Artificial Intelligent Techniques to Improve Pavement Maintenance Management
Publication: Construction Research Congress 2024
ABSTRACT
Pavement maintenance management requires artificial intelligent techniques to evaluate pavement condition automatically, predict pavement deterioration, and optimize maintenance actions under restrictive budgets. This paper presents three techniques that address the three objectives raised. First, Convolutional Neural Networks (CNN) are used to analyze images obtained by a camera installed on a vehicle. Several CNNs are trained to detect, classify, and quantify the 3D distresses. Second, pavement deterioration is predicted by Feed-forward Neural Networks (FNN). Pavement Condition Index (PCI) throughout a planning horizon is estimated from the information obtained by the inspection and the traffic and climate conditions. Third, heuristic optimization algorithms are used to determine the optimal maintenance plan. This plan indicates which sections should be repaired each year of the planning horizon and the treatment that must be used to optimize the maintenance cost, the CO2 emissions, the user cost, the network condition, and the accidents. These techniques are presented and discussed in this paper.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
AASHTO. 2012. Pavement Management Guide. American Association of State Highway and Transportation Officials (AASHTO). Washington D.C., USA.
Abdelaziz, N., R. T. Abd El-Hakim, S. M. El-Badawy, and H. A. Afify. 2020. “International Roughness Index prediction model for flexible pavements”. Int. J. Pavement Eng., 21 (1): 88–99. https://doi.org/10.1080/10298436.2018.1441414.
ASTM. 2018. Standard practice for roads and parking lots pavement condition index surveys (No. ASTM D6433-18). American Society for Testing and Materials. West Conshohocken, PA.
Augeri, M. G., S. Greco, and V. Nicolosi. 2019. “Planning urban pavement maintenance by a new interactive multiobjective optimization approach”. Eur. Transp. Res. Rev., 11 (1). https://doi.org/10.1186/s12544-019-0353-9.
Chen, W., and M. Zheng. 2021. “Multi-objective optimization for pavement maintenance and rehabilitation decision-making: A critical review and future directions”. Autom. Constr., 130: 103840. https://doi.org/10.1016/j.autcon.2021.103840.
Coenen, T. B. J., and A. Golroo. 2017. “A review on automated pavement distress detection methods”. Cogent Eng., 4 (1): 1374822. https://doi.org/10.1080/23311916.2017.1374822.
Donev, V., and M. Hoffmann. 2020. “Optimisation of pavement maintenance and rehabilitation activities, timing and work zones for short survey sections and multiple distress types”. Int. J. Pavement Eng., 21 (5): 583–607. https://doi.org/10.1080/10298436.2018.1502433.
García-Segura, T., L. Montalbán-Domingo, D. Llopis-Castelló, A. Sanz-Benlloch, and E. Pellicer. 2023. “Integration of deep learning techniques and sustainability-based concepts into an urban pavement management system”. Expert Syst. Appl., 231: 120851. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120851.
Gouda, M., I. Chowdhury, J. Weiß, A. Epp, and K. El-Basyouny. 2021. “Automated assessment of infrastructure preparedness for autonomous vehicles”. Autom. Constr., 129: 103820. https://doi.org/https://doi.org/10.1016/j.autcon.2021.103820.
Hankach, P., T. Lorino, and P. Gastineau. 2019. “A constraint-based, efficiency optimisation approach to network-level pavement maintenance management”. Struct. Infrastruct. Eng., 15 (11): 1450–1467. https://doi.org/10.1080/15732479.2019.1624787.
Hou, Y., Q. Li, C. Zhang, G. Lu, Z. Ye, Y. Chen, L. Wang, and D. Cao. 2021. “The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis”. Engineering, 7 (6): 845–856. https://doi.org/https://doi.org/10.1016/j.eng.2020.07.030.
Jato-Espino, D., J. Rodriguez-Hernandez, V. C. Andrés-Valeri, and F. Ballester-Muñoz. 2014. “A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements”. Expert Syst. Appl., 41 (15): 6807–6817. https://doi.org/10.1016/j.eswa.2014.05.008.
De La Garza, J. M., S. Akyildiz, D. R. Bish, and D. A. Krueger. 2011. “Network-level optimization of pavement maintenance renewal strategies”. Adv. Eng. Informatics, 25 (4): 699–712. https://doi.org/10.1016/J.AEI.2011.08.002.
Llopis-Castelló, D., T. García-Segura, L. Montalbán-Domingo, A. Sanz-Benlloch, and E. Pellicer. 2020. “Influence of pavement structure, traffic, and weather on urban flexible pavement deterioration”. Sustain., 12 (22): 1–20. https://doi.org/10.3390/su12229717.
Llopis-Castelló, D., R. Paredes, M. Parreño-Lara, T. García-Segura, and E. Pellicer. 2021. “Automatic classification and quantification of basic distresses on urban flexible pavement through convolutional neural networks”. J. Transp. Eng. Part B Pavements, 147 (4): 04021063. https://doi.org/https://doi.org/10.1061/JPEODX.0000321.
Peraka, N. S. P., and K. P. Biligiri. 2020. “Pavement asset management systems and technologies: A review”. Autom. Constr., 119. https://doi.org/10.1016/j.autcon.2020.103336.
Soncim, S. P., I. C. S. de Oliveira, and F. B. Santos. 2019. “Development of fuzzy models for asphalt pavement performance”. Acta Sci. - Technol., 41: 1–7. https://doi.org/10.4025/actascitechnol.v41i1.35626.
Tarawneh, B., and M. D. Nazzal. 2014. “Optimization of resilient modulus prediction from FWD results using artificial neural network”. Period. Polytech. Civ. Eng., 58 (2): 143–154. https://doi.org/10.3311/PPci.2201.
Torres-Machi, C., E. Pellicer, V. Yepes, and A. Chamorro. 2017. “Towards a sustainable optimization of pavement maintenance programs under budgetary restrictions”. J. Clean. Prod., 148: 90–102. https://doi.org/10.1016/J.JCLEPRO.2017.01.100.
Yepes, V., C. Torres-Machi, A. Chamorro, and E. Pellicer. 2016. “Optimal pavement maintenance programs based on a hybrid Greedy Randomized Adaptive Search Procedure Algorithm”. J. Civ. Eng. Manag., 22 (4): 540–550. https://doi.org/10.3846/13923730.2015.1120770.
Zimmerman, K. A., D. G. Peshkin, A. Wolters, and O. Smadi. 2011. Update to AASHTO Pavement Management Guide. Washington, DC, USA.
Information & Authors
Information
Published In
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Algorithms
- Architectural engineering
- Artificial intelligence and machine learning
- Building management
- Computer models
- Computer programming
- Computing in civil engineering
- Deterioration
- Engineering fundamentals
- Gravels
- Infrastructure
- Maintenance and operation
- Materials characterization
- Materials engineering
- Mathematics
- Models (by type)
- Neural networks
- Pavement condition
- Pavements
- Transportation engineering
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.