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Intelligent Infrastructure for Urban Transportation: The Role of Artificial Intelligence in Predictive Maintenance

Authors

  • Mohammed Ali Younus Alqasi Higher Institute of Science and Technology Wadi al-Shati, Fezzan, Libya
  • Youssif Ahmed Mohamed Alkelanie Higher Institute of Science and Technology Wadi al-Shati, Fezzan, Libya
  • Ahmed Jamah Ahmed Alnagrat Higher Institute of Science and Technology Wadi al-Shati, Fezzan, Libya

DOI:

10.47709/brilliance.v4i2.4889

Keywords:

Artificial Intelligence, Predictive Maintenance, Urban Transportation, Smart Infrastructure, Sensor Analytics

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Abstract

Urban transportation infrastructure, encompassing roads, bridges, and tunnels, is vital for city mobility but remains vulnerable to wear and damage over time. Traditional maintenance methods, which rely on reactive repairs and scheduled inspections, often fall short in preventing sudden failures, resulting in costly disruptions and safety risks. This study examines how artificial intelligence (AI) is revolutionizing infrastructure management through predictive maintenance. By deploying smart sensors and utilizing predictive analytics, AI enables the continuous monitoring of structural health and the proactive identification of potential issues before they escalate into serious failures. The research develops and tests an AI-based predictive maintenance model, which analyzes real-time data from embedded sensors in urban infrastructure to detect anomalies and predict failure patterns. Results indicate that the predictive maintenance model can enhance response times, reduce maintenance costs by 30%, and prevent approximately 92% of unexpected failures. These findings underscore the potential of AI-driven approaches to reduce unplanned disruptions, optimize resource allocation, and extend infrastructure lifespan, ultimately creating safer and more sustainable urban transportation systems. However, challenges in data variability and environmental interference are noted, suggesting areas for future refinement. This study provides a framework for integrating AI in urban infrastructure maintenance, highlighting its potential to transform how cities approach long-term infrastructure health and reliability.

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References

A. Gabbar, H., Chahid, A., U. Isham, M., Grover, S., Singh, K. P., Elgazzar, K., Mousa, A., & Ouda, H. (2023). HAIS: Highways Automated-Inspection System. Technologies, 11(2), 51. https://doi.org/10.3390/technologies11020051

AGOSTINELLI, S., & CUMO, F. (2022). MACHINE LEARNING APPROACH FOR PREDICTIVE MAINTENANCE IN AN ADVANCED BUILDING MANAGEMENT SYSTEM. WIT Trans. Ecol. Environ., 255, 131–138. https://doi.org/10.2495/EPM220111

Alm, J., Paulsson, A., & Jonsson, R. (2021). Capacity in municipalities: Infrastructures, maintenance debts and ways of overcoming a run-to-failure mentality. Local Economy, 36(2), 81–97. https://doi.org/10.1177/02690942211030475

Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), 355–361. https://doi.org/10.1109/IEA.2018.8387124

Austin, P. C., Harrell, F. E., & van Klaveren, D. (2020). Graphical calibration curves and the integrated calibration index (ICI) for survival models. Statistics in Medicine, 39(21), 2714–2742. https://doi.org/10.1002/sim.8570

Chen, Q., Cao, J., & Zhu, S. (2023). Data-driven monitoring and predictive maintenance for engineering structures: Technologies, implementation challenges, and future directions. IEEE Internet of Things Journal, 10(16), 14527–14551.

Cui, B., Wang, Z., Feng, Q., Ren, Y., Sun, B., & Yang, D. (2018). A Multi-Agent Based Framework for Maintenance Resource Scheduling Decision. 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC), 527–530. https://doi.org/10.1109/SDPC.2018.8664817

De Leon, V., Alcazar, Y., & Villa, J. L. (2019). Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors BT - Applied Computer Sciences in Engineering (J. C. Figueroa-García, M. Duarte-González, S. Jaramillo-Isaza, A. D. Orjuela-Cañon, & Y. Díaz-Gutierrez (eds.); pp. 523–533). Springer International Publishing.

Deng, Y., Li, F., Zhou, S., Zhang, S., Yang, Y., Zhang, Q., & Li, Y. (2023). Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2254), 20220175. https://doi.org/10.1098/rsta.2022.0175

Dhanraj, D., Sharma, A., Kaur, G., Mishra, S., Naik, P., & Singh, A. (2023). Comparison of Different Machine Learning Algorithms for Predictive Maintenance. 2023 International Conference for Advancement in Technology (ICONAT), 1–7. https://doi.org/10.1109/ICONAT57137.2023.10080334

Dhatrak, O., Vemuri, V., & Gao, L. (2020). Considering deterioration propagation in transportation infrastructure maintenance planning. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 520–528. https://doi.org/https://doi.org/10.1016/j.jtte.2019.04.001

Dhirani, L. L., Mukhtiar, N., Chowdhry, B. S., & Newe, T. (2023). Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. Sensors, 23(3), 1151. https://doi.org/10.3390/s23031151

Dui, H., Zhang, Y., Chen, L., & Wu, S. (2023). Cascading failures and maintenance optimization of urban transportation networks. Eksploatacja i Niezawodno?? – Maintenance and Reliability, 25(3). https://doi.org/10.17531/ein/168826

Durazo-Cardenas, I., Starr, A., Turner, C. J., Tiwari, A., Kirkwood, L., Bevilacqua, M., Tsourdos, A., Shehab, E., Baguley, P., Xu, Y., & Emmanouilidis, C. (2018). An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost. Transportation Research Part C: Emerging Technologies, 89, 234–253. https://doi.org/https://doi.org/10.1016/j.trc.2018.02.010

Elder, M. M. (2019). The Olympics Infrastructure: The Importance of Stakeholder Engagement and Sustainable, Intentional Urban Planning. International Conference on Sustainable Infrastructure 2019, 715–721.

Fan, Z., Liu, F., Li, Y., & Qu, J. (2020). A^ 2DTEL: Attention-Aware based Deep Tree Ensemble Learning. Proceedings of the 2020 International Conference on Computer Communication and Information Systems, 73–76.

Fengzhu, Y., Ruiping, Y., Bing, Z., & Cong, W. (2019). Research on Requirement Prediction of Equipment Maintenance Supporting Resources Based on Data Mining. 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 637–640. https://doi.org/10.1109/ICIVC47709.2019.8980858

Florian, E., Sgarbossa, F., & Zennaro, I. (2021). Machine learning-based predictive maintenance: A cost-oriented model for implementation. International Journal of Production Economics, 236, 108114. https://doi.org/https://doi.org/10.1016/j.ijpe.2021.108114

Forkan, A. R. M., Kang, Y.-B., Marti, F., Banerjee, A., McCarthy, C., Ghaderi, H., Costa, B., Dawod, A., Georgakopolous, D., & Jayaraman, P. P. (2024). AIoT-CitySense: AI and IoT-Driven City-Scale Sensing for Roadside Infrastructure Maintenance. Data Science and Engineering, 9(1), 26–40. https://doi.org/10.1007/s41019-023-00236-5

Fumagalli, M., & Simetti, E. (2018). Robotic Technologies for Predictive Maintenance of Assets and Infrastructure [From the Guest Editors]. IEEE Robotics & Automation Magazine, 25(4), 9–10. https://doi.org/10.1109/MRA.2018.2870987

Giannakidou, S., Radoglou-Grammatikis, P., Koussouris, S., Pertselakis, M., Kanakaris, N., Lekidis, A., Kaltakis, K., Koidou, M. P., Metallidou, C., Psannis, K. E., Goudos, S., & Sarigiannidis, P. (2022). 5G-Enabled NetApp for Predictive Maintenance in Critical Infrastructures. 2022 5th World Symposium on Communication Engineering (WSCE), 129–132. https://doi.org/10.1109/WSCE56210.2022.9916037

Gibson, J., & Rioja, F. (2017). Public infrastructure maintenance and the distribution of wealth. Economic Inquiry, 55(1), 175–186.

Giglioni, V., García-Macías, E., Venanzi, I., Ierimonti, L., & Ubertini, F. (2021). The use of receiver operating characteristic curves and precision-versus-recall curves as performance metrics in unsupervised structural damage classification under changing environment. Engineering Structures, 246, 113029. https://doi.org/https://doi.org/10.1016/j.engstruct.2021.113029

Gorenstein, A., & Kalech, M. (2022). Predictive maintenance for critical infrastructure. Expert Systems with Applications, 210, 118413. https://doi.org/https://doi.org/10.1016/j.eswa.2022.118413

Gyasi, P., & Wang, J. (2023). Optimal alarm trippoints and timers for avoiding false alarms *. 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), 1–7. https://doi.org/10.1109/SAFEPROCESS58597.2023.10295748

H. Money, W., & Cohen, S. (2019). Leveraging AI and sensor fabrics to evolve Smart City solution designs. Companion Proceedings of the 2019 World Wide Web Conference, 117–122.

Hadjidemetriou, G. M., Herrera, M., & Parlikad, A. K. (2022). Condition and criticality-based predictive maintenance prioritisation for networks of bridges. Structure and Infrastructure Engineering, 18(8), 1207–1221. https://doi.org/10.1080/15732479.2021.1897146

Huang, P., Zhang, X., Guo, L., & Li, M. (2021). Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations. IEEE Transactions on Mobile Computing, 20(2), 519–532. https://doi.org/10.1109/TMC.2019.2946800

Jiménez, F. J. G. (2013). Sostenibilidad económica municipal de los crecimientos y servicios urbanos asociados al planeamiento urbanístico. Universitat Politècnica de Catalunya (UPC).

Kalogridis, G., Fan, Z., & Basutkar, S. (2011). Affordable Privacy for Home Smart Meters. 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops, 77–84. https://doi.org/10.1109/ISPAW.2011.42

Kang, M. S., Jung, Y. G., Kim, M. H., & Ahn, K. (2014). Development of sensor nodes based on fault tolerance. International Journal of Security and Networks, 9(4), 190–196.

Kirichek, G., Kyrychek, D., Hrushko, S., & Timenko, A. (2019). Implementation the Protection Method of Data Transmission in Network. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), 129–132. https://doi.org/10.1109/ATIT49449.2019.9030482

Kosta*, B. P., & Naidu, D. P. S. (2020). A Lightweight Cryptographic Algorithm using Pseudo Stream Cipher and Trigonometric Technique with Dynamic Key. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 4623–4630. https://doi.org/10.35940/ijrte.F8749.038620

Kozhevnikov, S., & Svitek, M. (2022). From Smart City sustainable development to resiliency by-design. 2022 Smart City Symposium Prague (SCSP), 1–8. https://doi.org/10.1109/SCSP54748.2022.9792566

Kulkarni, A., Terpenny, J., & Prabhu, V. (2023). Leveraging Active Learning for Failure Mode Acquisition. Sensors, 23(5), 2818. https://doi.org/10.3390/s23052818

Kumar, V. S., Aavula, R., & Vasu, D. V. (2022). Real - Time Analytics Dashboard for Machine Maintenance in Legacy Machines Using Deep Transfer Learning and Computer Vision. Proceedings of the International Conference on Industrial Engineering and Operations Management, 500–512. https://doi.org/10.46254/IN02.20220204

Lee, K.-C., Villamera, C., Daroya, C. A., Samontanez, P., & Tan, W. M. (2021). Improving an IoT-Based Motor Health Predictive Maintenance System Through Edge-Cloud Computing. 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), 142–148. https://doi.org/10.1109/IoTaIS53735.2021.9628648

Liu, G., Zhang, X., Qian, Z., Chen, L., & Bi, Y. (2023). Life cycle assessment of road network infrastructure maintenance phase while considering traffic operation and environmental impact. Journal of Cleaner Production, 422, 138607. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.138607

Liu, J., Zhao, Y., Wang, B., Gao, J., Xu, L., & Ma, Y. (2022). Intelligent optimization and allocation strategy of emergency repair resources based on big data. 2022 18th International Conference on Mobility, Sensing and Networking (MSN), 1037–1042. https://doi.org/10.1109/MSN57253.2022.00168

Lok, L. K., Abdul Hameed, V., & Ehsan Rana, M. (2022). Hybrid machine learning approach for anomaly detection. Indonesian Journal of Electrical Engineering and Computer Science, 27(2), 1016. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024

Lorek, S., Vasishth, A., & Zoysa, U. de. (2012). Transforming livelihoods and lifestyles for the well-being of all: a Peoples’ Sustainability Treaty on Consumption and Production. Sustainability: Science, Practice and Policy, 8(2), 1–3. https://doi.org/10.1080/15487733.2012.11908091

Luo, X., Wang, F., & Li, Y. (2020). Prediction method of equipment maintenance time based on deep learning. Proc.SPIE, 11565, 115650M. https://doi.org/10.1117/12.2575725

Marovi?, I., Androji?, I., Jajac, N., & Hanák, T. (2018). Urban Road Infrastructure Maintenance Planning with Application of Neural Networks. Complexity, 2018(1), 5160417. https://doi.org/10.1155/2018/5160417

Morales, F. J., Reyes, A., Caceres, N., Romero, L. M., Benitez, F. G., Morgado, J., & Duarte, E. (2021). A machine learning methodology to predict alerts and maintenance interventions in roads. Road Materials and Pavement Design, 22(10), 2267–2288. https://doi.org/10.1080/14680629.2020.1753098

Mossé, D., Pötter, H., & Lee, S. (2020). Maintaining Privacy and Utility in IoT System Analytics. 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 157–164. https://doi.org/10.1109/TPS-ISA50397.2020.00030

Naskos, A., Gounaris, A., Metaxa, I., & Köchling, D. (2019). Detecting Anomalous Behavior Towards Predictive Maintenance BT - Advanced Information Systems Engineering Workshops (H. A. Proper & J. Stirna (eds.); pp. 73–82). Springer International Publishing.

Naskos, A., Kougka, G., Toliopoulos, T., Gounaris, A., Vamvalis, C., & Caljouw, D. (2020). Event-Based Predictive Maintenance on Top of Sensor Data in a Real Industry 4.0 Case Study BT - Machine Learning and Knowledge Discovery in Databases (P. Cellier & K. Driessens (eds.); pp. 345–356). Springer International Publishing.

Nguyen, P., Rao, R., Brown, V., McConnell, M., Barendt, N. A., Zingale, N. C., Mandal, S., Kaffashi, F., & Loparo, K. A. (2020). A Scalable Pavement Sensing, Data Analytics, and Visualization Platform for Lean Governance in Smart Communities. 2020 Moratuwa Engineering Research Conference (MERCon), 313–318. https://doi.org/10.1109/MERCon50084.2020.9185283

Park, H. J., Kim, N. H., & Choi, J.-H. (2022). A Trade-Off Analysis between Sensor Quality and Data Intervals for Prognostics Performance. Sensors, 22(19), 7220. https://doi.org/10.3390/s22197220

Pech, M., Vrchota, J., & Bedná?, J. (2021). Predictive maintenance and intelligent sensors in smart factory. Sensors, 21(4), 1470.

Piras, L., Al-Obeidallah, M. G., Pavlidis, M., Mouratidis, H., Tsohou, A., Magkos, E., Praitano, A., Iodice, A., & Crespo, B. G.-N. (2020). DEFeND DSM: A Data Scope Management Service for Model-Based Privacy by Design GDPR Compliance BT - Trust, Privacy and Security in Digital Business (S. Gritzalis, E. R. Weippl, G. Kotsis, A. M. Tjoa, & I. Khalil (eds.); pp. 186–201). Springer International Publishing.

Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22(4), 2462–2488.

Reiner, M., & McElvaney, L. (2017). Foundational infrastructure framework for city resilience. Sustainable and Resilient Infrastructure, 2(1), 1–7. https://doi.org/10.1080/23789689.2017.1278994

Sacconi, S., Ierimonti, L., Venanzi, I., & Ubertini, F. (2021). Life-cycle cost analysis of bridges subjected to fatigue damage. Journal of Infrastructure Preservation and Resilience, 2(1), 25. https://doi.org/10.1186/s43065-021-00040-3

Saini, R., & Ghosh, S. K. (2017). Ensemble classifiers in remote sensing: A review. 2017 International Conference on Computing, Communication and Automation (ICCCA), 1148–1152. https://doi.org/10.1109/CCAA.2017.8229969

Sarcevic, P., Sárosi, J., Csík, D., & Odry, Á. (2022). Inertial sensor-based movement classification with dimension reduction based on feature aggregation. 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), 113–118. https://doi.org/10.1109/CINTI-MACRo57952.2022.10029519

Selcuk, S. (2016). Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(9), 1670–1679. https://doi.org/10.1177/0954405415601640

Shafin, S. S., Karmakar, G., Mareels, I., Balasubramanian, V., & Kolluri, R. R. (2023). Whose Data are Reliable: Sensor Declared Data Reliability. 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 249–254. https://doi.org/10.1109/WiMob58348.2023.10187811

Shukla, K., Nefti-Meziani, S., & Davis, S. (2022). A heuristic approach on predictive maintenance techniques: Limitations and scope. Advances in Mechanical Engineering, 14(6), 16878132221101008. https://doi.org/10.1177/16878132221101009

Sim, S.-H., Paranjpe, T., Roberts, N., & Zhao, M. (2022). Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 1266–1273. https://doi.org/10.1109/ICMLA55696.2022.00203

Tang, H., Liu, X., & Zhang, R. (2021). Adaptive Noise Reduction Method of Sensor Signal for Health Monitoring of Civil Engineering Structure. 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), 1–6. https://doi.org/10.1109/PHM-Nanjing52125.2021.9612897

Teng, S., Chen, X., Chen, G., Cheng, L., & Bassir, D. (2022). Structural damage detection based on convolutional neural networks and population of bridges. Measurement, 202, 111747. https://doi.org/https://doi.org/10.1016/j.measurement.2022.111747

Thoben, K., Ait?Alla, A., Franke, M., Hribernik, K., Lütjen, M., & Freitag, M. (2018). Real?time Predictive Maintenance Based on Complex Event Processing. In Enterprise Interoperability (pp. 291–296). Wiley. https://doi.org/10.1002/9781119564034.ch36

Ucar, A., Karakose, M., & K?r?mça, N. (2024). Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends. Applied Sciences, 14(2), 898.

Ünal, A. F., Kaleli, A. Y., Ummak, E., & Albayrak, Ö. (2021). A Comparison of State-of-the-Art Machine Learning Algorithms on Fault Indication and Remaining Useful Life Determination by Telemetry Data. 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), 79–85. https://doi.org/10.1109/FiCloud49777.2021.00019

Vipond, N., Kumar, A., James, J., Paige, F., Sarlo, R., & Xie, Z. (2023). Real-time processing and visualization for smart infrastructure data. Automation in Construction, 154, 104998. https://doi.org/https://doi.org/10.1016/j.autcon.2023.104998

Wang, T., Zhao, D.-D., & Wu, S.-Y. (2023). Fatigue reliability evaluation of reinforced concrete bridges under stochastic traffic loading based on truck weight limits. Advances in Structural Engineering, 26(11), 1973–1987. https://doi.org/10.1177/13694332231178983

Xia, T., Fang, X., Gebraeel, N., Xi, L., & Pan, E. (2019). Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization. Journal of Manufacturing Science and Engineering, 141(5). https://doi.org/10.1115/1.4043255

Yang, D., Liu, Z., & Wei, S. (2023). Interactive Learning for Network Anomaly Monitoring and Detection with Human Guidance in the Loop. Sensors, 23(18), 7803. https://doi.org/10.3390/s23187803

Yang, F.-N., & Lin, H.-Y. (2019). Development of A Predictive Maintenance Platform for Cyber-Physical Systems. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 331–335. https://doi.org/10.1109/ICPHYS.2019.8780144

Yu, S. (2021). Research on the Construction of New Urban Rail Transit Management System Based on Cloud Computing Algorithm. 2021 International Conference on Networking, Communications and Information Technology (NetCIT), 139–142. https://doi.org/10.1109/NetCIT54147.2021.00035

Zenisek, J., Wolfartsberger, J., Sievi, C., & Affenzeller, M. (2019). Modeling sensor networks for predictive maintenance. On the Move to Meaningful Internet Systems: OTM 2018 Workshops: Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018, Valletta, Malta, October 22–26, 2018, Revised Selected Papers, 184–188.

Zhang, H., Zimmerman, J., Nettleton, D., & Nordman, D. J. (2020). Random Forest Prediction Intervals. The American Statistician, 74(4), 392–406. https://doi.org/10.1080/00031305.2019.1585288

Zhu, T. (2020). Analysis on the Applicability of the Random Forest. Journal of Physics: Conference Series, 1607(1), 12123. https://doi.org/10.1088/1742-6596/1607/1/012123

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ARTICLE Published HISTORY

Submitted Date: 2024-10-30
Accepted Date: 2024-10-31
Published Date: 2024-11-11

How to Cite

Alqasi, M. A. Y. ., Alkelanie, Y. A. M. ., & Alnagrat, A. J. A. (2024). Intelligent Infrastructure for Urban Transportation: The Role of Artificial Intelligence in Predictive Maintenance. Brilliance: Research of Artificial Intelligence, 4(2), 625-637. https://doi.org/10.47709/brilliance.v4i2.4889