Falcone R, Lima C, Martinelli E. Soft computing techniques in structural and earthquake engineering: a literature review. Eng Struct 2020;207:110269. https:// doi.org/10.1016/j.engstruct.2020.110269.
 Azimi M, Eslamlou AD, Pekcan G. Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review. Sensors 2020;20 (10):2778.
 Dong C-Z, Catbas FN. A review of computer vision-based structural health monitoring at local and global levels. Structural Health Monitoring 2021;20(2): 692–743.
 Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ. A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech Syst Sig Process 2021;147: 107077. https://doi.org/10.1016/j.ymssp.2020.107077.
 Sun L, Shang Z, Xia Ye., Bhowmick S, Nagarajaiah S. Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng 2020;146(5):4020073. https://doi. org/10.1061/(ASCE)ST.1943-541X.0002535.
 Bilal M, Oyedele LO, Qadir J, Munir K, Ajayi SO, Akinade OO, et al. Big Data in the construction industry: a review of present status, opportunities, and future trends. Adv Eng Inf 2016;30(3):500–21.
 Cevik, A., et al., Support vector machines in structural engineering: a review. Journal of Civil Engineering and Management, 2015. 21(3): p. 261-281.
 Lee S, Ha J, Zokhirova M, Moon H, Lee J. Background information of deep learning for structural engineering. Arch Comput Methods Eng 2018;25(1):121–9.
 Salehi H, Burgueño R. Emerging artificial intelligence methods in structural engineering. Eng Struct 2018;171(15):170–89.
 Mangalathu S, Jeon J. Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Eng Struct 2018;160(1):85–94.
 Noori Hoshyar A, Rashidi M, Liyanapathirana R, Samali B. Algorithm development for the non-destructive testing of structural damage. Applied Sciences-Basel 2019;9 (14):2810. https://doi.org/10.3390/app9142810.
 Mangalathu S, Jeon J-S. Machine learning-based failure mode recognition of circular reinforced concrete bridge columns: comparative study. J Struct Eng 2019; 145(10):4019104. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002402.
 Alipour M, Harris DK, Barnes LE, Ozbulut OE, Carroll J. Load-capacity rating of bridge populations through machine learning: application of decision trees and random forests. J Bridge Eng 2017;22(10):4017076. https://doi.org/10.1061/ (ASCE)BE.1943-5592.0001103.
 Bayar G, Bilir T. A novel study for the estimation of crack propagation in concrete using machine learning algorithms. Constr Build Mater 2019;215(8):670–85.
 Chou J-S, Pham T-P-T, Nguyen T-K, Pham A-D, Ngo N-T. Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Comput 2020;24(5):3393–411.
 Freischlad M, Schnellenbach-Held M. A machine learning approach for the support of preliminary structural design. Adv Eng Inf 2005;19(4):281–7.
 Jootoo A, Lattanzi D. Bridge type classification: supervised learning on a modified NBI data set. J Comput Civil Eng 2017;31(6):4017063. https://doi.org/10.1061/ (ASCE)CP.1943-5487.0000712.
 Charalampakis AE, Papanikolaou VK. Machine learning design of R/C columns. Eng Struct 2021;226:111412. https://doi.org/10.1016/j.engstruct.2020.111412.
 Bilal M, Oyedele L. Guidelines for applied machine learning in construction industry-a case of profit margins estimation. Adv Eng Inf 2020;43:101013.
 Tixier A-P, Hallowell MR, Rajagopalan B, Bowman D. Application of machine learning to construction injury prediction. Autom Constr 2016;69:102–14.
 Yu Y, Li W, Li J, Nguyen TN. A novel optimised self-learning method for compressive strength prediction of high performance concrete. Constr Build Mater 2018;184:229–47.
 Chou J-S, Tsai C-F, Pham A-D, Lu Y-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Constr Build Mater 2014;73:771–80.
 Nguyen T, Kashani A, Ngo T, Bordas S. Deep neural network with high-order neuron for the prediction of foamed concrete strength. Comput-Aided Civ Infrastruct Eng 2019;34(4):316–32.
 Yazdi JS, Kalantary F, Yazdi HS. Prediction of elastic modulus of concrete using support vector committee method. J Mater Civ Eng 2013;25(1):9–20.
 Naser MZ, Uppala VA. Properties and material models for construction materials post exposure to elevated temperatures. Mech Mater 2020;142:103293. https://doi. org/10.1016/j.mechmat.2019.103293.
 Chou J-S, Ngo N-T. Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system. Neural Comput Appl 2018;30(7): 2129–44.
 Kirts S, Panagopoulos OP, Xanthopoulos P, Nam BH. Soil-compressibility prediction models using machine learning. J Comput Civil Eng 2018;32(1):4017067. https:// doi.org/10.1061/(ASCE)CP.1943-5487.0000713.
 Asadi M, Eftekhari M, Bagheripour MH. Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 2011;11(2):1932–7.
 Pham BT, Nguyen MD, Bui K-T, Prakash I, Chapi K, Bui DT. A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. Catena 2019;173:302–11.
 Miyazawa Y, Briffod F, Shiraiwa T, Enoki M. Prediction of cyclic stress-strain property of steels by crystal plasticity simulations and machine learning. Materials 2019;12(22):3668. https://doi.org/10.3390/ma12223668.
 Shiraiwa T, Miyazawa Y, Enoki M. Prediction of fatigue strength in steels by linear regression and neural network. Mater Trans 2018;60(2):189–98.
 Wang C, Shen C, Cui Q, Zhang C, Xu W. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels. J Nucl Mater 2020;529:151823. https://doi.org/10.1016/j.jnucmat.2019. 151823.
 Xiong J, Zhang T, Shi S. Machine learning of mechanical properties of steels. Science China Technological Sciences 2020;63(7):1247–55.
 Olalusi OB, Spyridis P. Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear. Adv Eng Softw 2020;147:102832. https://doi.org/10.1016/j.advengsoft.2020.102832.
 Cheng M, Cao M. Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Eng Appl Artif Intell 2014;28(2):86–96.
 Chou J, Ngo N, Pham A. Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. J Comput Civil Eng 2016;30(1):8215001.
 Ly H-B, Le T-T, Vu H-L, Tran VQ, Le LM, Pham BT. Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability 2020;12(7):2709. https://doi.org/10.3390/su12072709.
 Skibniewski M, Arciszewski T, Lueprasert K. Constructability analysis: machine learning approach. J Comput Civil Eng 1997;11(1):8–16.
 Hwang H-J, Baek J-W, Kim J-Y, Kim C-S. Prediction of bond performance of tension lap splices using artificial neural networks. Eng Struct 2019;198:109535. https:// doi.org/10.1016/j.engstruct.2019.109535.
 Wang Y, Geem Z, Nagai K. Bond strength assessment of concrete-corroded rebar interface using artificial neutral network. Applied Sciences-Basel 2020;10(14):4724.
 Garcia-Sanchez D, Fernandez-Navamuel A, Sánchez DZ, Alvear D, Pardo D. Bearing assessment tool for longitudinal bridge performance. Journal of Civil Structural Health Monitoring 2020;10(5):1023–36.
 Tinoco J, Correia A, Cortez P. Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Comput Geotech 2014;55 (1):132–40.
 Cheng M-Y, Cao M-T, Wu Y-W. Predicting equilibrium scour depth at bridge piers using evolutionary radial basis function neural network. J Comput Civil Eng 2015; 29(5):4014070. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000380.
 Kim I, Fard MY, Chattopadhyay A. Investigation of a bridge pier scour prediction model for safe design and inspection. J Bridge Eng 2015;20(6):4014088. https:// doi.org/10.1061/(ASCE)BE.1943-5592.0000677.
 Sharafi H, Ebtehaj I, Bonakdari H, Zaji AH. Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Nat Hazards 2016;84(3):2145–62.
 Tien Bui D, Shirzadi A, Amini A, Shahabi H, Al-Ansari N, Hamidi S, et al. A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers. Sustainability 2020;12(3):1063. https://doi.org/10.3390/ su1203106310.37473/dac/10.3390/su12031063.
 Chen C, Zhang L, Tiong RLK. A novel learning cloud bayesian network for risk measurement. Appl Soft Comput 2020;87:105947. https://doi.org/10.1016/j.asoc. 2019.105947.
 Liao K-W, Chien F-S, Ju R-J. Safety evaluation of a water-immersed bridge against multiple hazards via machine learning. Applied Sciences-Basel 2019;9(15):3116. https://doi.org/10.3390/app9153116.
 Xiang Z, Chen J, Bao Y, Li H. An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mech Syst Sig Process 2020;140:106684. https://doi.org/10.1016/j.ymssp.2020.106684.
 Cheng K, Lu Z. Structural reliability analysis based on ensemble learning of surrogate models. Struct Saf 2020;83:101905. https://doi.org/10.1016/j.strusafe. 2019.101905.
 Papadrakakis M, Papadopoulos V, Lagaros N. Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 1996;136(1–2):145–63.
 Hurtado JE, Alvarez DA. Neural-network-based reliability analysis: a comparative study. Comput Methods Appl Mech Eng 2001;191(1–2):113–32.
 Chojaczyk AA, Teixeira AP, Neves LC, Cardoso JB, Guedes Soares C. Review and application of Artificial Neural Networks models in reliability analysis of steel structures. Struct Saf 2015;52:78–89.
 Mangalathu S, Jeon J-S. Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques. Earthquake Eng Struct Dyn 2019;48(11):1238–55.
 Wang K, Ruan T, Xie F. LR-BCA: label ranking for bridge condition assessment. IEEE Access 2021;9:4038–48.
 Pan H, Azimi M, Yan F, Lin Z. Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges. J Bridge Eng 2018;23(6): 4018033. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001199.
 Dang, H., et al., Deep learning-based detection of structural damage using time- series data. Structure and Infrastructure Engineering, 2020(9).
 Kostić B, Gül M. Vibration-based damage detection of bridges under varying temperature effects using time-series analysis and artificial neural networks. J Bridge Eng 2017;22(10):4017065. https://doi.org/10.1061/(ASCE)BE.1943-5592. 0001085.
 Chalouhi E, et al. Vibration-based SHM of railway bridges using machine learning: the influence of temperature on the health prediction. International Conference on Experimental Vibration Analysis for Civil Engineering Structures. San Diego, CA: Univ California San Diego; 2018. p. 200–11.
 Li S, Sun L. Detectability of bridge-structural damage based on fiber-optic sensing through deep-convolutional neural networks. J Bridge Eng 2020;25(4):4020012. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001531.
 Karanci E, Betti R. Modeling corrosion in suspension bridge main cables. I: annual corrosion rate. J Bridge Eng 2018;23(6):4018025. https://doi.org/10.1061/(ASCE) BE.1943-5592.0001233.
 Liu H, Zhang Y. Bridge condition rating data modeling using deep learning algorithm. Struct Infrastruct Eng 2020;16(10):1447–60.
 Figueiredo E, Moldovan I, Santos A, Campos P, Costa JCWA. Finite element-based machine-learning approach to detect damage in bridges under operational and environmental variations. J Bridge Eng 2019;24(7):4019061. https://doi.org/10. 1061/(ASCE)BE.1943-5592.0001432.
 Li S, Zuo X, Li Z, Wang H. Applying deep learning to continuous bridge deflection detected by fiber optic gyroscope for damage detection. Sensors 2020;20(3):911. https://doi.org/10.3390/s20030911.
 Assaad R, El-adaway IH. Bridge infrastructure asset management system: comparative computational machine learning approach for evaluating and predicting deck deterioration conditions. J Infrastruct Syst 2020;26(3):4020032. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000572.
 Chen Q, et al. Evaluation and prediction for effect of conductive gussasphalt mixture on corrosion of steel bridge deck. Constr Build Mater 2019;228:116837.
 Deng Y, Zhang M, Feng D-M, Li A-Q. Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning. Struct Infrastruct Eng 2021;17(2):233–48.
 Kim H, Ahn E, Shin M, Sim S-H. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring 2019;18(3): 725–38.
 Chun P-j., Izumi S, Yamane T. Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine. Comput-Aided Civ Infrastruct Eng 2021;36(1):61–72.
 Slonski M, Tekieli M. 2D digital image correlation and region-based convolutional neural network in monitoring and evaluation of surface cracks in concrete structural elements. Materials 2020;13(16):166–80.
 Deng Lu., Chu H-H, Shi P, Wang W, Kong X. Region-based CNN method with deformable modules for visually classifying concrete cracks. Applied Sciences 2020; 10(7):2528. https://doi.org/10.3390/app10072528.
 Wang SY, Zhang PZ, Zhou SY, Wei DB, Ding F, Li FK. A computer vision based machine learning approach for fatigue crack initiation sites recognition. Comput Mater Sci 2020;171:109259. https://doi.org/10.1016/j.commatsci.2019.109259.
 Chen K, Yadav A, Khan A, Meng Y, Zhu K. Improved crack detection and recognition based on convolutional neural network. Modelling and Simulation in Engineering 2019;2019:1–8.
 Li G, Liu Q, Zhao S, Qiao W, Ren X. Automatic crack recognition for concrete bridges using a fully convolutional neural network and naive Bayes data fusion based on a visual detection system. Meas Sci Technol 2020;31(7):75403. https://doi.org/10. 1088/1361-6501/ab79c8.
 Chen Y, Sareh P, Feng J, Sun Q. A computational method for automated detection of engineering structures with cyclic symmetries. Comput Struct 2017;191:153–64.
 De Luca F, Hossain MI, Kobourov S. Symmetry detection and classification in drawings of graphs. Cham: Springer International Publishing; 2019.
 Chen, Y., et al., Particle swarm optimization-based metaheuristic design generation of non-trivial flat-foldable origami tessellations with degree-4 vertices. Journal of Mechanical Design, 2021. 143(1): p. 011703