Deep Learning for Structural Health Monitoring of Pavements for Improving Road Maintenance and Management

Mohamadreza Azodinia; Kazizat Iskakov; Orman Indira; Sina Ardabili; Nader Karballaeezadeh; Amir Mosavi

doi:10.31081/dmame8220251629

Authors

Mohamadreza Azodinia Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary
Kazizat Iskakov L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Orman Indira L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Sina Ardabili Department of Engineering Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
Nader Karballaeezadeh
Amir Mosavi Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan, Institute of the Information Society, Ludovika University of Public Service, Budapest, Hungary, Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan, Faculty of Economics and Informatics, Univerzita J. Selyeho, Komarom, Slovakia, John Von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary & Office of Research and Development, Asia University, Taichung, Taiwan

DOI:

https://doi.org/10.31081/dmame8220251629

Keywords:

Deep Learning, Big Data, Machine Learning, Applied AI, Pavement, Sustainable Development, Sustainable Cities and Communities, Data Science, Responsible Consumption and Production, Applied Mathematics, Artificial Intelligence

Abstract

Durable and secure roadway networks underpin sustainable mobility systems and urban growth. In recent years, Artificial Intelligence models have increasingly been employed to estimate the structural condition of roads and pavements. Such approaches enable agencies to organise maintenance schedules more effectively and to channel resources based on evidence-driven priorities. When combined with continuous sensor streams and traffic information, these systems support anticipatory and financially efficient road management, thereby strengthening decision reliability. This investigation develops machine learning frameworks to evaluate pavement structural condition. Variables including asphalt temperature and pavement layer depth are incorporated to forecast overall pavement performance. The resulting model offers a cost-efficient, unobtrusive technique for assessing current pavement status and anticipating emerging defects, which subsequently aids maintenance planning and resource distribution. Linking this modelling approach with wider smart city and smart mobility platforms facilitates real time surveillance of pavement behaviour, enhances road safety, and reinforces transport networks that favour long-term sustainability. The study compares long short-term memory techniques with alternative machine learning methods. The outcomes reveal that the long short-term memory model demonstrates superior behaviour during both training and validation, showing high predictive precision and stronger generalisation when applied to unseen datasets. Consequently, it is identified as the most suitable predictor for the data employed in this research. The results indicate that transport authorities can utilise Artificial Intelligence-based structural health monitoring systems for continuous pavement evaluation. These agencies are also encouraged to integrate sensor outputs and traffic information within unified data environments. Furthermore, policy measures that advance predictive maintenance practices are essential, as they contribute to extended pavement longevity, reduced expenditure, and improved public safety.

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References

[1] Ahmad, F., Alasskar, A., Samui, P., & Asteris, P. G. (2025). Machine learning-based graphical user interface for predicting high-performance concrete compressive strength: comparative analysis of gradient boosting machine, random forest, and deep neural network models. Frontiers of Structural and Civil Engineering, 19(7), 1075-1090. https://doi.org/10.1007/s11709-025-1201-8

[2] Akhayeva, Z., Zakirova, A., & ZHMUD, V. (2024). Optimization of water resources management using big data and machine learning in smart cities. Eurasian Journal of Mathematical and Computer Applications, 12(2), 4-15. https://doi.org/10.32523/2306-6172-2024-12-2-4-15

[3] Alaloul, W. S., Altaf, M., Musarat, M. A., Faisal Javed, M., & Mosavi, A. (2021). Systematic review of life cycle assessment and life cycle cost analysis for pavement and a case study. Sustainability, 13(8), 4377. https://doi.org/10.3390/su13084377

[4] Alatoom, Y. I., & Al-Suleiman, T. I. (2022). Development of pavement roughness models using Artificial Neural Network (ANN). International Journal of Pavement Engineering, 23(13), 4622-4637. https://doi.org/10.1080/10298436.2021.1968396

[5] Ali, A. A., Milad, A., Hussein, A., Yusoff, N. I. M., & Heneash, U. (2023). Predicting pavement condition index based on the utilization of machine learning techniques: A case study. Journal of Road Engineering, 3(3), 266-278. https://doi.org/10.1016/j.jreng.2023.04.002

[6] Alnaqbi, A., Al-Khateeb, G., & Zeiada, W. (2024). A hybrid approach of support vector regression with genetic algorithm optimization for predicting spalling in continuously reinforced concrete pavement. Journal of Building Pathology and Rehabilitation, 9(2), 146. https://doi.org/10.1007/s41024-024-00499-z

[7] Amari, S.-i. (1993). Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4-5), 185-196. https://doi.org/10.1016/0925-2312(93)90006-O

[8] Sengun, E., Kim, S., & Ceylan, H. (2024). A comparative study on structural design of plain and roller-compacted concrete for heavy-duty pavements. Road Materials and Pavement Design, 25(2), 392-422. https://doi.org/10.1080/14680629.2023.2209194

[9] Azodinia, M., Mudabbir, M., Ardabili, S., Varkonyi-Koczy, A., Iskakov, K., & Mosavi, A. (2025). Service Life Modeling of Pavement with Ensemble Learning. 2025 IEEE 12th International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC),

[10] Baduge, S. K., Thilakarathna, S., Perera, J. S., Ruwanpathirana, G. P., Doyle, L., Duckett, M., Lee, J., Saenda, J., & Mendis, P. (2023). Assessment of crack severity of asphalt pavements using deep learning algorithms and geospatial system. Construction and Building Materials, 401, 132684. https://doi.org/10.1016/j.conbuildmat.2023.132684

[11] Benkhalfallah, M. S., Kouah, S., & Harous, S. (2025). Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models. Energies, 18(14), 3672. https://doi.org/10.3390/en18143672

[12] Bernard, A., & Yakovenko, S. (2025). Application of machine learning methods to channel flow modelling. Eurasian Journal of Mathematical and Computer Applications, 13(2), 4-12. https://ejmca.enu.kz/assets/files/13-2-1.pdf

[13] Cano-Ortiz, S., Pascual-Muñoz, P., & Castro-Fresno, D. (2022). Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139, 104309. https://doi.org/10.1016/j.autcon.2022.104309

[14] Cao, Q., Al-Qadi, I. L., & Abufares, L. (2022). Pavement moisture content prediction: A deep residual neural network approach for analyzing ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. https://doi.org/10.1109/TGRS.2022.3224159

[15] Chen, K., Torbaghan, M. E., Thom, N., Garcia-Hernández, A., Faramarzi, A., & Chapman, D. (2024). A Machine Learning based approach to predict road rutting considering uncertainty. Case Studies in Construction Materials, 20, e03186. https://doi.org/10.1016/j.cscm.2024.e03186

[16] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., & Zhou, T. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4), 1-4. https://cran.ms.unimelb.edu.au/web/packages/xgboost/vignettes/xgboost.pdf

[17] Di Graziano, A., Marchetta, V., & Cafiso, S. (2020). Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review. Journal of Traffic and Transportation Engineering (English Edition), 7(5), 639-651. https://doi.org/10.1016/j.jtte.2020.08.001

[18] Doroudi, R., Lavassani, S. H. H., & Shahrouzi, M. (2024). Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge. Structural Health Monitoring, 23(5), 3227-3252. https://doi.org/10.1177/14759217231216694

[19] Ebika, I. M., Idoko, D. O., Efe, F., Enyejo, L., Otakwu, A., Odeh, I. I., & Headquarters, A.-V. (2024). Utilizing machine learning for predictive maintenance of climate-resilient highways through integration of advanced asphalt binders and permeable pavement systems with IoT technology. International Journal of Innovative Science and Research Technology, 9(11). https://doi.org/10.38124/ijisrt/IJISRT24NOV074

[20] Elghaish, F., Matarneh, S., Abdellatef, E., Rahimian, F., Hosseini, M. R., & Farouk Kineber, A. (2025). Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks. Smart and Sustainable Built Environment, 14(2), 511-535. https://doi.org/10.1108/SASBE-09-2023-0251

[21] Elghaish, F., Matarneh, S. T., Talebi, S., Abu-Samra, S., Salimi, G., & Rausch, C. (2022). Deep learning for detecting distresses in buildings and pavements: a critical gap analysis. Construction Innovation, 22(3), 554-579. https://doi.org/10.1108/CI-09-2021-0171

[22] Engqvist Stöhr, H., & Hennings, E. (2025). Deep Learning for Pavement Distress Forecasting: A Hybrid-LSTM Approach and Application Evaluation for Efficient Pavement Maintenance Forecasting. In. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1977793

[23] Fawad, M., Salamak, M., Poprawa, G., Koris, K., Jasinski, M., Lazinski, P., Piotrowski, D., Hasnain, M., & Gerges, M. (2023). Automation of structural health monitoring (SHM) system of a bridge using BIMification approach and BIM-based finite element model development. Scientific Reports, 13(1), 13215. https://doi.org/10.1038/s41598-023-40355-7

[24] Flah, M., Nunez, I., Ben Chaabene, W., & Nehdi, M. L. (2021). Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Archives of computational methods in engineering, 28(4). https://doi.org/10.1007/s11831-020-09471-9

[25] García-Segura, T., Montalbán-Domingo, L., Llopis-Castelló, D., Sanz-Benlloch, A., & Pellicer, E. (2023). Integration of deep learning techniques and sustainability-based concepts into an urban pavement management system. Expert Systems with Applications, 231, 120851. https://doi.org/10.1016/j.eswa.2023.120851

[26] Gharehbaghi, V. R., Noroozinejad Farsangi, E., Noori, M., Yang, T., Li, S., Nguyen, A., Málaga-Chuquitaype, C., Gardoni, P., & Mirjalili, S. (2022). A critical review on structural health monitoring: Definitions, methods, and perspectives. Archives of computational methods in engineering, 29(4), 2209-2235. https://doi.org/10.1007/s11831-021-09665-9

[27] Gooda, S. K., Chinthamu, N., Selvan, S. T., Rajakumareswaran, V., & Paramasivam, G. (2023). Automatic detection of road cracks using efficientnet with residual u-net-based segmentation and yolov5-based detection. Int. J. Recent Innov. Trends Comput. Commun, 11(4), 84-91. https://doi.org/10.17762/ijritcc.v11i4s.6310

[28] Goumiri, S., Yahiaoui, S., & Djahel, S. (2025). Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions. Journal of Intelligent Transportation Systems, 29(1), 81-117. https://doi.org/10.1080/15472450.2023.2245750

[29] Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37-45. https://doi.org/10.1007/978-3-642-24797-2_4

[30] Guo, W., Zhang, J., Murtaza, M., Wang, C., & Cao, D. (2023). An ensemble learning with sequential model-based optimization approach for pavement roughness estimation using smartphone sensor data. Construction and Building Materials, 406, 133293. https://doi.org/10.1016/j.conbuildmat.2023.133293

[31] Hatta, N., Zain, A. M., Sallehuddin, R., Shayfull, Z., & Yusoff, Y. (2019). Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artificial intelligence review, 52(4), 2651-2683. https://doi.org/10.1007/s10462-018-9634-2

[32] Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development Discussions, 2022, 1-10. https://doi.org/10.5194/gmd-15-5481-2022

[33] Jagatheesaperumal, S. K., Bibri, S. E., Ganesan, S., & Jeyaraman, P. (2023). Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis. Computational Urban Science, 3(1), 28. https://doi.org/10.1007/s43762-023-00104-y

[34] Jiang, X., Gabrielson, J., Huang, B., Bai, Y., Polaczyk, P., Zhang, M., Hu, W., & Xiao, R. (2022). Evaluation of inverted pavement by structural condition indicators from falling weight deflectometer. Construction and Building Materials, 319, 125991. https://doi.org/10.1016/j.conbuildmat.2021.125991

[35] Kang, J., Tavassoti, P., Chaudhry, M. N. A. R., Baaj, H., & Ghafurian, M. (2025). Artificial intelligence techniques for pavement performance prediction: a systematic review. Road Materials and Pavement Design, 26(3), 497-522. https://doi.org/10.1080/14680629.2024.2373222

[36] Ramadan, S., Kassem, H., ElKordi, A., & Joumblat, R. (2024). Incorporating artificial intelligence applications in flexible pavements: A comprehensive overview. International Journal of Pavement Research and Technology, 1-26. https://doi.org/10.1007/s42947-024-00496-y

[37] K Setyawan, A., Nainggolan, J., & Budiarto, A. (2015). Predicting the remaining service life of road using pavement condition index. Procedia Engineering, 125, 417-423. https://doi.org/10.1016/j.proeng.2015.11.108

[38] Kaur, G., & Kumar, R. (2025). Assessing Fatigue and Rutting Life of Flexible Pavements Using Soft-Computing Techniques. Journal of Failure Analysis and Prevention, 1-17. https://doi.org/10.1007/s11668-025-02176-w

[39] Khan, S. M., Atamturktur, S., Chowdhury, M., & Rahman, M. (2016). Integration of structural health monitoring and intelligent transportation systems for bridge condition assessment: Current status and future direction. IEEE Transactions on Intelligent Transportation Systems, 17(8), 2107-2122. https://doi.org/10.1109/TITS.2016.2520499

[40] Klibanov, M. V., & Timonov, A. . (2022). Deep learning and successive iterations for an inverse problem of the variable density reconstruction by the boundary control method. . Eurasian Journal of Mathematical and Computer Applications, 10(3), 37-57. https://doi.org/10.32523/2306-6172-2022-10-3-37-57

[41] Kodikara, J., Sounthararajah, A., & Chen, L. (2024). Reimagining unbound road pavement technology: Integrating testing, design, construction and performance in the post-digital era. Transportation Geotechnics, 47, 101274. https://doi.org/10.1016/j.trgeo.2024.101274

[42] Kulkarni, N. N., Raisi, K., Valente, N. A., Benoit, J., Yu, T., & Sabato, A. (2023). Deep learning augmented infrared thermography for unmanned aerial vehicles structural health monitoring of roadways. Automation in Construction, 148, 104784. https://doi.org/10.1016/j.autcon.2023.104784

[43] Mers, M., Yang, Z., Hsieh, Y.-A., & Tsai, Y. (2023). Recurrent neural networks for pavement performance forecasting: review and model performance comparison. Transportation Research Record, 2677(1), 610-624. https://doi.org/10.1177/03611981221100521

[44] Mudabir, M., Mosavi, A., Imre, F., Moniz, N., Iskakov, K., & Mohamadreza, A. (2025). Modeling Remaining Service Life and Structural Health Monitoring of Roads with Machine Learning and Deep Learning. 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI), 9798350379365. https://doi.org/10.1109/SAMI63904.2025.10883327

[45] Ngoc-Mai, N., & Cao, M. T. (2024). Predicting Mechanical Properties of Concrete Structures Using Metaheuristic Optimization-Based Machine Learning Models. Available at SSRN 4923416. https://ssrn.com/abstract=4923416

[46] Noureddine, S., Zineeddine, B., Toumi, A., Betka, A., & Benharkat, A.-N. (2022). A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm. International Journal of Computers and Applications, 44(5), 465-479. https://doi.org/10.1080/1206212X.2020.1809825

[47] Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27(5), 77. https://doi.org/10.3390/mca27050077

[48] Omar, I. (2023). Machine learning (ml) approaches to model interdependencies between dynamic loads and crack propagation Cranfield University]. https://dspace.lib.cranfield.ac.uk/handle/1826/24026

[49] Plevris, V., & Papazafeiropoulos, G. (2024). AI in structural health monitoring for infrastructure maintenance and safety. Infrastructures, 9(12), 225. https://doi.org/10.3390/infrastructures9120225

[50] Qureshi, W. S., Hassan, S. I., McKeever, S., Power, D., Mulry, B., Feighan, K., & O’Sullivan, D. (2022). An exploration of recent intelligent image analysis techniques for visual pavement surface condition assessment. Sensors, 22(22), 9019. https://doi.org/10.3390/s22229019

[51] Ramzan, B., Malik, M. S., Martarelli, M., Ali, H. T., Yusuf, M., & Ahmad, S. (2021). Pixel frequency based railroad surface flaw detection using active infrared thermography for structural health monitoring. Case Studies in Thermal Engineering, 27, 101234. https://doi.org/10.1016/j.csite.2021.101234

[52] Roberts, R., Inzerillo, L., & Di Mino, G. (2021). Exploiting data analytics and deep learning systems to support pavement maintenance decisions. Applied Sciences, 11(6), 2458. https://doi.org/10.3390/app11062458

[53] Saidin, S. S., Jamadin, A., Abdul Kudus, S., Mohd Amin, N., & Anuar, M. A. (2022). An overview: The application of vibration-based techniques in bridge structural health monitoring. International Journal of Concrete Structures and Materials, 16(1), 69. https://doi.org/10.1186/s40069-022-00557-1

[54] Sedighian-Fard, M., Golroo, A., Javanmardi, M., Alahi, A., & Rasti, M. (2025). Data generation for asphalt pavement evaluation: Deep learning-based insights from generative models. Case Studies in Construction Materials, e05116. https://doi.org/10.1016/j.cscm.2025.e05116

[55] Taherkhani, A., Cosma, G., & McGinnity, T. M. (2020). AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning. Neurocomputing, 404, 351-366. https://doi.org/10.1016/j.neucom.2020.03.064

[56] Tamagusko, T., Gomes Correia, M., & Ferreira, A. (2024). Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions. Infrastructures, 9(12). http://doi.org/10.3390/infrastructures9120213

[57] Xin, J., Akiyama, M., Frangopol, D. M., Zhang, M., Pei, J., & Zhang, J. (2021). Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks. Structure and Infrastructure Engineering, 17(6), 872-886. https://doi.org/10.1080/15732479.2020.1815807

[58] Yao, L., Leng, Z., Jiang, J., & Ni, F. (2022). Modelling of pavement performance evolution considering uncertainty and interpretability: a machine learning based framework. International Journal of Pavement Engineering, 23(14), 5211-5226. https://doi.org/10.1080/10298436.2021.2001814

[59] Zhang, H., Liu, T., Lu, J., Lin, R., Chen, C., He, Z., Cui, S., Liu, Z., Wang, X., & Liu, B. (2023). Static and ultrasonic structural health monitoring of full-size aerospace multi-function capsule using FBG strain arrays and PSFBG acoustic emission sensors. Optical Fiber Technology, 78, 103316. https://doi.org/10.1016/j.yofte.2023.103316

[60] Zhao, K., Xu, S., Loney, J., Visentin, A., & Li, Z. (2024). Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model. Automation in Construction, 167, 105664. https://doi.org/10.1016/j.autcon.2024.105664

[61] Zhu, J., Yin, Y., Ma, T., & Wang, D. (2025). A novel maintenance decision model for asphalt pavement considering crack causes based on random forest and XGBoost. Construction and Building Materials, 477, 140610. https://doi.org/10.1016/j.conbuildmat.2025.140610