Bridge Health Monitoring: A Review

Authors: Mohammad Bakhshandeh

Abstract:

Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: Structural health monitoring, bridge health monitoring, sensor-based methods, machine-learning algorithms, model-based techniques, sensor placement, data acquisition, data analysis.

References:

[1] Zhu, X., Wang, K., & Li, H. (2020). Review on bridge health monitoring systems: Sensor placement, data acquisition, and damage identification. Sensors, 20(22), 6589. doi: 10.3390/s20226589
[2] Lim, I. K., Kim, J. T., & Kim, H. S. (2015). Bridge health monitoring: Review and application. Journal of Performance of Constructed Facilities, 29(2), 04014101. doi: 10.1061/(ASCE)CF.1943-5509.0000542.
[3] Duan, Z., Xiang, H., Wang, X., & Chen, G. (2021). A review of bridge health monitoring: From sensing technology to data analytics. Measurement, 181, 109617. doi: 10.1016/j.measurement.2021.109617.
[4] Gholampour, A., Shariatmadar, H., Zhang, Y., & Jiang, X. (2020). Bridge health monitoring: State-of-the-art, challenges, and opportunities. Journal of Bridge Engineering, 25(10), 04020076. doi: 10.1061/(ASCE)BE.1943-5592.0001553.
[5] Kim, J. T., Lim, I. K., & Cho, Y. (2019). Emerging technologies for bridge health monitoring: From wireless sensor networks to unmanned aerial vehicles. Advances in Civil Engineering, 2019, 6179354. doi: 10.1155/2019/6179354.
[6] Wang, K., Xu, J., & Zou, J. (2019). Long-term performance monitoring of bridges: Review and future directions. Structural Health Monitoring, 18(5-6), 1355-1383. doi: 10.1177/1475921718818318.
[7] Ghorbanpoor, A., & Fahnestock, L. (2017). Bridge health monitoring: History, challenges, and opportunities. Journal of Civil Structural Health Monitoring, 7(4), 441-455. doi: 10.1007/s13349-017-0221-7.
[8] Yen, T., & Pham, M. (2016). Bridge health monitoring using wireless sensor networks: A review. Journal of Civil Structural Health Monitoring, 6(2), 177-202. doi: 10.1007/s13349-016-0156-9.
[9] Liu, Y., Li, Z., & Hao, H. (2016). Bridge health monitoring using vision-based techniques: A review. Journal of Civil Structural Health Monitoring, 6(2), 203-227. doi: 10.1007/s13349-016-0158-7.
[10] Liu, J., Han, B., & Zhang, X. (2017). Non-destructive testing methods for bridge health monitoring: A review. Journal of Civil Structural Health Monitoring, 7(1), 1-23. doi: 10.1007/s13349-016-0186-3.
[11] Omenzetter, P., Brownjohn, J. M., & Carden, E. P. (2004). A review of techniques for the operational modal analysis of bridges. Journal of Sound and Vibration, 277(1-2), 1-21. doi: 10.1016/j.jsv.2003.07.018.
[12] Kim, S., Lee, S., Lee, S., & Yun, C. B. (2017). Bridge health monitoring: traditional methods and modern techniques. Journal of Sensors, 2017, 1-18. doi: 10.1155/2017/6193502
[13] Federal Highway Administration, "Visual Inspection Techniques for Bridge Elements," FHWA-HRT-06-109, Washington, DC, 2006.
[14] Li, H., Li, L., & Chen, Z. (2016). Health monitoring technologies and systems for large-scale bridge structures: a review. Structural Health Monitoring, 15(3), 257-277. doi: 10.1177/1475921715626963
[15] Yan, Y., & Frangopol, D. M. (2013). Structural health monitoring and management for civil infrastructures: recent advances and applications. Structural Control and Health Monitoring, 20(6), 833-857. https://doi.org/10.1002/stc.1527
[16] Ni, Y. Q., & Feng, D. (2016). Bridge health monitoring using wireless sensor networks and computational intelligence: a review. Sensors, 16(9), 1466.
[17] Ahn, J., et al. (2011). "Non-destructive evaluation of the condition of prestressing strands in concrete bridge decks using ultrasonic testing." NDT & E International, 44(3), 246-255.
[18] K. Nishida, M. Kubo, T. Miyashita, T. Kitada, and Y. Nihei, “Ultrasonic inspection of a steel box girder bridge,” Proc. Jpn. Soc. Civ. Eng., vol. 63, no. 2, pp. 41–50, 2007.
[19] M. F. Ahmed, M. A. Mannan, M. A. Basunia, and M. A. Rahman, “Ground penetrating radar for bridge deck condition assessment: a review,” Measurement, vol. 145, pp. 582-597, 2019.
[20] Liu, J., Tang, Y., & Zhang, Y. (2015). Delamination detection of concrete bridge decks using ground penetrating radar. Construction and Building Materials, 93, 263-271.
[21] Roberts, R., & Maser, K. (2017). Ground Penetrating Radar for Bridge Health Monitoring: A Case Study. In 2017 Construction Research Congress (CRC) (pp. 69-79). IEEE.
[22] Guo, Z., Wang, J., Liu, Y., & Li, Y. (2018). Bridge health monitoring using infrared thermography: a review. Journal of Bridge Engineering, 23(9), 04018063. doi: 10.1061/(ASCE)BE.1943-5592.0001247
[23] Sfarra, S., Ceruti, A., Perilli, S., Paoletti, D., & Ambrosini, D. (2015). Corrosion detection on steel elements of a bridge using active thermography. Measurement, 61, 189-199. doi: 10.1016/j.measurement.2014.10.011
[24] Zhang, Y., & Aktan, A. E. (2006). Quantitative thermal imaging for bridge deck condition assessment. Journal of Infrastructure Systems, 12(1), 25-34. doi: 10.1061/(ASCE)1076-0342(2006)12:1(25)
[25] ASTM International. (2019). Standard practice for magnetic particle examination. ASTM E1444/E1444M-19. doi: 10.1520/E1444_E1444M-19
[26] Ma, Y., Ting, S., & Wang, W. (2013). Magnetic particle inspection for the detection of fatigue cracks in steel bridges. Journal of Bridge Engineering, 18(10), 1099-1109. doi: 10.1061/(ASCE)BE.1943-5592.0000472
[27] Chen, J., Gallegos, G., & Bao, J. (2012). Evaluation of magnetic particle inspection for detection of corrosion in steel bridges. Journal of Bridge Engineering, 17(3), 492-503. doi: 10.1061/(ASCE)BE.1943-5592.0000294
[28] ASTM International. (2016). Standard practice for acoustic emission monitoring of structures during controlled laboratory tests. ASTM E976-16. doi: 10.1520/E0976-16
[29] Ahlborn, T. M., Elliott, S. J., & Gupta, A. (2009). Acoustic emission monitoring of fatigue cracking in a reinforced concrete bridge deck. Journal of Bridge Engineering, 14(3), 180-187. doi: 10.1061/(ASCE)BE.1943-5592.0000017
[30] He, H., Cui, W., & Bakis, C. E. (2014). Acoustic emission based damage detection and characterization for steel bridge components. Structural Control and Health Monitoring, 21(7), 938-951. doi: 10.1002/stc.1715
[31] American Society of Civil Engineers. (2018). Guidelines for using LiDAR for infrastructure monitoring and inspection. ASCE/G-I 57-16. doi: 10.1061/9780784481598.001
[32] Dehghan-Niri, E., Bai, Y., & Glaser, S. D. (2015). Detection and monitoring of bridge deformation using terrestrial laser scanning. Journal of Bridge Engineering, 20(6), 04014085. doi: 10.1061/(ASCE)BE.1943-5592.0000666
[33] Guo, Y., Zhang, Y., Song, G., & Chen, S. (2019). Structural vibration monitoring of a concrete bridge deck using terrestrial LiDAR. Journal of Bridge Engineering, 24(8), 04019070. doi: 10.1061/(ASCE)BE.1943-5592.0001437
[34] Schenk, A., Schindler, C., & Sturzenegger, M. (2017). Image-based 3D modeling for the monitoring of structural deformations. Journal of Structural Engineering, 143(11), 04017157. doi: 10.1061/(ASCE)ST.1943-541X.0001892
[35] H. Zhang, X. Chen, Y. Du, and J. Chen, "Photogrammetry-Based Non-Destructive Testing Techniques for Steel Bridges: A Review," Sensors, vol. 18, no. 4, p. 1184, 2018.
[36] Wang, J., et al. "Bridge deformation monitoring using digital photogrammetry." Journal of Bridge Engineering 19.6 (2014): 04014009.
[37] Nikitas, N., Ettouney, M., & Gergely, P. (2017). Damage Detection of a Full-Scale Bridge Deck Using Non-Destructive Testing. Journal of Non-destructive Evaluation, 36(2), 21. https://doi.org/10.1007/s10921-017-0396-1
[38] De Domenico, D., et al. "A review of vibration-based methods for damage detection in structures." Journal of Structural Health Monitoring 17.1 (2018): 3-35.
[39] T. Gao, X. Wang, and Z. Li, "Sensor-based methods for structural health monitoring of bridges: a review," Measurement, vol. 115, pp. 243-261, 2018.
[40] Brincker, R., Zhang, L., & Andersen, P. (2015). Modal identification: a review and comparison of different methods. Journal of seismic engineering, 19(1), 219-241.
[41] Zou, G., Liu, Y., & Hao, H. (2016). Vibration-based fatigue crack detection and monitoring of a steel truss bridge. Engineering Structures, 125, 111-122.
[42] Koo, K., Vandenbossche, J. M., Agarwal, A., & El-Gohary, N. (2014). Field implementation and testing of vibration-based monitoring for bridge health monitoring. Journal of Structural Engineering, 140(1), 04013011.
[43] Casas, J. R., & Rodellar, J. (2010). Non-destructive monitoring of a cable-stayed bridge using strain measurements. Measurement, 43(10), 1391-1399.
[44] Yan, C., Bai, Y., & Wei, Z. (2018). Structural health monitoring of prestressed concrete bridges using strain-based methods. Journal of Bridge Engineering, 23(1), 04017069. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001119
[45] Kawashima, K., Fujino, Y., Kanda, Y., & Kimura, H. (2001). Long-term monitoring of a cable-stayed bridge subjected to strong earthquakes. Engineering structures, 23(5), 503-514.
[46] Zhang, Y., Li, X., Li, W., Wang, W., & Li, H. (2019). Performance analysis of different displacement measurement systems for bridge health monitoring. Journal of Bridge Engineering, 24(7), 04019047.
[47] Worden, K., & Manson, G. (2007). The role of monitoring and testing in assessing the structural integrity of bridges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 371-393.
[48] D. D. Y. Yun, "Model-Based Structural Health Monitoring for Bridges," in Structural Health Monitoring of Civil Infrastructure Systems, J. Ou and H. Li, Eds. Woodhead Publishing, 2019, pp. 389-427.
[49] Finite element method (FEM), which involves creating a finite element model of the bridge using a series of interconnected elements
[49].
[50] Chakraborty, S., & Das, S. (2017). Bridge health monitoring: a review of recent trends and technologies. Structural Monitoring and Maintenance, 4(4), 289-317. https://doi.org/10.12989/smm.2017.4.4.289
[51] E. Aktan, V. Kiremidjian, L. Farrar, and S. Park, “Civil infrastructure systems: Emerging challenges and opportunities,” Journal of Structural Engineering, vol. 129, no. 6, pp. 775–784, 2003.
[52] Cho, S., & Choi, S. (2015). Application of Finite Element Analysis for Structural Health Monitoring of Bridges. KSCE Journal of Civil Engineering, 19(3), 540-547. doi: 10.1007/s12205-014-0476-9
[53] M. Li, H. Li, J. Li, H. Li, and Y. Li, "A Kalman Filter-Based Structural Damage Identification Method Using Displacement Data," Shock and Vibration, vol. 2018, Article ID 6207089, 14 pages, 2018. https://doi.org/10.1155/2018/6207089.
[54] C. M. Sevcik and B. F. Spencer, "Structural Health Monitoring Using a Wireless Network of Smart Sensors," in Proceedings of the SPIE Conference on Smart Structures and Materials, San Diego, CA, USA, March 2003, vol. 5057, pp. 406-417.
[55] Wu, Z., Liu, M., Zhang, X., & Chen, Z. (2020). Virtual reality based bridge health monitoring: a review. Virtual Reality, 24(3), 447-464.
[56] Wang, Z., Li, N., & Li, J. (2019). A review of virtual reality applications for bridge health monitoring. Advances in Civil Engineering, 2019. https://doi.org/10.1155/2019/8276810
[57] Fakharifar, M., Shahria Alam, M., & Ataei, M. (2020). Virtual Reality and Augmented Reality Applications in Bridge Health Monitoring: A Comprehensive Review. Sensors, 20(12), 3324. https://doi.org/10.3390/s20123324
[58] Zhang, J., Wang, X., Zhao, Y., & Spencer Jr, B. F. (2018). Real-time bridge monitoring using virtual reality and machine learning techniques. Computer-Aided Civil and Infrastructure Engineering, 33(10), 831-847.
[59] Kim, Y., Glaser, S.D., Liu, Z. and Lynch, J.P., 2015. Automated visual monitoring system for bridge health assessment. Journal of Computing in Civil Engineering, 29(2), p.04014068.
[60] Li, H., Huang, H., Zhang, Y., & Chen, S. (2019). Bridge Crack Detection Using Deep Learning: A Comparison of Systems Based on Convolutional Neural Networks and Deep Belief Networks. Applied Sciences, 9(12), 2566.
[61] Liu, Y., Li, J., He, Y., Li, H., & Li, A. (2021). A Deep Learning Framework for Structural Health Monitoring: From Sensor Data to Health Diagnosis. Applied Sciences, 11(6), 2756.
[62] S. AghaKouchakzadeh, S. A. Hosseini, and M. N. Noori, "Bridge health monitoring using deep learning-based algorithms: A review," Journal of Intelligent Material Systems and Structures, vol. 31, no. 4, pp. 451-479, Feb. 2020.
[63] D. Ren et al., "Crack Detection in Concrete Bridges Using UAV-Based Images and Deep Learning," Remote Sensing, vol. 11, no. 10, p. 1156, 2019.
[64] Li, B., & Ou, J. (2017). Bridge health monitoring by a fuzzy logic-based approach using acceleration data. Smart Structures and Systems, 20(4), 439-449.
[65] Hussain, M. S., Jafari, M. A., & Pozzi, M. (2016). Fuzzy logic system for bridge health monitoring using vibration data. Journal of Bridge Engineering, 21(1), 04015028. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000782
[66] Mendes, R., Gomes, L., & Cardoso, A. (2018). Bridge condition assessment based on fuzzy logic. Engineering Structures, 164, 238-247. https://doi.org/10.1016/j.engstruct.2018.02.043
[67] Wang, K., Zhang, Y., & Su, Y. (2019). An artificial neural network-based approach for identifying the location and severity of bridge damage using vibration data. Structural Health Monitoring, 18(1), 174-187. 67
[68] Wang, H., Wu, W., Zhang, Q., & Liu, H. (2019). An intelligent bridge health monitoring system based on artificial neural network and multiple sensors. IEEE Access, 7, 166781-166793.
[69] Shahsavari, S., LaFave, J.M., D'Ambrosia, M.D. et al. "Load rating of reinforced concrete bridge decks using non-destructive testing and artificial neural networks." Journal of Civil Structural Health Monitoring, vol. 9, no. 5, pp. 587-602, 2019.
[70] Chen, Z., Li, Y., & Liu, Z. (2016). A bridge crack detection method based on acoustic emission signal and artificial neural network. Sensors, 16(2), 233.
[71] Chen, X., Han, S., Liu, S., & Zhou, W. (2019). A review of artificial intelligence applications in bridge engineering. Journal of Computing in Civil Engineering, 33(1), 04018045. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000817
[72] A. Dhonde and D. D. Ganorkar, "Artificial neural network based bridge health monitoring," International Journal of Engineering Research & Technology, vol. 4, no. 4, pp. 46-49, 2015.
[73] Koo, K. C., & Park, H. J. (2019). Bridge health monitoring using deep learning: A review. Engineering Structures, 186, 684-696. https://doi.org/10.1016/j.engstruct.2019.01.038
[74] S. M. Islam, S. A. Billah, M. A. Hossain, and M. H. Rashid, “A survey on bridge health monitoring: challenges and opportunities,” SN Appl. Sci., vol. 1, no. 9, pp. 1–26, 2019.
[75] Yousefi-Khoshbakht, M., Yavari, A., & Khanzadi, M. (2020). A hybrid genetic algorithm and wavelet transform approach for bridge damage detection using acceleration data. Journal of Bridge Engineering, 25(10), 04020127.
[76] Liu, Y., Liu, J., Sun, Y., & Gao, J. (2016). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 3(5), 1125-1142.
[77] Hwang, Y., & Sohn, H. (2009). Genetic algorithm-based optimal sensor placement for bridge damage detection. Journal of Sound and Vibration, 327(3-5), 524-535.
[78] H. Zhang, Y. Liu, and J. Li, "Optimization of Inspection and Maintenance Schedules for Bridge Components Using Genetic Algorithm," Mathematical Problems in Engineering, vol. 2016, Article ID 9802826, 2016. https://doi.org/10.1155/2016/9802826.
[79] Debnath, K., Padgett, J. E., & Kim, J. T. (2016). Optimal sensor placement using genetic algorithms for structural health monitoring of bridges. Structural Control and Health Monitoring, 23(2), 329-344.
[80] K. Gopalakrishnan, "Structural health monitoring of civil infrastructure," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, no. 1851, pp. 589-622, 2007.
[81] Li, J., Li, Z., & Liu, Y. (2019). An efficient genetic algorithm for optimal sensor placement in bridge health monitoring. Advances in Civil Engineering, 2019, 1-14. doi:10.1155/2019/7935851
[82] Kaya, T., & Ulker-Kaya, E. (2012). Genetic algorithm based optimization for water distribution network design. Water resources management, 26(6), 1499-1513.
[83] Nguyen, H. T., Nguyen, T. H., Nguyen, V. T., Nguyen, H. H., & Vo, D. H. (2021). A novel hybrid algorithm for optimizing water distribution network design. Journal of Cleaner Production, 284, 125073.
[84] Jain, A., Kumar, A., & Kumar, S. (2019). Optimization techniques and their applications in civil engineering: A review. Journal of Cleaner Production, 210, 1461-1484. https://doi.org/10.1016/j.jclepro.2018.11.111
[85] Yang, Z., & He, X. (2014). Genetic algorithm and its applications in engineering optimization. Advances in Mechanical Engineering, 6, 679105. https://doi.org/10.1155/2014/679105
[86] K. Mechitoua, R. Chikhi, A. Tounsi, and M. Mechitoua, "Review of bridge health monitoring systems," Journal of Civil Structural Health Monitoring, vol. 9, no. 3, pp. 283-313, 2019.
[87] Farrar, C.R., Jaishi, B., Park, G., and Todd, M.D. (2007). "Structural health monitoring: from sensing to diagnosis." Proceedings of the IEEE, Vol. 95, No. 3, pp. 632-652.
[88] Farrar, C.R., Doebling, S.W., Nix, D.A. and Park, G., 2007. Structural health monitoring for aerospace systems. Progress in Aerospace Sciences, 43(7-8), pp.203-247.
[89] Doebling, S. W., Farrar, C. R., Prime, M. B., & Shevitz, D. W. (1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review. Los Alamos National Laboratory report, LA-13070-MS.
[90] Worden, K., & Manson, G. (2007). The role of signal processing in structural health monitoring. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463(2082), 2929-2950.
[91] Liu, J., Liu, Y., & Zhang, Y. (2020). Structural Health Monitoring Based on Data Fusion of Multiple Sensing Modalities. Sensors, 20(4), 1091. https://doi.org/10.3390/s20041091
[92] Kopsaftopoulos, F., Chatzi, E., & Smyth, A. W. (2019). Damage Detection and Localization via Machine Learning: A Comprehensive Review. Journal of Non-destructive Evaluation, 38(3), 67. https://doi.org/10.1007/s10921-019-0583-6
[93] Ngamkhanong, C., & Au, F. T. K. (2019). Pattern Recognition Approaches in Bridge Health Monitoring: A Review. Applied Sciences, 9(8), 1664. https://doi.org/10.3390/app9081664
[94] Conte, J. P., & Brownjohn, J. M. W. (2005). Model-Based Damage Identification Methods: Part I - Theory, Implementation and Localization for Beams. Journal of Sound and Vibration, 284(3-5), 579-598. https://doi.org/10.1016/j.jsv.2004.06.052
[95] Farrar, C. R., & Worden, K. (2007). An Introduction to Structural Health Monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 303-315. https://doi.org/10.1098/rsta.2006.1912
[96] Li, H., Li, J., Li, H., Li, B., & Li, H. (2021). A Review of Data-Driven Approaches for Structural Damage Detection. Sensors, 21(3), 1068. https://doi.org/10.3390/s21031068
[97] Gao, W., Hu, J., & Wang, Y. (2021). A Review of Data-Driven Approaches for Structural Health Monitoring. Applied Sciences, 11(1), 343. https://doi.org/10.3390/app11010343
[98] Shao, S., & Chan, T. H. T. (2019). Review of Artificial Intelligence and Data-Driven Techniques for Structural Health Monitoring. Structural Health Monitoring, 18(4), 1124-1144. https://doi.org/10.1177/1475921718810514
[99] Kundu, T., & Banerjee, S. (2015). A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Structures. Journal of Vibration Engineering & Technologies, 3(3), 197-216. https://doi.org/10.1007/s42417-015-0019-9
[100] Wang, S., & Gao, H. (2018). Structural Health Monitoring Technologies and Next-Generation Smart Composite Structures. Springer. https://doi.org/10.1007/978-981-10-5945-0
[101] Kundu, T., & Banerjee, S. (2015). A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Structures. Journal of Vibration Engineering & Technologies, 3(3), 197-216. https://doi.org/10.1007/s42417-015-0019-9
[102] Zhao, X., & Shrive, N. (2009). A hybrid ultrasonic testing and finite element modeling approach for damage assessment in concrete bridge decks. NDT & E International, 42(3), 241-248. https://doi.org/10.1016/j.ndteint.2008.10.002
[103] He, Y., Kong, Q., & Chen, G. (2018). Sensor fusion approach for fatigue crack detection and monitoring in steel bridges using acoustic emission and infrared thermography. Engineering Structures, 161, 172-182. https://doi.org/10.1016/j.engstruct.2018.01.0
[104] Ehsani, M. R., Zhang, Y., Ghasemi, H., Rajabifard, A., & Goulding, J. (2020). A multi-scale approach for bridge health monitoring using photogrammetry and LiDAR. Automation in Construction, 116, 103207.
[105] Ramezanianpour, A.A., Ahmadi, H., & Rofooei, F.R. (2017). Bridge health monitoring using acoustic emission technique and artificial neural network. Measurement, 110, 189-198. doi: 10.1016/j.measurement.2017.06.011
[106] Liu, G., Zhang, H., & Chen, G. (2020). A review of recent advances in bridge health monitoring using big data. IEEE Access, 8, 111554-111567. https://doi.org/10.1109/ACCESS.2020.3002444
[107] Gao, Z., Deng, Y., & Liu, J. (2017). Bayesian network-based bridge health monitoring. Journal of Bridge Engineering, 22(4), 04016089.
[108] N. H. Ma and J. Li, "Structural health monitoring of bridges using Bayesian networks: a review," Journal of Civil Structural Health Monitoring, vol. 7, no. 2, pp. 223-244, 2017.
[109] Luo, Y., Wang, J., & Chen, G. (2019). Risk assessment of bridges using Bayesian networks: A review. Structure and Infrastructure Engineering, 15(9), 1228-1242.
[110] Zhu, J., & Liu, B. (2019). A Bayesian network approach for bridge maintenance decision-making under uncertainty. Journal of Bridge Engineering, 24(10), 04019076. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001435
[111] Ghasemi, H., & Zareei, A. (2021). A review of artificial intelligence applications in bridge engineering. Journal of Bridge Engineering, 26(4), 04021010. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001723
[112] Hua, X., Huang, H., & Zhang, H. (2020). Bridge defect detection using deep learning: A review. Journal of Computing in Civil Engineering, 34(6), 04020035. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000922
[113] S. Bandyopadhyay, S. Sengupta, and S. Maiti, “Artificial Intelligence and machine learning techniques in structural health monitoring: A comprehensive review,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 1187–1221, Nov. 2021. Online. Available: https://doi.org/10.1007/s11831-021-09654-2
[114] Siu, T., Wu, Z., & Hao, H. (2020). Artificial intelligence and machine learning for bridge health monitoring and risk assessment: A review. Engineering Structures, 214, 110671. doi: 10.1016/j.engstruct.2020.110671
[115] Au, F.T.K., & Beck, S.B.M. (2013). Applications of Markov decision processes in bridge management. Structure and Infrastructure Engineering, 9(2), 164-177. doi: 10.1080/15732479.2011.633632
[116] A. Mitropoulos, D. Stratopoulos, and N. D. Lagaros, "A Markov decision process-based framework for maintenance planning of aging bridges," Structure and Infrastructure Engineering, vol. 12, no. 8, pp. 973-985, Aug. 2016.
[117] Choi, S., Han, S., & Park, J. (2019). Inspection scheduling optimization of deteriorating bridge structures using Markov decision processes. Journal of Computing in Civil Engineering, 33(6), 04019023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000869
[118] Zhang, Y., Li, Z., Li, H., & Li, B. (2019). Bridge health monitoring and maintenance decision-making under uncertainty: A review. Structural Health Monitoring, 18(1), 3-23. doi: 10.1177/1475921718806326
[119] S. Y. Liu, S. H. Ding, Z. Q. Lu, and X. B. Zhang, "Bridge health monitoring: review and future directions," Journal of Bridge Engineering, vol. 19, no. 6, pp. 05014001-1-05014001-16, 2014.
[120] C. Li and H. Hao, "A review of data-driven approaches for bridge health monitoring," Sensors, vol. 18, no. 8, p. 2633, 2018.
[121] Kaloop, M. R., Mahmoud, M. A., & Aziz, M. A. (2020). Bridge health monitoring systems: A review. Structure and Infrastructure Engineering, 16(6), 731-760.
[122] Li, Z., Li, C. Q., Li, Y. X., Wang, J. J., & Zhang, Y. (2018). Bridge health monitoring based on data mining: state of the art and future challenges. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2598-2616.
[123] Goyal, S., & Thakur, J. S. (2019). A review on machine learning approaches for bridge health monitoring. Journal of Civil Structural Health Monitoring, 9(6), 667-683.
[124] M. Shahverdi, S. Nadimi, and M. Moslemi, "A review of structural health monitoring systems for bridges: A case study approach," Journal of Performance of Constructed Facilities, vol. 34, no. 2, pp. 04019064, 2020.
[125] H. Li, B. Li, and Y. Li, "Challenges and opportunities of bridge health monitoring and assessment: a review," Journal of Civil Structural Health Monitoring, vol. 7, no. 1, pp. 69-83, 2017.
[126] A. Bhattacharya and S. Mukherjee, “Structural health monitoring of bridges: A review of recent trends and developments,” Structural Health Monitoring, vol. 18, no. 3, pp. 743-768, May 2019.
[127] K. Menon and P. Gardoni, "Long-term monitoring of bridges: a review of recent advances and future directions," Structural Health Monitoring, vol. 17, no. 3, pp. 577-596, 2018.
[128] F. F. Chen, J. M. Ko, and J. Y. Tu, "A review of monitoring and interpretation of data in bridge health monitoring," Smart Materials and Structures, vol. 17, no. 2, p. 023001, 2008.
[129] Zhang, Y., & Frangopol, D. M. (2018). Data-driven life-cycle management of civil infrastructure systems: Review and future directions. Journal of Civil Structural Health Monitoring, 8(3), 299-321.
[130] Nazari, A., & Yildirim, T. (2021). Artificial intelligence and machine learning in bridge health monitoring: A comprehensive review. Automation in Construction, 122, 103471.
[131] Wang, Y., Huang, H., He, X., & Zhou, J. (2021). A review of bridge health monitoring systems based on multiple data sources. Journal of Bridge Engineering, 26(7), 04021030.
[132] T. Chen, J. J. Lee, and K. Li, "Wireless Sensor Networks for Structural Health Monitoring: A Review," Journal of Sensors, vol. 2017, Article ID 6160304, 18 pages, 2017. https://doi.org/10.1155/2017/6160304.
[133] Chen, G., Zou, G., & Ou, J. (2020). Recent development of non-destructive testing techniques for structural health monitoring of bridges. Advances in Civil Engineering, 2020. https://doi.org/10.1155/2020/8916127
[134] Wang, H., & Wang, Z. (2018). A Review on Multi-Scale Modeling of Bridges. Applied Sciences, 8(8), 1387. https://doi.org/10.3390/app8081387
[135] Liu, M., Chen, Z., & Yao, J. (2021). Multi-Scale Modelling of Concrete Bridges for Durability Analysis: A Review. Materials, 14(5), 1218. https://doi.org/10.3390/ma14051218
[136] O’Connor, A., A. Al-Jumaili, A. Alanazi, and B. Pakrashi. “Challenges of Implementing a Cyber-Security Strategy for Bridge Infrastructure: A Systematic Review.” Sustainability 11, no. 22 (2019): 6336. https://doi.org/10.3390/su11226336.
[137] Li, X., Li, J., & Cheng, X. (2018). Cybersecurity in bridge infrastructure: Threats and countermeasures. Structural Control and Health Monitoring, 25(10), e2216.
[138] Islam, M. R., Islam, M. M., & Arif, M. T. (2021). A Comprehensive Review on Bridge Health Monitoring Systems: Recent Advances, Challenges, and Opportunities. Journal of Infrastructure Systems, 27(4), 04021011
[139] J. Song, Y. Bai, and Y. Lu, "Security of Bridge Health Monitoring Systems: A Review," Journal of Computing in Civil Engineering, vol. 32, no. 2, 2018.
[140] National Institute of Standards and Technology (NIST). (2018). Cybersecurity Framework Version 1.1. Retrieved from https://www.nist.gov/cyberframework