BrIM and Digital Twin Integration for Structural Health Monitoring and Analysis of the Villena Rey Bridge via Laser Scanning
Source abstract
Abstract
This research analyzes the implementation of terrestrial laser scanning (TLS) in Building Information Modeling for Bridges (BrIM) for the structural monitoring and analysis of the Villena Rey Bridge, considering that bridges undergo progressive deterioration due to environmental factors and dynamic loads, making advanced monitoring technologies essential. The capture of three-dimensional data through TLS enabled the generation of a point cloud in RCS format, which was processed and optimized in Autodesk ReCap Pro, and subsequently used to create a digital twin in Revit, facilitating simulations and structural analyses. Furthermore, the integration of this information with Power BI resulted in a decision support system (DSS) that enhances data interpretation. The results indicate quantifiable improvements directly related to the application of the proposed BrIM-based methodology. Specifically, the accuracy in detecting structural anomalies increased by 37% compared to traditional visual inspection methods, as detailed. Likewise, the time required for structural evaluation and diagnosis decreased by 42%, allowing faster and more reliable decision-making. Furthermore, the integration of structural data with analytical tools enhanced the accuracy of maintenance planning by 31%, which, in turn, contributed to a 25% reduction in operational costs. These findings, discussed in detail in the Results section, confirm the effectiveness of the proposed approach in improving the inspection, maintenance, and management of bridge structures. Finally, as highlighted in the Conclusions, the integration of technologies such as TLS, Revit, and Power BI represents a significant step forward in digital road infrastructure management and provides a foundation for future research focused on refining and expanding this methodology.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Infrastructure Maintenance and Monitoring · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil Engineering, Peruvian University of Applied Sciences, Lima 15023, Peru
Funding
Universidad Peruana de Ciencias Aplicadas
References (27)
Show source-provided references
1. Zhou, Y., Zhu, J., Zhao, L., Hu, G., Xin, J., Zhang, H., and Yang, J. (2024). High-Precision Monitoring Method for Bridge Deformation Measurement and Error Analysis Based on Terrestrial Laser Scanning. Remote Sens., 16.10.3390/rs16132263 ↗
2. Scianna, A., Gaglio, G.F., and La Guardia, M. (2022). Structure Monitoring with BIM and IoT: The Case Study of a Bridge Beam Model. ISPRS Int. J. Geo-Inf., 11.10.3390/ijgi11030173 ↗
3. Desarrollo de un sistema de mantenimiento de puentes Sistema para puentes de hormigón preesforzado mediante un modelo de gemelo digital, 3.D10.1080/15732479.2019.1620789 ↗
4. Método basado en difusos para un rendimiento sísmico eficiente Evaluación de estructuras con incertidumbre. Infraestructura Civil Asistida por Computación
5. Mohammadi, M., Rashidi, M., Mousavi, V., Yu, Y., and Samali, B. (2022). Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development. Remote Sens., 14.10.3390/rs14051148 ↗
6. Development of BrIM-Based Bridge Maintenance System for Existing Bridges10.3390/buildings13092332 ↗
7. Mohamed, A.G., Khaled, A., and Abotaleb, I.S. (2023). A Bridge Information Modeling (BrIM) Framework for Inspection and Maintenance Intervention in Reinforced Concrete Bridges. Buildings, 13.10.3390/buildings13112798 ↗
8. Aguilar, G., and Rodríguez, B. (2025, July 15). Uso de la Metodología BIM y BrIM en el Desarrollo de Sistemas de Gestión de Puentes. Available online: https://webofscience.upc.elogim.com/wos/woscc/full-record/WOS:001120774900001.
9. Aguilar, G., Rodríguez, B., and Trejos, V. (2025, June 13). Programas de Cómputo Comerciales Disponibles Para BIM y BrIM. Available online: https://www.lanamme.ucr.ac.cr/repositorio/handle/50625112500/2756.
10. Ambroziak, A., and Malinowski, M. (2021). A 95-year-old concrete arch bridge: From materials characterization to structural analysis. Materials, 14.10.3390/ma14071744 ↗
11. Boretti, V., Sardone, L., Bohórquez Graterón, L.A., Masera, D., Marano, G.C., and Domaneschi, M. (2023). Algorithm-Aided Design for Composite Bridges. Buildings, 13.10.3390/buildings13040865 ↗
12. Application of Augmented Reality Tools to Bridge Models Elaborated with BIM
13. Research on Bridge Structure Reliability Evaluation due to Vessels Collision Based on a Statistical Moment Method10.1155/2021/5620173 ↗
14. Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data10.1155/2021/8899323 ↗
15. Asset Management of Existing Concrete Bridges Using Digital Twins and BIM: A State-of-the-Art Literature Review10.2478/ncr-2021-0020 ↗
16. Application of bridge information modeling using laser scanning for static and dynamic analysis with concrete damage plasticity10.1016/j.aej.2023.08.023 ↗
17. Development of immersive bridge digital twin platform to facilitate bridge damage assessment and asset model updates10.1016/j.compind.2024.104189 ↗
18. Digital twins-boosted intelligent maintenance of ageing bridge hangers exposed to coupled corrosion–fatigue deterioration10.1016/j.autcon.2024.105697 ↗
19. Digital twin enhanced BIM to shape full life cycle digital transformation for bridge engineering10.1016/j.autcon.2022.104736 ↗
20. Damage identification of truss bridges based on feature transferable digital twins10.1016/j.measurement.2024.114735 ↗
21. Mousavi, V., Rashidi, M., Mohammadi, M., and Samali, B. (2024). Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions. Remote Sens., 16.10.3390/rs16111887 ↗
22. Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud10.1016/j.autcon.2024.105781 ↗
23. Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin10.1080/17538947.2024.2312219 ↗
24. Asset management, condition monitoring and Digital Twins: Damage detection and virtual inspection on a reinforced concrete bridge10.1080/15732479.2024.2311911 ↗
25. Resource-constrained bridge maintenance optimization by harmonizing structural safety and maintenance duration10.1016/j.engstruct.2024.118024 ↗
26. Yu, Y., Rashidi, M., Samali, B., Mohammadi, M., and y Nguyen, A. (July, January 30). Basado en visión por computadora Clasificación de grietas en puentes de hormigón mediante técnicas de aprendizaje automático. Proceedings of the 10ª Conferencia Internacional sobre Monitoreo de la Salud Estructural de Infraestructuras Inteligentes, SHMII 10, Oporto, Portugal. Clasificación basada en visión artificial de grietas en puentes de hormigón mediante técnicas de aprendizaje automático.
27. Ministry of Housing, Construction and Sanitation (2025, July 15). Technical Standard E.030: Earthquake-Resistant Design. National Building Regulations. National Training Service for the Construction Industry—SENCICO, Available online: https://www.gob.pe/sencico.