Use of unmanned aerial vehicles in the determination of pavement surface condition indices

Authors

  • Juan José Alarcón Universidad Pedagógica y Tecnológica de Colombia
  • Hannsell Germán Contreras-Urbano Universidad Pedagógica y Tecnológica de Colombia
  • Laura Camila Uribe-Suarez Universidad Pedagógica y Tecnológica de Colombia

DOI:

https://doi.org/10.20868/ade.2024.5487

Keywords:

UAS, infraestructure, pavement management, geographic information systems

Abstract

Road infrastructure plays a fundamental role in the development of nations, both in terms of quality of life and economic growth. In Colombia, pavement management is a crucial aspect of keeping roads in good condition and ensuring their proper functioning. Currently, traditional pavement evaluation methods are expensive and ambiguous, so new technologies such as unmanned aerial vehicles (UAVs) are being implemented to optimize this process. The research presented aims to determine the feasibility of using UAVs to calculate the International Regularity Index (IRI), a key indicator to assess the surface condition of pavements. Through the processing of images captured by the UAVs, detailed information will be extracted from the running surface, which will be analyzed in specialized road management software. This information will make it possible to identify areas with potential damage and predict its evolution over time, facilitating timely decision-making for road maintenance and rehabilitation.
The successful implementation of this technology would have a significant impact on pavement management in Colombia, allowing for a more efficient, accurate and economical assessment of the national road network.

Downloads

Download data is not yet available.

References

1. Atencio, E., Plaza-Muñoz, F., Muñoz-La Rivera, F., & Lozano-Galant, J. A. (2022). Calibration of UAV flight parameters for pavement pothole detection using orthogonal arrays. Automation in Construction, 143, e104545. https://doi.org/10.1016/j.autcon.2022.104545

2. Badilla Vargas, G. (2011). Aspectos Y Consideraciones Importantes En El Cálculo Del Índice De Regularidad Internacional (Iri). Revista Ingeniería, 20(1–2). https://doi.org/10.15517/ring.v20i1-2.7271

3. Biçici, S., & Zeybek, M. (2021). Automation in Construction An approach for the automated extraction of road surface distress from a UAV-derived point cloud. Automation in Construction, 122, e103475. https://doi.org/10.1016/j.autcon.2020.103475

4. Chun, P. J., Yamane, T., & Tsuzuki, Y. (2021). Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and gis visualization. Applied Sciences (Switzerland), 11(3), 1–15. https://doi.org/10.3390/app11030892

5. Cruz Toribio, J. O., & Gutierrez Lazares, J. W. (2019). Evaluación Superficial de Vías Urbanas empleando Vehículo Aéreo No Tripulado (VANT). Métodos y Materiales, 8, 23–32. https://doi.org/10.15517/mym.v8i1.34113

6. De Solminihac T., H., N., T. E., & G., A. C. (2019). Gestión De Activos Viales. Gestión De Infraestructura Vial, 659–689. https://doi.org/10.2307/j.ctvkjb4dw.23

7. Ferrer-González, E., Aguera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2020). UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points. Remote Sensing, 12(2447), 1–19. https://doi.org/10.3390/RS12152447

8. Gisbert, C. M., Lozano-Galant, J. A., Paya-Zaforteza, I., & Turmo, J. (2020). Calibration of the descent local search algorithm parameters using orthogonal arrays. Computer-Aided Civil and Infrastructure Engineering, 35, 997–1008. https://doi.org/10.1111/mice.12545

9. Gopalakrishnan, K. (2018). Deep learning in data-driven pavement image analysis and automated distress detection: A review. Data, 3(3). https://doi.org/10.3390/data3030028

10. Gui, R., Xu, X., Zhang, D., Lin, H., Pu, F., He, L., & Cao, M. (2018). A component decomposition model for 3D laser scanning pavement data based on high-pass filtering and sparse analysis. Sensors (Switzerland), 18(7). https://doi.org/10.3390/s18072294

11. Herra Gómez, L. D. (2018). Conceptualización del procesamiento digital de imágenes para la evaluación de superficies de pavimento en Costa Rica. Infraestructura Vial, 20(35), 20–26. https://doi.org/10.15517/iv.v20i35.34831

12. Hoang, N. D. (2018). An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction. Advances in Civil Engineering, 2018, 1–12. https://doi.org/10.1155/2018/7419058

13. Jofré Briceño, C., Muñoz, F., Atencio, E., & Herrera, R. F. (2021). Implementation of facility management for port infrastructure through the use of UAVs, photogrammetry and BIM. Sensors, 21(6686), 1–27. https://doi.org/10.3390/s21196686

14. Kashiyama, T., Sekimoto, Y., Seto, T., & Lwin, K. K. (2020). Analyzing road coverage of public vehicles according to number and time period for installation of road inspection systems. International Journal of Geo-Information, 9(161), 1–14. https://doi.org/10.3390/ijgi9030161

15. Ministerio de Transporte. (2006). Manual para la inspección visual de pavimentos flexibles. In Manual para la inspeccion visual de pavimentos flexibles.

16. Ministerio de Transporte. (2016). Volumen 1: Aspector Informativos. In Manual de Mantenimiento de Carreteras.

17. Pacha, P. A., & Zárate Torres, B. A. (2020). Evaluación superficial de pavimentos rígidos en carreteras mediante ortoimágenes obtenidas mediante un vehículo aéreo no tripulado. Avances: Investigación En Ingeniería, 17(2), 1–15. https://doi.org/10.18041/1794-4953/avances.2.6599

18. Pan, Y., Zhang, X., Cervone, G., & Yang, L. (2018). Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3701–3712. https://doi.org/10.1109/JSTARS.2018.2865528

19. Prosser-Contreras, M., Atencio, E., La Rivera, F. M., & Herrera, R. F. (2020). Use of unmanned aerial vehicles (Uavs) and photogrammetry to obtain the international roughness index (iri) on roads. Applied Sciences (Switzerland), 10(24), 1–22. https://doi.org/10.3390/app10248788

20. Ragnoli, A., De Blasiis, M. R., & Di Benedetto, A. (2018). Pavement distress detection methods: A review. Infrastructures, 3(58), 1–19. https://doi.org/10.3390/infrastructures3040058

21. Romero-Chambi, E., Villarroel-Quezada, S., Atencio, E., & Rivera, F. M. La. (2020). Analysis of optimal flight parameters of unmanned aerial vehicles (UAVs) for detecting potholes in pavements. Applied Sciences (Switzerland), 10(12), 1–33. https://doi.org/10.3390/APP10124157

22. Sayers, M. (1995). On the calculation of international roughness index from longitudinal road profile. Transportation Research Record, 1501, 1–12.

23. Solminihac, H., Echaveguren, T., & Chamorro, A. (2019). Gestión de infraestructura vial. Alfaomega.

24. Tan, Y., & Li, Y. (2019). UAV photogrammetry-based 3D road distress detection. International Journal of Geo-Information, 8(409), 1–24. https://doi.org/10.3390/ijgi8090409

25. Wang, Y., & Ye, T. (2022). Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery. Journal of Engineering (United Kingdom), 2022, 1–16. https://doi.org/10.1155/2022/7733196

26. Yousaf, M. H., Azhar, K., Murtaza, F., & Hussain, F. (2018). Visual analysis of asphalt pavement for detection and localization of potholes. Advanced Engineering Informatics, 38(March), 527–537. https://doi.org/10.1016/j.aei.2018.09.002

27. Zhang, Y., Chen, B., Wang, J., Li, J., & Sun, X. (2020). APlCnet: Automatic pixel-level crack detection network based on instance segmentation. IEEE Access, 8, 199159–199170. https://doi.org/10.1109/ACCESS.2020.3033661

Downloads

Published

2024-12-01

Issue

Vol. 10 No. 3 (2024): September - December

Section

Articles

License

Copyright (c) 2025 Autor / BY-NC

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Anales de Edificación does not charge authors for processing or publishing an article and provides immediate Open Access to its content. All content is available free of charge to the user or his institution. Users are permitted to read, download, copy, distribute, print, search or link to the full text of articles, or use them for any other lawful purpose, without prior permission from the publisher or author. This is in accordance with the BOAI definition of open access.

  1. Authors retain the copyright and grant to the journal the right to a Creative Commons attribution / Non-Commercial / Non-Derivative 4.0 International (CC BY NC ND) License that allows others to share the work with an acknowledgement of authorship and non-commercial use.
  2. Authors may separately establish additional agreements for the non-exclusive distribution of the version of the work published in the journal (for example, placing it in an institutional repository or publishing it in a book).

Unless otherwise indicated, all contents of the electronic edition are distributed under a Creative Commons license.

 

How to Cite

Alarcón, J. J., Contreras-Urbano, H. G. ., & Uribe-Suarez, L. C. . (2024). Use of unmanned aerial vehicles in the determination of pavement surface condition indices. Anales De Edificación, 10(3), 33-36. https://doi.org/10.20868/ade.2024.5487