PSI - Issue 64

Francesco Pentassuglia et al. / Procedia Structural Integrity 64 (2024) 254–261 F. Pentassuglia et al./ Structural Integrity Procedia 00 (2019) 000 – 000

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Kim, I.H., Jeon, H., Baek, S.C., Wong, W.H., Jung, H.J., 2018. Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors18, 1881. Kim, H., Yoon, J., and Sim, S., 2020. Automated bridge component recognition from point clouds using deep learning. Structural Control Health Monitoring 27, e:2591. Lee, J.S., Park, J., R., Y.M., 2021. Semantic segmentation of bridge components based on hierarchical point cloud model. Automation in Construction 130, 103847. Liu, C.H., Chou, J.S., 2023. Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicle. Automation in Construction 146:104666. Lu, N., Beer, M., Noori, M., Liu, Y., 2017. Lifetime Deflections of Long-Span Bridges under Dynamic and Growing Traffic Loads. Journal of Bridge Engineering 22(11). Ma, Z., Zhao, E., Granello, G., Loporcaro, G., 2020. Drone aided machine learning tool for post-earthquake bridge damage reconnaissance,17th world conference on earthquake engineering, Earthquake Engineering Association, Sendai, Japan, pp.1 – 12. Nazri, F.M., 2018. Seismic Fragility Assessment for Buildings due to Earthquake Excitation. SpringerBriefs in Applied Sciences and Technology. Messina, D., Proverbio, E., 2022. Effect of prestressing corrosion on failure in bridges. Structural Concrete Journal of the fib 24(1), 227-238. Podolny, W., 1985. The Cause of Cracking in Post-Tensioned Concrete Box Girder Bridges and Retrofit Procedures. Ono, R., Ha, T.M., Fukada, S., 2019. Analytical study on damage detection method using displacement influence lines of road bridge slab. Journal of Civil Structural Health Monitoring 9:565 – 577. Salehi, H., Burgueño, R.,2018. Emerging artificial intelligence methods in Structural Engineering. Engineering Structures 171, 170-189. Yang, D.S., Wang, C.M., 2022. Bridge damage detection using reconstructed mode shape by improved vehicle scanning method. Structures 263:114373. Zhang, G., Liu, Y., Liu, J., Lan, S., Yiang, J., 2022. Causes and statistical characteristics of bridge failures: A review. Journal of Traffic and Transportation Engineering 9(3), 388-406. Zhang, Y., Yuen, K.V., 2022. Review of artificial intelligence-based bridge damage detection. Advances in Mechanical Engineering 14(9), 1-21. Zhang, Y.C., Yi, T.H., Lin, S., Li, H.N., Lv, S., 2022. Automatic corrosive environment detection of RC bridge decks from ground penetrating radar data based on deep learning. Journal of Performance of Constructed Facilities 36:04022011.

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