F., Carrazedo, R. and Possan, E. (2021), "Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis", Constr. Build. Mater., 266(A), 121050. https://doi.org/10.1016/j.conbuildmat.2020.121050.
  45. Gong, C., Jianzhong, L., Cuicui, C., Changfeng, L. and Liang, S. (2012), "Study on silane impregnation for protection of high performance concrete", Chin. Mater. Conf., Procedia Eng., 27, 301-307. https://doi.org/10.1016/j.proeng.2011.12.456.
  46. Haddad, R. and Haddad, M. (2020), "Predicting fiber-reinforced polymer-concrete bond strength using artificial neural networks: A comparative analysis study", Struct. Concrete, 22(1), 38-49. https://doi.org/10.1002/suco.201900298.
  47. Hadhood, A., Mohamed, H.M. and Benmokrane, B. (2017), "Axial load-moment interaction diagram of circular concrete columns reinforced with CFRP bars and spirals: Experimental and theoretical investigations", J. Compos. Constr., 21, 04016092. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000748.
  48. Haykin, S.O. (2008), Neural Networks and Machine Learning, Pearson Education, New Delhi, India.
  49. Hong, W.K., Nguyen, M.C. and Pham, T.D. (2023), "Optimized interaction P-M diagram for rectangular reinforced concrete column based on artificial neural networks", J. Asian Arch. Build. Eng., 22(1), 201-225. https://doi.org/10.1080/13467581.2021.2018697.
  50. Huang, J., Zhang, J., Li, X., Qiao, Y., Zhang, R. and Kumar, G.S. (2023), "Investigating the effects of ensemble and weight optimization approaches on neural networks' performance to estimate the dynamic modulus of asphalt concrete", Road Mater. Pavem. Des., 24(8), 1939-1959. https://doi.org/10.1080/14680629.2022.2112061.
  51. Jekabsons, G. (2016), M5PrimeLab: M5' Regression Tree, Model Tree, and Tree Ensemble Toolbox for Matlab/Octave, http://www.cs.rtu.lv/jekabsons/.
  52. Jiang, W., Liu, M., Peng, Y., Wu, L. and Wang, Y. (2021), "HDCB-Net: A neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges", IEEE Trans. Indus. Inform., 17(8), 5485-5494. https://doi.org/10.1109/TII.2020.3033170.
  53. Kaeseberg, S., Messerer, D. and Holschemacher, K. (2020), "Experimental study on concrete under combined FRP-Steel confinement", Mater., 13, 4467. https://doi.org/10.3390/ma13204467.
  54. Kashani, A.R., Gandomi, M., Camp, C.V., Rostamian, M. and Gandomi, A.H. (2020), "Metaheuristics in civil engineering: A review", Metaheur. Comput. Appl., 1(1), 19-42. https://doi.org/10.12989/mca.2020.1.1.019.
  55. Kawasaki, Y., Tomoda, Y. and Ohtsu, M. (2010), "AE monitoring of corrosion process in cyclic wet-dry test", Constr. Build. Mater., 24, 2353-2357. https://doi.org/10.1016/j.conbuildmat.2010.05.006.
  56. Koroglu, M.A. (2019), "Artificial neural network for predicting the flexural bond strength of FRP bars in concrete", Sci. Eng. Compos. Mater., 26(1), 12-29. https://doi.org/10.1515/secm2017-0155.
  57. Kulkarni, K.S., Narayan, K.S.B. and Yaragal, S.C. (2023), "Decision tree model for predicting exposure temperature and retention period-Dependent behavior of blended concrete", Iran. J. Sci. Technol., Trans. Civil Eng., 1-13. https://doi.org/10.1007/s40996-023-01142-6.
  58. Kumar, A., Arora, H.C., Kapoor, N.R., Kontoni, D.P.N., Kumar, K., Jahangir, H. and Bhushan, B. (2023), "Practical applicable model for estimating the carbonation depth in fly-ash based concrete structures by utilizing adaptive neuro-fuzzy inference system", Comput. Concrete, 32(2), 119-138. https://doi.org/10.12989/cac.2023.32.2.119.
  59. Kumar, A., Arora, H.C., Kapoor, N.R., Kumar, K., Hadzima-Nyarko, M. and Radu, D. (2023), "Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams", Scientif. Report., 13, 2857. https://doi.org/10.1038/s41598-023-30037-9
  60. Lee, Y., Seo, S., You, I., Yun, T.S. and Zi, G. (2023), "Prediction of calcium leaching resistance of fly ash blended cement composites using artificial neural network", Comput. Concrete, 31(4), 315-325. https://doi.org/10.12989/cac.2023.31.4.315.
  61. Li, C., Mei, X., Dias, D., Cui, Z. and Zhou, J. (2023), "Compressive strength prediction of rice husk ash concrete using a hybrid artificial neural network model", Mater., 16, 3135. https://doi.org/10.3390/ma16083135.
  62. Lim, J.C. (2014), "Design model for FRP-confined normal- and high-strength concrete square and rectangular columns", Mag. Concrete Res., 66(20), 1020-1035. http://doi.org/10.1680/macr.14.00059.
  63. Lin, G. and Teng, J.G. (2020), "Advanced stress-strain model for FRP-confined concrete in square columns", Compos. Part B, 197, 108149. https://doi.org/10.1016/j.compositesb.2020.108149.
  64. Liu, J., Han, X., Pan, Y., Cui, K. and Xiao, Q. (2023), "Physics‑assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete", Scientif. Report., 13, 9078. https://doi.org/10.1038/s41598-023-36303-0.
  65. Liu, X. and Wang, X. (2023), "Application of artificial intelligence for solving the engineering problems", Struct. Eng. Mech., 85(1), 15-27. https://doi.org/10.12989/sem.2023.85.1.015.
  66. Luat, N.V., Shin, J., Han, S.W., Nguyen, N.V. and Lee, K. (2021), "Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models-A new approach", Steel Compos. Struct., 40(3), 461-479. http://doi.org/10.12989/scs.2021.40.3.461.
  67. Ly, H.B., Nguyen, M.H. and Pham, B.T. (2021), "Metaheuristic optimization of Levenberg-Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength", Neur. Comput. Appl., 33, 17331-17351. https://doi.org/10.1007/s00521-021-06321-y.
  68. Ma, C.K., Lee, Y.H., Awang, A.Z., Omar, W., Mohammad, S. and Liang, M. (2019), "Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects", Neur. Comput. Appl., 31, 711-717. https://doi.org/10.1007/s00521-017-3104-7.
  69. Majlesi, A., Koodiani, H.K., Rincon, O.T.D., Montoya, A., Millano, V., Torres-Acosta, A.A. and Troconis, B.C.R. (2023), "Artificial neural network model to estimate the long-term carbonation depth of concrete exposed to natural environments", J. Build. Eng., 74, 106545. https://doi.org/10.1016/j.jobe.2023.106545.
  70. Martinez, I. and Andrade, C. (2008), "Application of EIS to cathodically protected steel: Tests in sodium chloride solution and in chloride contaminated concrete", Corros. Sci., 50, 2948-2958. https://doi.org/10.1016/j.corsci.2008.07.012.
  71. Mashrei, M.A., Seracino, R. and Rahman, M.S. (2013), "Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints", Constr. Build. Mater., 40, 812-821. http://doi.org/10.1016/j.conbuildmat.2012.11.109.
  72. Megalooikonomou, K.G. and Papavasileiou, G.S. (2019), "Analytical stress-strain model for FRP-confined rectangular RC columns", Front. Built Environ., 5(39), 1-14. https://doi.org/10.3389/fbuil.2019.00039.
  73. Mesalami, N.E., Abed, F. and Refai, A.E. (2021a), "Concrete columns reinforced with GFRP and BFRP bars under concentric and eccentric loads: Experimental testing and analytical investigation", J. Compos. Constr., 25, 04021003. https://doi.org/10.1061/(ASCE)CC.1943-5614.0001115.
  74. Mesalami, N.E., Abed, F. and Refai, A.E. (2021b), "Response of concrete columns reinforced with longitudinal and transverse BFRP bars under concentric and eccentric loading", Compos. Struct., 255, 113057. https://doi.org/10.1016/j.compstruct.2020.113057.
  75. Naderpour, H. and Kheyroddin, A.V.G.G. (2010), "Prediction of FRP-confined compressive strength of concrete using artificial neural networks", Compos. Struct., 92, 2817-2829. https://doi.org/10.1016/j.compstruct.2010.04.008.
  76. Najmi, L.A. and Abed, F. (2020), "Evaluation of FRP bars under compression and their performance in RC columns", Mater., 13, 4541. https://doi.org/10.3390/ma13204541.
  77. Nanehkaran, Y.A., Azarafza, M., Pusatli, T., Bonab, M.H., Irani, A.E., Kouhdarag, M., Chen, J. and Derakhshani, R. (2023), "Deep learning method for compressive strength prediction for lightweight concrete", Comput. Concrete, 32(3), 327-337. https://doi.org/10.12989/cac.2023.32.3.327.
  78. Naser, M.Z. (2020), "Machine learning assessment of fiber-reinforced polymer-strengthened and reinforced concrete members", ACI Struct. J., 117(6), 237-251.
  79. Naser, M.Z. and Tapeh, A.T.G. (2023), "Causality, causal discovery, causal inference and counterfactuals in civil engineering: Causal machine learning and case studies for knowledge discovery", Comput. Concrete, 31(4), 277-292. https://doi.org/10.12989/cac.2023.31.4.277.
  80. Ngo, N.T., Le, H.A., Huynh, V.V. and Pham, T.P.T. (2021), "Machine learning models for inferring the axial strength in short concrete-filled steel tube columns infilled with various strength concrete", Eng. J., 25(7), 135-145. https://doi.org/10.4186/ej.2021.25.7.135.
  81. Nguyen, A.Q., Klysz, G., Deby, F. and Balayssac, J.P. (2018), "Assessment of the electrochemical state of steel reinforcement in water saturated concrete by resistivity measurement", Constr. Build. Mater., 171, 455-466. https://doi.org/10.1016/j.conbuildmat.2018.01.155.
  82. Nguyen, D.H., Bui-Tien, T., Roeck, G.D. and Wahab, M.A. (2021), "Damage detection in structures using modal curvatures gapped smoothing and deep learning", Struct. Eng. Mech., 77(1), 47-56. http://doi.org/10.12989/sem.2021.77.1.047.
  83. Nguyen, Q.H., Ly, H.B., Nguyen, T.A., Phan, V.H., Nguyen, L.K. and Tran, V.Q. (2021), "Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams", PLoS ONE, 16(4), e0247391. https://doi.org/10.1371/journal.pone.0247391.
  84. Nikoo, M., Hadzima-Nyarko, M., Khademi, F. and Mohasseb, S. (2017), "Estimation of fundamental period of reinforced concrete shear wall buildings using self organization feature map", Struct. Eng. Mech., 63(2), 237-249. http://doi.org/10.12989/sem.2017.63.2.237.
  85. Oller, E., Torres, L. and Diego A.D. (2023), "Embedded Fibre Reinforced Polymer (FRP) reinforcement in concrete structures according to the new version of Eurocode 2", Hormigon y Acero, 74(299-300), 199-210. https://doi.org/10.33586/hya.2022.3098.
  86. Pan, L., Wang, Y., Li, K. and Guo, X. (2022), "Predicting compressive strength of green concrete using hybrid artificial neural network with genetic algorithm", Struct. Concrete, 24(2), 1980-1996. https://doi.org/10.1002/suco.202200034.
  87. Parhi, S.K. and Panigrahi, S.K. (2023), "Alkali-silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms", Asian J. Civil Eng., 25(1), 1091-1113. https://doi.org/10.1007/s42107-023-00799-8.
  88. Parhi, S.K. and Patro, S.K. (2023), "Compressive strength prediction of PET fiber-reinforced concrete using Dolphin echolocation optimized decision tree-based machine learning algorithms", Asian J. Civil Eng., 25(1), 977-996. https://doi.org/10.1007/s42107-023-00826-8.
  89. Piro, N.S., Mohammed, A., Hamad, S.M. and Kurda, R. (2023), "Artificial neural networks (ANN), MARS, and adaptive network based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement", Neur. Comput. Appl., 35, 13293-13319. https://doi.org/10.1007/s00521-023-08439-7.
  90. Quinlan, J.R. (1992), C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, USA.
  91. Ranjbar, I., Toufigh, V. and Boroushaki, M. (2022), "A combination of deep learning and genetic algorithm for predicting the compressive strength of high-performance concrete", Struct. Concrete, 23(4), 2405-2418. https://doi.org/10.1002/suco.202100199.
  92. Raza, A., Shah, S.A.R., Alhazmi, H., Abrar, M. and Razzaq, S. (2021), "Strength profile pattern of FRP-reinforced concrete structures: A performance analysis through finite element analysis and empirical modeling technique", Polym., 13(1256), 1-33. https://doi.org/10.3390/polym13081265.
  93. Raza, A., Shah, S.A.R., Haq, F., Arshad, H., Raza, S.S., Farhan, M. and Waseem, M. (2020), "Prediction of axial load-carrying capacity of GFRP-reinforced concrete columns through artificial neural networks", Struct., 28, 1551-1571. https://doi.org/10.1016/j.istruc.2020.10.010.
  94. Rokach, L. and Maimon, O. (2015), Data Mining with Decision Trees: Theory and Applications, World Scientific, Singapore.
  95. Saadoon, A.S. and Malik, H.S. (2017), "Prediction of ultimate load of concrete beams reinforced with FRP bars using artificial neural networks", Al-Qadisiyah J. Eng. Sci., 10(1), 11-25.
  96. Sahib, M.M. and Kovacs, G. (2023), "Using Artificial Neural Network in the reverse design of a composite sandwich structure", Struct. Eng. Mech., 85(5), 635-644. https://doi.org/10.12989/sem.2023.85.5.635.
  97. Saleem, M. and Gutierrez, H. (2021), "Using artificial neural network and non-destructive test for crack detection in concrete surrounding the embedded steel reinforcement", Struct. Concrete, 22(5), 2849-2867. https://doi.org/10.1002/suco.202000767.
  98. Sangeetha, P. and Shanmugapriya, M. (2020), "GFRP wrapped concrete column compressive strength prediction through neural network", SN Appl. Sci., 2, 2036. https://doi.org/10.1007/s42452-020-03753-4.
  99. Shamayleh, O.A. and Far, H. (2023), "Utilising artificial neural networks for prediction of properties of geopolymer concrete", Comput. Concrete, 31(4), 327-335. https://doi.org/10.12989/cac.2023.31.4.327.
  100. Shubina, V., Gaillet, L., Chaussadent, T., Meylheuc, T. and Creus, J. (2016), "Biomolecules as a sustainable protection against corrosion of reinforced carbon steel in concrete", J. Clean. Prod., 112, 666-671. https://doi.org/10.1016/j.jclepro.2015.07.124.
  101. Shukla, N.K., Garg, A., Bhutto, J., Aggarwal, M., Raja, M.R., Hussein, H.S., Khan, T.M.Y. and Sabherwal, P. (2023), "An insight into the prediction of mechanical properties of concrete using machine learning techniques", Comput. Concrete, 32(3), 263-286. https://doi.org/10.12989/cac.2023.32.3.263.
  102. Sowjanya, G.V., Anadinni, S.B. and Mahadevaiah, T. (2023), "Application and validation of internally cured concrete strength characteristics by machine learning", Mater. Today: Proc., https://doi.org/10.1016/j.matpr.2023.03.552.
  103. Subramaniam, D.N., Jeyananthan, P. and Sathiparan, N. (2023), "Soft computing techniques to predict the electrical resistivity of pervious concrete", Asian J. Civil Eng., 1-12. https://doi.org/10.1007/s42107-023-00806-y.
  104. Sun, Y., Deng, K., Zhan, Y., Huang, W. and Yin, C. (2021), "Interfacial behavior of segmental concrete-filled Basalt FRP tube under cyclic loading", Steel Compos. Struct., 40(1), 65-74. http://doi.org/10.12989/scs.2021.40.1.065.
  105. Taffese, W.Z. and Espinosa-Leal, L. (2023), "Multitarget regression models for predicting compressive strength and chloride resistance of concrete", J. Build. Eng., 72, 106523. https://doi.org/10.1016/j.jobe.2023.106523.
  106. Tam, V.W.Y., Butera, A., Le, K.N. and Silva, L.C.F.D. and Evangelista, A.C.J. (2022), "A prediction model for compressive strength of CO concrete using regression analysis and artificial neural networks", Constr. Build. Mater., 324, 126689. https://doi.org/10.1016/j.conbuildmat.2022.126689.
  107. Tan, Z.X., Thambiratnam, D.P., Chan, T.H.T., Gordan, M. and Razak, H.A. (2020), "Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network", Struct. Infrastr. Eng., 16(9), 1247-1261. https://doi.org/10.1080/15732479.2019.1696378.
  108. Tao, Y., Gu, J.B., Chen, J.F. and Feng, P. (2021), "Behavior of hybrid CFST with FRP-confined UHPC core under axial compression", Steel Compos. Struct., 40(1), 75-85. http://doi.org/10.12989/scs.2021.40.1.075.
  109. Tipu, R.K., Suman, and Batra, V. (2023), "Enhancing prediction accuracy of workability and compressive strength of high-performance concrete through extended dataset and improved machine learning models", Asian J. Civil Eng., 1-12. https://doi.org/10.1007/s42107-023-00768-1.
  110. Trinh, M.C. and Jun, H. (2021), "Stochastic vibration analysis of functionally graded beams using artificial neural networks", Struct. Eng. Mech., 78(5), 529-543. http://doi.org/10.12989/sem.2021.78.5.529.
  111. Varol, E., Benzer, D. and Ozcan, N.T. (2023), "An adaptive neuro-fuzzy inference system (ANFIS) model to predict the pozzolanic activity of natural pozzolans", Comput. Concrete, 31(2), 85-95. https://doi.org/10.12989/cac.2023.31.2.085.
  112. Venkatesan, P., Palaniswamy, N. and Rajagopal, K. (2006), "Corrosion performance of coated reinforcing bars embedded in concrete and exposed to natural marine environment", Progr. Organ. Coat., 56, 8-12. https://doi.org/10.1016/j.porgcoat.2006.01.011.
  113. Wasserman, P.D. (1993), Advanced Methods in Neural Computing, John Wiley & Sons, Inc., New York, USA.
  114. Witten, I.H. and Frank, E. (2000), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, USA.
  115. Xie, H., Fang, H., Li, X., Wan, L., Wu, P. and Yu, Y. (2021). "Low-velocity impact damage detection and characterization in composite sandwich panels using infrared thermography", Compos. Struct., 269, 114008. https://doi.org/10.1016/j.compstruct.2021.114008.
  116. Xie, H., Shen, C., Fang, H., Han, J. and Cai, W. (2022). "Flexural property evaluation of web reinforced GFRP-PET foam sandwich panel: Experimental study and numerical simulation", Compos. Part B: Eng., 234, 109725. https://doi.org/10.1016/j.compositesb.2022.109725.
  117. Xu, J. and Yao, W. (2011), "Electrochemical studies on the performance of conductive overlay material in cathodic protection of reinforced concrete", Constr. Build. Mater., 25, 2655-2662. https://doi.org/10.1016/j.conbuildmat.2010.12.015.
  118. Yamani, W.H.A., Ghunimat, D.M. and Bisharah, M.M. (2023), "Modeling and predicting the sensitivity of high performance concrete compressive strength using machine learning methods", Asian J. Civil Eng., 24, 1943-1955. https://doi.org/10.1007/s42107-023-00614-4.
  119. Yang, Y., Sun, D., Xue, Y., Yu, Y., An, K. and Chen, Y. (2021), "Seismic performance of RC columns with encased prefabricated high-strength CFST core", Steel Compos. Struct., 39(6), 723-736. http://doi.org/10.12989/scs.2021.39.6.723.
  120. Yazdanpanah, O., Chang, M., Park, M. and Chae, Y. (2023), "Force-deformation relationship prediction of bridge piers through stacked LSTM network using fast and slow cyclic tests", Struct. Eng. Mech., 85(4), 469-484. https://doi.org/10.12989/sem.2023.85.4.469.
  121. Zand, J.P., Katebi, J. and Yaghmaei-Sabegh, S. (2023), "A generalized ANFIS controller for vibration mitigation of uncertain building structure", Struct. Eng. Mech., 87(3), 231-242. https://doi.org/10.12989/sem.2023.87.3.231.
  122. Zhang, J.C., Liu, X.Y., Zeng, L., Du, G.F. and Xiao, J.H. (2021a), "Seismic behavior of cross-shaped concrete-filled steel tubular columns", Steel Compos. Struct., 40(3), 405-420. http://doi.org/10.12989/scs.2021.40.3.405.
  123. Zhang, Z., Zhou, D., Fang, H., Zhang, J. and Li, X. (2021b), "Analysis of layered rectangular plates under thermo-mechanical loads considering temperature-dependent material properties", Appl. Math. Model., 92, 244-260. https://doi.org/10.1016/j.apm.2020.10.036.
  124. Zhang, Z., Zhou, D., Lim, Y.M., Fang, H. and Huo, R. (2022), "Analytical solutions for multilayered pipes with temperature-dependent properties under non-uniform pressure and thermal load", Appl. Math. Model., 106, 369-389. https://doi.org/10.1016/j.apm.2022.01.024.
  125. Zhao, Y., Goulias, D. and Seremi,S. (2021), "Enhancing prediction accuracy of concrete compressive strength using stacking ensemble machine learning", Comput. Concrete, 32(3), 233-246. https://doi.org/10.12989/cac.2023.32.3.233.
  126. Zhao, Y., Hu, H., Song, C. and Wang, Z. (2022), "Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network", Measure., 194, 110993. https://doi.org/10.1016/j.measurement.2022.110993.
  127. Zheng, W., Zarman, A. and Farooq, F. (2023), "Sustainable predictive model of concrete utilizing waste ingredient: Individual alogrithms with optimized ensemble approaches", Mater. Today Commun., 35, 105901. https://doi.org/10.1016/j.mtcomm.2023.105901.