Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods

References

• Zhang B, Zhu H, Dong ZQ, et al. Mechanical properties and durability of FRP-reinforced coral aggregate concrete structures: a critical review. Mater Today Commun. 2023;35:105656. doi: 10.1016/j.mtcomm.2023.105656
   Google Scholar
• Yang ZY, Zhan X, Zhu H, et al. Mesoscopic investigation of the matrix pores and ITZ effects on the mechanical properties of seawater sea-sand coral aggregate concrete. J Build Eng. 2024;109375. doi: 10.1016/j.jobe.2024.109375
   Google Scholar
• Sun Z, Zhang L, Niu D, et al. Time-varying model for predicting the resistivity of coral aggregate concrete. Constr Build Mater. 2020;265:120588. doi: 10.1016/j.conbuildmat.2020.120588
   Web of Science ®Google Scholar
• Yang ZY, Lu F, Zhan X, et al. Mechanical properties and mesoscopic damage characteristics of basalt fibre-reinforced seawater sea-sand slag-based geopolymer concrete. J Buil Eng. 2024;84:108688. doi: 10.1016/j.jobe.2024.108688
   Web of Science ®Google Scholar
• Sun Z, Niu D, Wang X, et al. Bond behavior of coral aggregate concrete and corroded Cr alloy steel bar. J Buil Eng. 2022;61:105294. doi: 10.1016/j.jobe.2022.105294
   Web of Science ®Google Scholar
• Yang ZY, Zhan X, Zhu H, et al. Performance-based alkali-activated seawater sea-sand concrete: Mixture optimization for mechanical, environmental, and economical objectives. Constr Build Mater. 2020;409:134156. doi: 10.1016/j.conbuildmat.2023.134156
   Google Scholar
• Sun Z, Niu D, Luo D, et al. Hybrid machine learning-based prediction model for the bond strength of corroded Cr alloy-reinforced coral aggregate concrete. Mater Today Commun. 2023;35:106141. doi: 10.1016/j.mtcomm.2023.106141
   Web of Science ®Google Scholar
• Zhang B, Zhu H, Li F, et al. Compressive stress-strain behavior of seawater coral aggregate concrete incorporating eco-efficient alkali-activated slag materials. Constr Build Mater. 2021;299:123886. doi: 10.1016/j.conbuildmat.2021.123886
   Web of Science ®Google Scholar
• Wang G, Wu Q, Zhou H, et al. Diffusion of chloride ion in coral aggregate seawater concrete under marine environment. Constr Build Mater. 2021;284:122821. doi: 10.1016/j.conbuildmat.2021.122821
   Web of Science ®Google Scholar
• Da B, Chen Y, Yu H, et al. Preparation technology, mechanical properties and durability of coral aggregate seawater concrete in the island-reef environment. J Clean Prod. 2022;339:130572. doi: 10.1016/j.jclepro.2022.130572
   Web of Science ®Google Scholar
• Li YL, Sun Z, Li YQ. Exploring the shear performance and predictive shear capacity of corroded RC columns utilizing the modified compression-field theory: An investigative study. Eng Struct. 2024;302. doi: 10.1016/j.engstruct.2023.117390
   Web of Science ®Google Scholar
• Da B, Yu H, Ma H, et al. Electrochemical study on steel corrosion in coral aggregate seawater concrete. Emerg Mater Res. 2020;9(3):642–654. doi: 10.1680/jemmr.19.00105
   Web of Science ®Google Scholar
• Li YL, Sun Zhen, Li YQ. Time-dependent combined index seismic resilience assessment of shear-critical RC bridge piers with height-varying corrosion. Eng Struct. 2024;308. doi: 10.1016/j.engstruct.2024.117957
   Google Scholar
• Liu S, Wang X, Ali YMS, et al. Experimental study on eccentric compression behavior of slender rectangular concrete columns reinforced with steel and BFRP bars. Eng Struct. 2023;293:116626. doi: 10.1016/j.engstruct.2023.116626
   Web of Science ®Google Scholar
• Sharifi-Asl S, Mao F, Lu P, et al. Exploration of the effect of chloride ion concentration and temperature on pitting corrosion of carbon steel in saturated Ca (OH)2 solution. Corros Sci. 2015;98:708–715. doi: 10.1016/j.corsci.2015.06.010
   Web of Science ®Google Scholar
• Angst U, Elsener B, Larsen CK, et al. Critical chloride content in reinforced concrete—A review. Cement Concr Res. 2009;39(12):1122–1138. doi: 10.1016/j.cemconres.2009.08.006
   Web of Science ®Google Scholar
• Niu D, Zhang L, Fu Q, et al. Critical conditions and life prediction of reinforcement corrosion in coral aggregate concrete. Constr Build Mater. 2020;238:117685. doi: 10.1016/j.conbuildmat.2019.117685
   Web of Science ®Google Scholar
• Zhang L, Niu D, Wen B, et al. Initial-corrosion condition behavior of the Cr and Al alloy steel bars in coral concrete for marine construction. Cem Concr Compos. 2021;120:104051. doi: 10.1016/j.cemconcomp.2021.104051
   Web of Science ®Google Scholar
• Wu Z, Yu H, Ma H, et al. Rebar corrosion in coral aggregate concrete: determination of chloride threshold by LPR. Corros Sci. 2020;163:108238. doi: 10.1016/j.corsci.2019.108238
   Web of Science ®Google Scholar
• Wu Z, Yu H, Ma H, et al. Rebar corrosion behavior of coral aggregate seawater concrete by electrochemical techniques. Anti-Corrosion Methods Materials. 2020;67(1):59–72. doi: 10.1108/ACMM-05-2019-2128
   Web of Science ®Google Scholar
• Nguyen NH, Vo TP, Lee S, et al. Heuristic algorithm-based semi-empirical formulas for estimating the compressive strength of the normal and high performance concrete. Constr Build Mater. 2021;304:124467. doi: 10.1016/j.conbuildmat.2021.124467
   Web of Science ®Google Scholar
• Yang Z, Zhu H, Zhang B, et al. Short-term creep behaviors of seawater sea-sand coral aggregate concrete: An experimental study with Rheological model and neural network. Constr Build Mater. 2023;363:129786. doi: 10.1016/j.conbuildmat.2022.129786
   Web of Science ®Google Scholar
• Mahmood W, Mohammed AS, Sihag P, et al. Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches. Arch Civil Mech Eng. 2021;22(1):19. doi: 10.1007/s43452-021-00341-0
   Web of Science ®Google Scholar
• Emad W, Mohammed AS, Kurda R, et al. Prediction of concrete materials compressive strength using surrogate models. Structures. 2022;46:1243–1267. doi: 10.1016/j.istruc.2022.11.002
   Web of Science ®Google Scholar
• Asteris PG, Roussis PC, Douvika MG. Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors. 2017;17(6):1344. doi: 10.3390/s17061344
   PubMed Web of Science ®Google Scholar
• Asteris PG, Kolovos KG. Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl. 2019;31(1):409–424. doi: 10.1007/s00521-017-3007-7
   Google Scholar
• Alkayem NF, Shen L, Mayya A, et al. Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future perspectives. J Buil Eng. 2023;83:108369. doi: 10.1016/j.jobe.2023.108369
   Web of Science ®Google Scholar
• Sathiparan N, Jeyananthan P. Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity. Case Stud NondestrTest Eval. 2024;1–24. doi: 10.1080/10589759.2024.2304257
   Google Scholar
• Zhou S, Lei Y, Zhang ZX, et al. Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties. Case Stud NondestrTest Eval. 2024:1–24. doi: 10.1080/10589759.2024.2313569
   Google Scholar
• Lin X, Zhu G, Yu S, et al. Prediction of corrosion degree of reinforced concrete based on improved QPSO-neural network. Case Stud NondestrTest Eval. 2023;38(3):412–430. doi: 10.1080/10589759.2022.2122967
   Google Scholar
• Zhu Y, Macdonald DD, Qiu J, et al. Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data. Corros Sci. 2021;185:109438. doi: 10.1016/j.corsci.2021.109438
   Web of Science ®Google Scholar
• Chun P, Ujike I, Mishima K, et al. Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results. Constr Build Mater. 2020;253:119238. doi: 10.1016/j.conbuildmat.2020.119238
   Web of Science ®Google Scholar
• Zhou Y, Gao L, Fan X, et al. Prediction of concrete structure service life based on the principle of neural connections in brain circuits. NeuroQuantology. 2018;16(5): doi: 10.14704/nq.2018.16.5.1398
   Google Scholar
• Atha DJ, Jahanshahi MR. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Struct Health Monit. 2018;17(5):1110–1128. doi: 10.1177/1475921717737051
   Web of Science ®Google Scholar
• Huang D, Niu D, Zheng H, et al. Study on chloride transport performance of eco-friendly coral aggregate concrete in marine environment. Constr Build Mater. 2020;258:120272. doi: 10.1016/j.conbuildmat.2020.120272
   Web of Science ®Google Scholar
• Huang D, Niu D, Su L, et al. Diffusion behavior of chloride in coral aggregate concrete in marine salt-spray environment. Constr Build Mater. 2022;316:125878. doi: 10.1016/j.conbuildmat.2021.125878
   Web of Science ®Google Scholar
• Huang D, Niu D, Su L, et al. Durability of coral aggregate concrete under coupling action of sulfate, chloride and drying-wetting cycles. Case Studies Construction Mater. 2022;16:e01003. doi: 10.1016/j.cscm.2022.e01003
   Google Scholar
• Da B. Study on preparation technology, durability and mechanical properties of components of high strength whole coral seawater concrete. Nanjing University of Aeronautics and Astronautics; 2017.
   Google Scholar
• Da B, Yu H, Ma H, et al. Chloride diffusion study of coral concrete in a marine environment. Constr Build Mater. 2016;123:47–58. doi: 10.1016/j.conbuildmat.2016.06.135
   Web of Science ®Google Scholar
• Wang Y, Zhang S, Niu D, et al. Effects of silica fume and blast furnace slag on the mechanical properties and chloride ion distribution of coral aggregate concrete. Constr Build Mater. 2019;214:648–658. doi: 10.1016/j.conbuildmat.2019.04.149
   Web of Science ®Google Scholar
• Niu D, Su L, Luo Y, et al. Experimental study on mechanical properties and durability of basalt fiber reinforced coral aggregate concrete. Constr Build Mater. 2020;237:117628. doi: 10.1016/j.conbuildmat.2019.117628
   Web of Science ®Google Scholar
• Wang Y, Zhang S, Niu D, et al. Strength and chloride ion distribution brought by aggregate of basalt fiber reinforced coral aggregate concrete. Constr Build Mater. 2020;234:117390. doi: 10.1016/j.conbuildmat.2019.117390
   Web of Science ®Google Scholar
• Ly A, Marsman M, Wagenmakers EJ. Analytic posteriors for Pearson’s correlation coefficient. Stat Neerl. 2018;72(1):4–13. doi: 10.1111/stan.12111
   PubMed Web of Science ®Google Scholar
• Mudelsee M. Estimating Pearson’s correlation coefficient with bootstrap confidence interval from serially dependent time series. Math Geol. 2003;35:651–665. doi: 10.1023/B:MATG.0000002982.52104.02
   Google Scholar
• Ao Y, Li H, Zhu L, et al. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J Pet Sci Eng. 2019;174:776–789. doi: 10.1016/j.petrol.2018.11.067
   Web of Science ®Google Scholar
• Singh U, Rizwan M, Alaraj M, et al. A machine learning-based gradient boosting regression approach for wind power production forecasting: a step towards smart grid environments. Energies. 2021;14(16):5196. doi: 10.3390/en14165196
   Web of Science ®Google Scholar
• Dhiman HS, Deb D, Guerrero JM. Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renew Sust Energ Rev. 2019;108:369–379. doi: 10.1016/j.rser.2019.04.002
   Web of Science ®Google Scholar
• Sun Z, Li Y, Li Y, et al. Prediction of chloride ion concentration distribution in basalt-polypropylene fiber reinforced concrete based on optimized machine learning algorithm. Mater Today Commun. 2023;36:106565. doi: 10.1016/j.mtcomm.2023.106565
   Web of Science ®Google Scholar
• Lv W, Sun Z, Li Y, et al. Hybrid machine learning-based model for predicting chloride ion concentration in coral aggregate concrete and its ethically aligned graphical user interface design. Mater Today Commun. 2023;37:107053. doi: 10.1016/j.mtcomm.2023.107053
   Web of Science ®Google Scholar
• Jangir P, Buch H, Mirjalili S, et al. MOMPA: multi-objective marine predator algorithm for solving multi-objective optimization problems. Evol Int. 2023;16(1):169–195. doi: 10.1007/s12065-021-00649-z
   Web of Science ®Google Scholar
• Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimedia Tools Appl. 2021;80(5):8091–8126. doi: 10.1007/s11042-020-10139-6
   PubMed Web of Science ®Google Scholar
• Sun Z, Li Y, Li Y, et al. Investigation on compressive strength of coral aggregate concrete: hybrid machine learning models and experimental validation. J Buil Eng. 2024;82:108220. doi: 10.1016/j.jobe.2023.108220
   Web of Science ®Google Scholar
• Apostolopoulou M, Asteris PG, Armaghani DJ, et al. Mapping and holistic design of natural hydraulic lime mortars. Cement Concr Res. 2020;136:106167. doi: 10.1016/j.cemconres.2020.106167
   Web of Science ®Google Scholar
• Asteris PG, Koopialipoor M, Armaghani DJ, et al. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput Appl. 2021;33(19):13089–13121. doi: 10.1007/s00521-021-06004-8
   Web of Science ®Google Scholar
• Asteris PG, Armaghani DJ, Hatzigeorgiou GD, et al. Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concrete, An Int J. 2019;24(5):469–488.
   Web of Science ®Google Scholar
• 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(9):4501–4532. doi: 10.1007/s00521-020-05244-4
   Web of Science ®Google Scholar
• Farsi M, Mohamadian N, Ghorbani H, et al. Predicting formation pore-pressure from well-log data with hybrid machine-learning optimization algorithms. Nat Resour Res. 2021;30(5):3455–3481. doi: 10.1007/s11053-021-09852-2
   Web of Science ®Google Scholar
• Sun Z, Li Y, Yang Y, et al. Splitting tensile strength of basalt fiber reinforced coral aggregate concrete: optimized XGBoost models and experimental validation. Constr Build Mater. 2024;416:135133. doi: 10.1016/j.conbuildmat.2024.135133
   Web of Science ®Google Scholar
• Sun Z, Li Y, Bei Y, et al. Compressive strength resistance coefficient of sustainable concrete in sulfate environments: hybrid machine learning model and experimental verification. Mater Today Commun. 2024;39:108667. doi: 10.1016/j.mtcomm.2024.108667
   Google Scholar
• Fu Q, Zhang Z, Zhao X, et al. Effect of nano calcium carbonate on hydration characteristics and microstructure of cement-based materials: a review. J Buil Eng. 2022;50:104220. doi: 10.1016/j.jobe.2022.104220
   Web of Science ®Google Scholar
• Xu W, Tang Z, Xie Y, et al. Understanding the impact of synthesis parameters on the pore structure properties of fly ash-based geopolymers. Constr Build Mater. 2024;411:134640. doi: 10.1016/j.conbuildmat.2023.134640
   Web of Science ®Google Scholar
• Su L, Ma Z, Niu D, et al. Corrosion characteristics of basalt-polypropylene hybrid fiber concrete under the compound salt and drying-wetting cycles. Constr Build Mater. 2024;419:135529. doi: 10.1016/j.conbuildmat.2024.135529
   Web of Science ®Google Scholar
• Zhang B. Durability of sustainable geopolymer concrete: a critical review. Sustainable Mater Technol. 2024;40:e00882. doi: 10.1016/j.susmat.2024.e00882
   Google Scholar
• Sun Z, Li Y, Su L, et al. Investigation of electrical resistivity for fiber-reinforced coral aggregate concrete. Constr Build Mater. 2024;414:135011. doi: 10.1016/j.conbuildmat.2024.135011
   Web of Science ®Google Scholar
• Chen Z, Wang X, Ding L, et al. Spalling resistance and mechanical properties of ultra-high performance concrete reinforced with multi-scale basalt fibers and hybrid fibers under elevated temperature. J Buil Eng. 2023;77:107435. doi: 10.1016/j.jobe.2023.107435
   Web of Science ®Google Scholar
• Chen Z, Wang X, Ding L, et al. Mechanical properties of a novel UHPC reinforced with macro basalt fibers. Constr Build Mater. 2023;377:131107. doi: 10.1016/j.conbuildmat.2023.131107
   Web of Science ®Google Scholar
• Huang D, Feng Y, Xia Q, et al. Research on mechanical properties and durability of early frozen concrete: A review. Constr Build Mater. 2024;425. doi: 10.1016/j.conbuildmat.2024.135988
   Google Scholar
• Sun Z, He W, Niu D, et al. Resistivity prediction model for basalt–polypropylene fiber-reinforced concrete. Buildings. 2022;13(1):84. doi: 10.3390/buildings13010084
   Web of Science ®Google Scholar