Oral Presentation Ninth International Symposium on Life-Cycle Civil Engineering 2025

Deformation Monitoring of Bridges using Point Clouds (107775)

Felipe Brandao 1 , Túlio Prof. Dr. Nogueira Bittencourt 1
  1. University of Sao Paulo, USP, São Paulo, BR, Brazil

Deformation monitoring is a critical aspect of structural health monitoring (SHM), ensuring the safety and longevity of infrastructures such as bridges, buildings, and dams. The advent of point cloud technology has revolutionized this field by enabling the creation of highly accurate and detailed three-dimensional data of structures. This article explores the methodologies and applications of point clouds in capturing and analyzing structural deformations, discussing the processes of acquiring point cloud data through laser scanning techniques, registering and aligning these datasets, and employing techniques for detecting and quantifying deformations. Additionally, besides the use of a TLS, this article also examines the utilization of publicly available point cloud data from government sources for assessing the condition of a bridge. By leveraging point clouds, engineers and researchers can achieve a precise and comprehensive understanding of structural behavior over time, enabling timely interventions and maintenance. A case study of an iconic cable-stayed bridge featuring a distinctive "X"-shaped tower, 138 meters tall, with two cable-stayed curved roadways of approximately 580 meters and 144 stays, illustrates the successful implementation of point cloud technology over different periods, demonstrating its effectiveness in enhancing the reliability and accuracy of deformation monitoring practices. To compare the deformation data obtained from point clouds, a 3D model and a parametric FEM model were created using the point cloud data. The results predict the bridge's behavior with both the prestressed cable forces from the original project and the forces measured in the field. Force identification of bridge cables is important to the performance evaluation of cable-supported bridges. Furthermore, to efficiently store and share the generated data with potential users, a web platform was implemented using IFC models. This comprehensive approach underscores the transformative potential of point cloud technology in the field of deformation monitoring, offering significant advancements in structural health assessment and management.

  1. Alani, A. M., Tosti, F., Ciampoli, L. B., Gagliardi, V., & Benedetto, A. (2020). An integrated investigative approach in health monitoring of masonry arch bridges using GPR and InSAR technologies. NDT and E International, 115. https://doi.org/10.1016/j.ndteint.2020.102288.
  2. Armesto, J., Roca-Pardiñas, J., Lorenzo, H., & Arias, P. (2010). Modelling masonry arches shape using terrestrial laser scanning data and nonparametric methods. Engineering Structures, 32(2), 607–615. https://doi.org/10.1016/j.engstruct.2009.11.007.
  3. Bak, M., & Çeli̇k, R. N. (2023). Web-NDefA: Open-source and web-based online platform for 3-D deformation analysis of geodetic networks. SoftwareX, 24, 101523. https://doi.org/10.1016/j.softx.2023.101523
  4. Guth, P. L., & Geoffroy, T. M. (2021). LiDAR point cloud and ICESat‐2 evaluation of 1 second global digital elevation models: Copernicus wins. Transactions in GIS, 25(5), 2245–2261. https://doi.org/10.1111/tgis.12825
  5. Hu, R., Chen, K., Jiang, W., & Luo, H. (2024). IFC data extension for real-time safety monitoring of automated construction in high-rise building projects. Automation in Construction, 162, 105408. https://doi.org/10.1016/j.autcon.2024.105408
  6. Jacquot, K., & Saleri, R. (2024). Gathering, integration, and interpretation of heterogeneous data for the virtual reconstruction of the Notre Dame de Paris roof structure. Journal of Cultural Heritage, 65, 232–240. https://doi.org/10.1016/j.culher.2023.06.010
  7. Li, S., Moan, T., Fu, S., Zhang, S., & Xu, Y. (2023). Hydroelastic analysis of a floating bridge under spatially inhomogeneous waves, with emphasis on the effect of drift force modeling. Applied Ocean Research, 139, 103666. https://doi.org/10.1016/j.apor.2023.103666
  8. Lu, D., Tang, G., Yan, G., Yu, F., & Lin, X. (2024). Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping. Earth Surface Processes and Landforms, 49(4), 1411–1427. https://doi.org/10.1002/esp.5777
  9. Mazzatura, I., Salvatore, W., Caprili, S., Celati, S., Mori, M., & Gammino, M. (2023). Damage detection, localization, and quantification for steel cables of post-tensioned bridge decks. Structures, 57, 105314. https://doi.org/10.1016/j.istruc.2023.105314
  10. Pauwels, P., van den Bersselaar, E., & Verhelst, L. (2024). Validation of technical requirements for a BIM model using semantic web technologies. Advanced Engineering Informatics, 60, 102426. https://doi.org/10.1016/j.aei.2024.102426
  11. Pregnolato, M., Gunner, S., Voyagaki, E., De Risi, R., Carhart, N., Gavriel, G., Tully, P., Tryfonas, T., Macdonald, J., & Taylor, C. (2022). Towards Civil Engineering 4.0: Concept, workflow and application of Digital Twins for existing infrastructure. Automation in Construction, 141, 104421. https://doi.org/10.1016/j.autcon.2022.104421
  12. Shen, N., Wang, B., Ma, H., Zhao, X., Zhou, Y., Zhang, Z., & Xu, J. (2023). A review of terrestrial laser scanning (TLS)-based technologies for deformation monitoring in engineering. In Measurement: Journal of the International Measurement Confederation (Vol. 223). Elsevier B.V. https://doi.org/10.1016/j.measurement.2023.113684
  13. Su, P., Han, H., Liu, M., Yang, T., & Liu, S. (2024). MOD-YOLO: Rethinking the YOLO architecture at the level of feature information and applying it to crack detection. Expert Systems with Applications, 237, 121346. https://doi.org/10.1016/j.eswa.2023.121346
  14. Vital, W., Silva, R., de Morais, M. V. G., Emidio Sobrinho, B., Pereira, R., & Evangelista, F. (2023). Application of bridge information modelling using laser scanning for static and dynamic analysis with concrete damage plasticity. Alexandria Engineering Journal, 79, 608–628. https://doi.org/10.1016/j.aej.2023.08.023
  15. Xiao, J.-L., Fan, J.-S., Liu, Y.-F., Li, B.-L., & Nie, J.-G. (2024). Region of interest (ROI) extraction and crack detection for UAV-based bridge inspection using point cloud segmentation and 3D-to-2D projection. Automation in Construction, 158, 105226. https://doi.org/10.1016/j.autcon.2023.105226
  16. Zhou, K., Lei, D., Chun, P., She, Z., He, J., Du, W., & Hong, M. (2024). Evaluation of BFRP strengthening and repairing effects on concrete beams using DIC and YOLO-v5 object detection algorithm. Construction and Building Materials, 411, 134594. https://doi.org/10.1016/j.conbuildmat.2023.134594