PSI - Issue 64

Mariniello Giulio et al. / Procedia Structural Integrity 64 (2024) 2101–2108 G. Mariniello, D. Coluccino, A. Bilotta, D. Asprone / Structural Integrity Procedia 00 (2019) 000 – 000

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5. Conclusions This paper explores the detection of deflection in a bridge using time-of-flight laser sensors. The study introduces these sensors, typically used in industrial engineering, within the context of structural health monitoring of a bridge. By installing the sensor vertically at the mid-span of the beam, it can detect the passage of heavy vehicles with high sensitivity, allowing for the estimation of vertical displacement down to the order of 1 mm. Since this is a real-time monitoring system, deflection can be estimated with good precision by subtracting a moving average calculated over a 30-second interval from the reading at each instant, effectively removing slow temperature-related trends. In addition to the vertical sensor, a diagonal laser was fixed from the pier, pointing toward the mid-span of the beam. This setup expands the system's applicability to scenarios where a road or watercourse beneath the monitored bridge precludes the use of a vertical laser. Despite the complexity of supporting diagonal sensors, which require manual alignment between the sensor and target, the results show a correlation between the readings of the reference sensor and the diagonal sensor, despite the amplification of ambient noise in the diagonal direction. Regarding deflection, it was found that increasing the threshold value for peak selection leads to more precise results. Lower thresholds are susceptible to noise interference. In the future, an experimental campaign is planned wherein heavy vehicles will be driven at predetermined mass, speed, and lane to calibrate the system. This calibration will enable the development of a low-cost truck weight in motion system. Acknowledgements The presented work has been partially supported by the project “DS2: Digital Smart Structures”, funded by INAIL (National Institute for Insurance against Accidents at Work), BRIC 2021. References He, Z., Li, W., Salehi, H., Zhang, H., Zhou, H., & Jiao, P. (2022). Integrated structural health monitoring in bridge engineering. Automation in construction , 136 , 104168. Huang, Y., Feng, R., Zhong, C., Tong, X., Shao, X., Gu, L., & Hui, Z. (2024). Computer vision-based real-time deflection monitoring of complex and sizeable steel structures. Engineering Structures , 305 , 117752. Mishra, M., Lourenço, P. B., & Ramana, G. V. (2022). Structural health monitoring of civil engineering structures by using the internet of things: A review. Journal of Building Engineering , 48 , 103954. Romanelloa, R., Miragliaa, E., Micelia, G., Gazzob, S., Contrafattob, L., & Cuomob, M. (2024). Structural monitoring of a historic masonry bridge. Brownjohn, J. M. (2007). Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 365 (1851), 589-622. Scuro, C., Sciammarella, P. F., Lamonaca, F., Olivito, R. S., & Carni, D. L. (2018). IoT for structural health monitoring. IEEE Instrumentation & Measurement Magazine , 21 (6), 4-14. Al-Ali, A. R., Beheiry, S., Alnabulsi, A., Obaid, S., Mansoor, N., Odeh, N., & Mostafa, A. (2024). An IoT-based road bridge health monitoring and warning system. Sensors , 24 (2), 469. Sun, L., Shang, Z., Xia, Y., Bhowmick, S., & Nagarajaiah, S. (2020). Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection. Journal of Structural Engineering , 146 (5), 04020073. Helmi, K., Taylor, T., Zarafshan, A., & Ansari, F. (2015). Reference free method for real time monitoring of bridge deflections. Engineering Structures , 103 , 116-124. Zhang, C., Ge, Y., Hu, Z., Zhou, K., Ren, G., & Wang, X. (2019). Research on deflection monitoring for long span cantilever bridge based on optical fiber sensing. Optical Fiber Technology , 53 , 102035. Tang, Y., Cang, J., Zheng, B., & Tang, W. (2023). Deflection Monitoring Method for Simply Supported Girder Bridges Using Strain Response under Traffic Loads. Buildings , 14 (1), 70. Lee, Z. K., Bonopera, M., Hsu, C. C., Lee, B. H., & Yeh, F. Y. (2022, October). Long-term deflection monitoring of a box girder bridge with an optical-fiber, liquid-level system. In Structures (Vol. 44, pp. 904-919). Elsevier. You, R. Z., Yi, T. H., Ren, L., & Li, H. N. (2023). Equivalent estimation method (EEM) for quasi-distributed bridge-deflection measurement using only strain data. Measurement , 221 , 113492. Deng, Y., Ju, H., Zhai, W., Li, A., & Ding, Y. (2022). Correlation model of deflection, vehicle load, and temperature for in‐ service bridge using deep learning and structural health monitoring. Structural Control and Health Monitoring , 29 (12), e3113. Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal. 15 (2022), 531–538. https://doi.org/10.1002/sam.11583