PSI - Issue 64

Tahreer M. Fayyad et al. / Procedia Structural Integrity 64 (2024) 708–715 Tahreer M. Fayyad / Structural Integrity Procedia 00 (2019) 000–000

715

8

rotation stage that precedes failure. This finding underscores the effectiveness of integrating vision-based and vibration-based methods, where each technique compensates for the limitations of the other, enabling a more comprehensive capture of structural damage. The success of the DIC technique, when used in with frequency measurement, not only facilitates an in-depth monitoring of flexural crack development but also paves the way for new insights into damage mechanisms. Integration of both approaches in practical SHM will provide a promising direction for future developments in SHM. Acknowledgements The authors would like to thank Daphne Jackson Trust, the Royal Society and the Engineering and Physical Sciences Research Council (EPSRC) for their generous support of this project. References Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z., & Li, H. (2019). The State of the Art of Data Science and Engineering in Structural Health Monitoring. Engineering , 5 (2), 234–242. https://doi.org/10.1016/j.eng.2018.11.027 Calvi, G. M., Moratti, M., O’Reilly, G. J., Scattarreggia, N., Monteiro, R., Malomo, D., Calvi, P. M., & Pinho, R. (2019). Once upon a Time in Italy: The Tale of the Morandi Bridge. Structural Engineering International , 29 (2), 198–217. https://doi.org/10.1080/10168664.2018.1558033 Dennison, P. E., Brewer, S. C., Arnold, J. D., & Moritz, M. A. (2014). Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters , 41 (8), 2928–2933. https://doi.org/https://doi.org/10.1002/2014GL059576 Fayyad, T. M., & Lees, J. M. (2014). Application of digital image correlation to reinforced concrete fracture. Procedia Materials Science , 3 , 1585–1590. https://doi.org/10.1016/j.mspro.2014.06.256 Fayyad, T. M., & Lees, J. M. (2015). Integrated fracture-based model for the analysis of cracked reinforced concrete beams. Concrete 2015- 27th Biennial National Conference of the Concrete Institute of Australia, Melbourne . Fayyad, T. M., & Lees, J. M. (2017). Experimental investigation of crack propagation and crack branching in lightly reinforced concrete beams using Digital Image Correlation. Engineering Fracture Mechanics , 182 , 487–505. Feng, K., Casero, M., & González, A. (2019). The use of accelerometers in UAVs for bridge health monitoring. Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP 2019) . Hossain, T., Segura, S., & Okeil, A. M. (2020). Structural effects of temperature gradient on a continuous prestressed concrete girder bridge: analysis and field measurements. Structure and Infrastructure Engineering , 16 (11), 1539–1550. https://doi.org/10.1080/15732479.2020.1713167 Li, H., Ou, J., Zhao, X., Zhou, W., Li, H., Zhou, Z., & Yang, Y. (2006). Structural Health Monitoring System for the Shandong Binzhou Yellow River Highway Bridge. Computer-Aided Civil and Infrastructure Engineering , 21 (4), 306–317. https://doi.org/10.1111/j.1467 8667.2006.00437.x Lydon, D., Kromanis, R., Lydon, M., Early, J., & Taylor, S. (2022). Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges. Journal of Civil Structural Health Monitoring , 12 (6), 1299–1316. https://doi.org/10.1007/s13349-022-00617-w Lydon, D., Lydon, M., Taylor, S., Del Rincon, J. M., Hester, D., & Brownjohn, J. (2019). Development and field testing of a vision-based displacement system using a low cost wireless action camera. Mechanical Systems and Signal Processing , 121 , 343–358. https://doi.org/10.1016/j.ymssp.2018.11.015 Lydon, D., Taylor, S. E., Lydon, M., del Rincon, J. M., & Hester, D. (2019). Development and testing of a composite system for bridge health monitoring utilising computer vision and deep learning. Smart Structures and Systems , 24 (6), 723–732. https://doi.org/10.12989/sss.2019.24.6.723 Poorghasem, S., & Bao, Y. (2022). Review of robot-based automated measurement of vibration for civil engineering structures. Measurement , 112382. Sarmadi, H., Entezami, A., Yuen, K.-V., & Behkamal, B. (2023). Review on smartphone sensing technology for structural health monitoring. Measurement , 223 , 113716. https://doi.org/https://doi.org/10.1016/j.measurement.2023.113716 Sony, S., Laventure, S., & Sadhu, A. (2019). A literature review of next‐generation smart sensing technology in structural health monitoring. Structural Control and Health Monitoring , 26 (3), e2321. Ye, X. W., Dong, C. Z., & Liu, T. (2016). A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications. Journal of Sensors , 2016 , 7103039. https://doi.org/10.1155/2016/7103039 Zhang, G., Liu, Y., Liu, J., Lan, S., & Yang, J. (2022). Causes and statistical characteristics of bridge failures: A review. Journal of Traffic and Transportation Engineering (English Edition) , 9 (3), 388–406. https://doi.org/https://doi.org/10.1016/j.jtte.2021.12.003 Zhou, G.-D., & Yi, T.-H. (2013). Thermal Load in Large-Scale Bridges: A State-of-the-Art Review. International Journal of Distributed Sensor Networks , 9 (12), 217983. https://doi.org/10.1155/2013/217983 Zonta, D., Glisic, B., & Adriaenssens, S. (2014). Value of information: impact of monitoring on decision-making. Structural Control and Health Monitoring , 21 (7), 1043–1056. https://doi.org/10.1002/stc.1631

Made with FlippingBook Digital Proposal Maker