Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications

Journal article

Hassanin, H., Fan , W., Chen, Y., Li, J., Sun, Y., Feng, F. and Sareh, P. 2021. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures.
AuthorsHassanin, H., Fan , W., Chen, Y., Li, J., Sun, Y., Feng, F. and Sareh, P.

Machine learning is one of the key pillars of industry 4.0 that has enabled rapid technological advancement through establishing complex connections among heterogeneous and highly complex engineering data automatically. Once the machine learning model is trained appropriately, it becomes able to effectively predict and make decisions. The technology is rapidly evolving and has found numerous applications in various branches of engineering due to its preponderance. This study is focused on exploring the recent advances of machine learning and its applications in reinforced concrete bridges. It covers a range of different machine learning techniques exploited in structural design, construction quality management, bridge engineering, and the inspection of reinforced concrete bridges. This review demonstrated that machine learning algorithms have established new research directions in bridge engineering, in particular for applications such as the form-finding of innovative long-span structures, structural reinforcement, and structural optimization.

KeywordsMachine learning; Deep learning; Reinforced concrete bridges; Strength prediction; Structural health monitoring
Digital Object Identifier (DOI)
Publication process dates
Accepted30 Jun 2021
Deposited09 Jul 2021
Accepted author manuscript
File Access Level
Output statusIn press

[1] Falcone R, Lima C, Martinelli E. Soft computing techniques in structural and earthquake engineering: a literature review. Eng Struct 2020;207:110269. https://
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] Cevik, A., et al., Support vector machines in structural engineering: a review. Journal of Civil Engineering and Management, 2015. 21(3): p. 261-281.
[8] 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.
[9] Salehi H, Burgueño R. Emerging artificial intelligence methods in structural engineering. Eng Struct 2018;171(15):170–89.
[10] 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.
[11] 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.
[12] 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.
[13] 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. (ASCE)BE.1943-5592.0001103.
[14] 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.
[15] 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.
[16] Freischlad M, Schnellenbach-Held M. A machine learning approach for the support of preliminary structural design. Adv Eng Inf 2005;19(4):281–7.
[17] Jootoo A, Lattanzi D. Bridge type classification: supervised learning on a modified NBI data set. J Comput Civil Eng 2017;31(6):4017063. (ASCE)CP.1943-5487.0000712.
[18] Charalampakis AE, Papanikolaou VK. Machine learning design of R/C columns. Eng Struct 2021;226:111412.
[19] Bilal M, Oyedele L. Guidelines for applied machine learning in construction industry-a case of profit margins estimation. Adv Eng Inf 2020;43:101013.
[20] Tixier A-P, Hallowell MR, Rajagopalan B, Bowman D. Application of machine learning to construction injury prediction. Autom Constr 2016;69:102–14.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] Kirts S, Panagopoulos OP, Xanthopoulos P, Nam BH. Soil-compressibility prediction models using machine learning. J Comput Civil Eng 2018;32(1):4017067. https://
[28] Asadi M, Eftekhari M, Bagheripour MH. Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 2011;11(2):1932–7.
[29] 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.
[30] 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.
[31] 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.
[32] 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. 151823.
[33] Xiong J, Zhang T, Shi S. Machine learning of mechanical properties of steels. Science China Technological Sciences 2020;63(7):1247–55.
[34] 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.
[35] 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.
[36] 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.
[37] 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.
[38] Skibniewski M, Arciszewski T, Lueprasert K. Constructability analysis: machine learning approach. J Comput Civil Eng 1997;11(1):8–16.
[39] 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://
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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://
[45] 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.
[46] 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. su1203106310.37473/dac/10.3390/su12031063.
[47] Chen C, Zhang L, Tiong RLK. A novel learning cloud bayesian network for risk measurement. Appl Soft Comput 2020;87:105947. 2019.105947.
[48] 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.
[49] 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.
[50] Cheng K, Lu Z. Structural reliability analysis based on ensemble learning of surrogate models. Struct Saf 2020;83:101905. 2019.101905.
[51] 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.
[52] Hurtado JE, Alvarez DA. Neural-network-based reliability analysis: a comparative study. Comput Methods Appl Mech Eng 2001;191(1–2):113–32.
[53] 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.
[54] 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.
[55] Wang K, Ruan T, Xie F. LR-BCA: label ranking for bridge condition assessment. IEEE Access 2021;9:4038–48.
[56] 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.
[57] Dang, H., et al., Deep learning-based detection of structural damage using time- series data. Structure and Infrastructure Engineering, 2020(9).
[58] 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. 0001085.
[59] 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.
[60] 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.
[61] Karanci E, Betti R. Modeling corrosion in suspension bridge main cables. I: annual corrosion rate. J Bridge Eng 2018;23(6):4018025. BE.1943-5592.0001233.
[62] Liu H, Zhang Y. Bridge condition rating data modeling using deep learning algorithm. Struct Infrastruct Eng 2020;16(10):1447–60.
[63] 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. 1061/(ASCE)BE.1943-5592.0001432.
[64] 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.
[65] 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.
[66] 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.
[67] 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.
[68] 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.
[69] 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.
[70] 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.
[71] 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.
[72] 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.
[73] 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.
[74] 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. 1088/1361-6501/ab79c8.
[75] 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.
[76] De Luca F, Hossain MI, Kobourov S. Symmetry detection and classification in drawings of graphs. Cham: Springer International Publishing; 2019.
[77] 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

Permalink -

  • 5
    total views
  • 1
    total downloads
  • 5
    views this month
  • 1
    downloads this month

Export as

Related outputs

Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review
Hassanin, H., Bidare, P., Jiménez, A. and Essa, K. 2021. Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review. Advances in Manufacturing.
Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches
Hassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M. 2021. Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches. Materials. 14 (8), p. 2056.
3DP printing of oral solid formulations: a systematic review
Brambilla, C., Okafor-Muo, O., Hassanin, H. and ElShaer, A. 2021. 3DP printing of oral solid formulations: a systematic review. Pharmaceutics. 13 (3), p. 358.
Powder-based laser hybrid additive manufacturing of metals: A review
Hassanin, H. 2021. Powder-based laser hybrid additive manufacturing of metals: A review. The International Journal of Advanced Manufacturing Technology.
Micro-fabrication of ceramics: additive manufacturing and conventional technologies
Hassanin, H., Essa, K., Elshaer, A., Imbaby, M. and El-Sayed, T. E. 2021. Micro-fabrication of ceramics: additive manufacturing and conventional technologies. Journal of Advanced Ceramics. 10, pp. 1-27.
4D Printing of origami structures for minimally invasive surgeries using functional scaffold
Langford, T, Mohammed, A., Essa, K., Elshaer, A. and Hassanin, H. 2020. 4D Printing of origami structures for minimally invasive surgeries using functional scaffold. Applied Sciences. 11 (1), p. 332.
Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile
Hassanin, H., Mohammed, M., Abdel-Wahab, A. and Essa, K 2020. Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile. Materials.
3D printing of solid oral dosage forms: numerous challenges with unique opportunities
Hassanin, H. 2020. 3D printing of solid oral dosage forms: numerous challenges with unique opportunities. Journal of Pharmaceutical Sciences.
Design optimisation of additively manufactured titanium lattice structures for biomedical implants
El-Sayed, M.A., Essa, K., Ghazy, M. and Hassanin, H. 2020. Design optimisation of additively manufactured titanium lattice structures for biomedical implants. The International Journal of Advanced Manufacturing Technology.
4D Printing of NiTi auxetic structure with improved ballistic performance
Hassanin, H., Abena, A., Elsayed, M.A. and Essa, K. 2020. 4D Printing of NiTi auxetic structure with improved ballistic performance. Micromachines. 11 (8), p. 745.