[1] Turan Aİ, Ayaz Y, Yalciner H, Kumbasaroglu A. An experimental evaluation on structural performance level of corroded reinforced concrete frames. Engineering Structures. 2025;325:119479.
[2] Song H-W, Saraswathy V. Corrosion monitoring of reinforced concrete structures-A. Int J Electrochem Sci. 2007;2:1-28.
[3] Lin H, Zhao Y, Feng P, Ye H, Ozbolt J, Jiang C et al. State-of-the-art review on the bond properties of corroded reinforcing steel bar. Construction and Building Materials. 2019;213:216-33.
[4] Hughes B, Videla C. Design criteria for early-age bond strength in reinforced concrete. Materials and Structures. 1992;25:445-63.
[5] Cairns J, Abdullah R. Evaluation of bond pullout tests and their relevance to structural performance. The Structural Engineer. 1995;73:179-85.
[6] Dahou Z, Sbartaï ZM, Castel A, Ghomari F. Artificial neural network model for steel–concrete bond prediction. Engineering Structures. 2009;31:1724-33.
[7] Cairns J, Jones K. Influence of rib geometry on strength of lapped joints: an experimental and analytical study. Magazine of Concrete Research. 1995;47:253-62.
[8] Andrade MSA, Ribeiro JCL, de Oliveira DS, Pedroti LG, Santos CFR. Experimental evaluation of concrete-reinforcement bond: Bond failure mechanisms after exposure to elevated temperatures. Engineering Structures. 2024;311:118148.
[9] Gambarova PG, Rosati GP. Bond and splitting in bar pull-out: behavioural laws and concrete cover role. Magazine of Concrete Research. 1997;49:99-110.
[10] Luccioni BM, López DE, Danesi RF. Bond-slip in reinforced concrete elements. Journal of structural engineering. 2005;131:1690-8.
[11] Alhawat M, Ashour A. Bond strength between corroded steel reinforcement and recycled aggregate concrete. Structures: Elsevier; 2019. p. 369-85.
[12] Auyeung Y, Balaguru P, Chung L. Bond behavior of corroded reinforcement bars. Materials Journal. 2000;97:214-20.
[13] Horrigmoe G, Saether I, Antonsen R, Arntsen B. Laboratory investigations of steel bar corrosion in concrete: Sustainable Bridges Background document SB3. 10. 2007.
[14] Yalciner H, Eren O, Sensoy S. An experimental study on the bond strength between reinforcement bars and concrete as a function of concrete cover, strength and corrosion level. Cement and Concrete Research. 2012;42:643-55.
[15] Güneyisi EM, Mermerdaş K, Gültekin A. Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements. Materials and Structures. 2016;49:3195-215.
[16] Hoang N-D, Tran X-L, Nguyen H. Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Computing and Applications. 2020;32:7289-309.
[17] Saifullah M, Clark L. Effect of corrosion rate on the bond strength of corroded reinforcement. Corrosion and corrosion protection of steel in concrete. 1994;1:591-602.
[18] Almusallam AA, Al-Gahtani AS, Aziz AR. Effect of reinforcement corrosion on bond strength. Construction and building materials. 1996;10:123-9.
[19] Fang C, Lundgren K, Chen L, Zhu C. Corrosion influence on bond in reinforced concrete. Cement and concrete research. 2004;34:2159-67.
[20] Amleh L. Bond deterioration of reinforcing steel in concrete due to corrosion [PhD Thesis]. Montreal: McGill University; 2000.
[21] Kranc S, Sagüés AA. Detailed modeling of corrosion macrocells on steel reinforcing in concrete. Corrosion Science. 2001;43:1355-72.
[22] Steffens A, Dinkler D, Ahrens H. Modeling carbonation for corrosion risk prediction of concrete structures. Cement and Concrete Research. 2002;32:935-41.
[23] Parthiban T, Ravi R, Parthiban G, Srinivasan S, Ramakrishnan K, Raghavan M. Neural network analysis for corrosion of steel in concrete. Corrosion Science. 2005;47:1625-42.
[24] Karray F, Karray FO, De Silva CW. Soft computing and intelligent systems design: theory, tools, and applications: Pearson Education; 2004.
[25] Ai D, Mo F, Han Y, Wen J. Automated identification of compressive stress and damage in concrete specimen using convolutional neural network learned electromechanical admittance. Engineering Structures. 2022;259:114176.
[26] Elshafey AA, Dawood N, Marzouk H, Haddara M. Crack width in concrete using artificial neural networks. Engineering Structures. 2013;52:676-86.
[27] Köroğlu MA, Ceylan M, Arslan MH, Ilki A. Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network. Engineering Structures. 2012;42:23-32.
[28] Tanyildizi H. Predicting bond strength of corroded reinforcement by deep learning. Computers and Concrete. 2022;29:145-59.
[29] Shirkhani A, Davarnia D, Azar BF. Prediction of bond strength between concrete and rebar under corrosion using ANN. Computers and Concrete, An International Journal. 2019;23:273-9.
[30] Ikumi T, Galeote E, Pujadas P, de la Fuente A, López-Carreño R. Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete. Computers & Structures. 2021;256:106640.
[31] Ghorbani B, Arulrajah A, Narsilio G, Horpibulsuk S, Bo MW. Hybrid Formulation of Resilient Modulus for Cohesive Subgrade Soils Utilizing CPT Test Parameters. Journal of Materials in Civil Engineering. 2020;32:06020011.
[32] Pan L, Novák L, Lehký D, Novák D, Cao M. Neural network ensemble-based sensitivity analysis in structural engineering: Comparison of selected methods and the influence of statistical correlation. Computers & Structures. 2021;242:106376.
[33] Mate C, Jimeńez L. Forecasting exchange rates with the iMLP: New empirical insight on one multi-layer perceptron for interval time series (ITS). Engineering Applications of Artificial Intelligence. 2021;104:104358.
[34] Ly H-B, Nguyen MH, Pham BT. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications. 2021;33:17331-51.
[35] Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology. 1996;49:1225-31.
[36] Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M. Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renewable energy. 2018;116:309-23.
[37] Yeh I-C. Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research. 1998;28:1797-808.
[38] Topçu İB, Sarıdemir M. Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction and Building Materials. 2008;22:532-40.
[39] Trtnik G, Kavčič F, Turk G. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics. 2009;49:53-60.
[40] Dias W, Pooliyadda S. Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials. 2001;15:371-9.
[41] Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software. 2010;25:747-59.
[42] Delnavaz M, Ayati B, Ganjidoust H. Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN). Journal of hazardous materials. 2010;179:769-75.
[43] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS’95. Proceedings of the sixth international symposium on micro machine and human science: Ieee Piscataway, NJ, USA; 1995. p. 39-43.
[44] He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering applications of artificial intelligence. 2007;20:89-99.
[45] Yildiz AR. A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2012;226:1340-51.
[46] Alam MN. Particle swarm optimization: Algorithm and its codes in matlab. ResearchGate. 2016;8:1-10.
[47] Pathak NN, Mahanti G, Singh SK, Mishra JK, Chakraborty A. Synthesis of thinned planar circular array antennas using modified particle swarm optimization. Progress In Electromagnetics Research Letters. 2009;12:87-97.
[48] Qi C, Ly H-B, Chen Q, Le T-T, Le VM, Pham BT. Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach. Chemosphere. 2020;244:125450.
[49] Yong W, Zhou J, Jahed Armaghani D, Tahir M, Tarinejad R, Pham BT et al. A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles. Engineering with Computers. 2021;37:2111-27.
[50] Pham BT, Le LM, Le T-T, Bui K-TT, Le VM, Ly H-B et al. Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research. 2020;237:104845.
[51] Ly H-B, Le T-T, Vu H-LT, Tran VQ, Le LM, Pham BT. Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability. 2020;12:2709.
[52] Topçu İB, Boğa AR, Hocaoğlu FO. Modeling corrosion currents of reinforced concrete using ANN. Automation in Construction. 2009;18:145-52.
[53] Gao P, Li Y, Sun J, Huang GH. A Monte-Carlo-based interval De Novo programming method for optimal system design under uncertainty. Engineering Applications of Artificial Intelligence. 2018;72:30-42.
[54] Green II RC, Wang L, Alam M, Singh C. Intelligent state space pruning for Monte Carlo simulation with applications in composite power system reliability. Engineering applications of artificial intelligence. 2013;26:1707-24.
[55] Martín-Fernández L, Ruiz DP, Torija AJ, Míguez J. A Bayesian method for model selection in environmental noise prediction. Journal of Environmental Informatics. 2016;27:31-42.
[56] Yu L, Li Y, Huang GH, Shan B. A hybrid fuzzy-stochastic technique for planning peak electricity management under multiple uncertainties. Engineering Applications of Artificial Intelligence. 2017;62:252-64.
[57] Eberhart R, Shi Y. Particle swarm optimization: developments, applications and resources. Proceedings of the 2001 congress on evolutionary computation (IEEE Cat No 01TH8546): IEEE; 2001. p. 81-6.
[58] Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation. 2002;6:58-73.
[59] Poli R, Kennedy J, Blackwell T. Particle swarm optimization: An overview. Swarm intelligence. 2007;1:33-57.
[60] Engelbrecht AP. Fundamentals of Computational Swarm Intelligence. Hoboken, NJ: Wiley. 2005.
