Sustainable Smart Bridge Systems for Saudi Vision 2030 Infrastructure Development

References

[1] Alamoudi, A. K., Alsaad, A., & Alomari, K. (2023). Implementing smart sustainable cities in Saudi Arabia: A framework for citizen participation and stakeholder management. Sustainability, 15(8), 6648. https://doi.org/10.3390/su15086648

[2] Armijo, A., et al. (2024). Integration of railway bridge structural health monitoring with low-cost wireless accelerometers and machine learning. Sensors, 24(7), 2115. https://doi.org/10.3390/s24072115

[3] Bhatta, S., Shrestha, A., & Dissanayake, R. (2024). Use of IoT for structural health monitoring of civil engineering infrastructures: A review. Discover Civil Engineering, 1, 31. https://doi.org/10.1007/s44285-024-00031-2

[4] Costin, A., Adibfar, A., & Hu, H. (2024). Digital twin framework for bridge structural health monitoring and management. Transportation Research Record. https://doi.org/10.1177/03611981231208908

[5] Elsisi, A., et al. (2025). A comprehensive review of structural health monitoring technologies for steel bridges. Applied Sciences, 15(22), 12090. https://doi.org/10.3390/app152212090

[6] Gbran, H., &Alzamil, W. (2025). Achieving sustainable housing in smart cities: The role of model-based engineering in developing complex systems in line with Vision 2030. Smart Construction and Sustainable Cities, 3, 24. https://doi.org/10.1007/s44268-025-00062-w

[7] Hielscher, T., Khalil, S., Virgona, N., & Hadigheh, S. A. (2023). A neural network based digital twin model for the structural health monitoring of reinforced concrete bridges. Structures, 55, 105248. https://doi.org/10.1016/j.istruc.2023.105248

[8] Jimenez Rios, A., et al. (2023). Bridge management through digital twin-based anomaly detection: A systematic review. Frontiers in Built Environment, 9, 1176621. https://doi.org/10.3389/fbuil.2023.1176621

[9] Kang, V., et al. (2024). Evolution of digital twin frameworks in bridge management. Remote Sensing, 16(11), 1887. https://doi.org/10.3390/rs16111887

[10] Kang, X., et al. (2025). A concise review of state-of-the-art sensing technologies for bridge structural health monitoring. Sensors, 25(17), 5460. https://doi.org/10.3390/s25175460

[11] Li, X., et al. (2024). Structural health monitoring system based on digital twins for civil infrastructure safety assessment. Results in Engineering, 24, 102892. https://doi.org/10.1016/j.rineng.2024.102892

[12] Mariniello, G., et al. (2025). Enhancing structural health monitoring data management through blockchain-enabled traceability. Automation in Construction, 176, 106098. https://doi.org/10.1016/j.autcon.2025.106098

[13] Medrano-Sanchez, E., et al. (2025). Strategies for bridge maintenance using BIM: An analysis of digital workflows and common data environments. Frontiers in Built Environment, 11, 1693644. https://doi.org/10.3389/fbuil.2025.1693644

[14] Mousavi, V., Rashidi, M., Ghazimoghadam, S., & Samali, B. (2025). A data-driven digital twin model for bridge health monitoring using feature fusion and unsupervised deep learning. Information Fusion, 120, 103534. https://doi.org/10.1016/j.inffus.2025.103534

[15] Nasim, M., et al. (2025). A demonstration of a digital twin framework for structural health monitoring of ageing bridge infrastructure. Developments in the Built Environment, 22, 100477. https://doi.org/10.1016/j.dibe.2025.100477

[16] Noori Hoshyar, A., et al. (2023). Proposed machine learning techniques for bridge structural health monitoring using remote sensing data. Remote Sensing, 15(8), 1984. https://doi.org/10.3390/rs15081984

[17] Ozer, E., et al. (2024). Smartphone prospects in bridge structural health monitoring: A focused review. Sensors, 24(11), 3287. https://doi.org/10.3390/s24113287

[18] Plevris, V. (2024). AI in structural health monitoring for infrastructure systems: Trends, challenges and opportunities. Infrastructures, 9(12), 225. https://doi.org/10.3390/infrastructures9120225

[19] Plevris, V., et al. (2025). Responsible AI in structural engineering: A framework for trustworthy analysis, monitoring and design. Frontiers in Built Environment, 11, 1612575. https://doi.org/10.3389/fbuil.2025.1612575

[20] Prakash, V., et al. (2025). Structural health monitoring of concrete bridges through artificial intelligence, machine learning and deep learning techniques. Applied Sciences, 15(9), 4855. https://doi.org/10.3390/app15094855

[21] Saudi Vision 2030. (2024). Vision 2030 annual report and national transformation priorities. Government of Saudi Arabia. https://www.vision2030.gov.sa/

[22] Sonbul, O. S., et al. (2025). Bridge structural health monitoring: A multi-dimensional review of scalable and interpretable anomaly detection. Buildings, 15(19), 3603. https://doi.org/10.3390/buildings15193603

[23] Spencer, B. F., Jr., et al. (2025). Advances in artificial intelligence for structural health monitoring: From vibration-based methods to vision-based techniques. International Journal of Steel Structures, 25, 1-24.

[24] Sun, Z., Jayasinghe, S., Sidiq, A., Shahrivar, F., Mahmoodian, M., &Setunge, S. (2025). Approach towards the development of digital twin for structural health monitoring of civil infrastructure: A comprehensive review. Sensors, 25(1), 59. https://doi.org/10.3390/s25010059

[25] Wang, Q., et al. (2025). Current status and prospects of digital twin approaches in structural health monitoring. Buildings, 15(7), 1021. https://doi.org/10.3390/buildings15071021

[26] Xu, J., et al. (2023). Developing a digital twin model for monitoring building and infrastructure structural health information. Measurement, 218, 113189. https://doi.org/10.1016/j.measurement.2023.113189

[27] Zhang, D. Y., et al. (2024). Research on a bridge health monitoring system based on machine learning. ACM International Conference Proceedings. https://doi.org/10.1145/3701100.3701144

[28] Jayasinghe, S., et al. (2025). Smart structural monitoring: Real-time bridge response assessment using a digital twin. Sensors, 25(11), 3513. https://doi.org/10.3390/s25113513

[29] Salcedo Cerquin, R. F., et al. (2025). BrIM and digital twin integration for structural health monitoring using terrestrial laser scanning. Applied Sciences, 15(21), 11741. https://doi.org/10.3390/app152111741

[30] Jirawattanasomkul, T., et al. (2025). Digital twin-based structural health monitoring and predictive maintenance for bridge infrastructure. Developments in the Built Environment, 22, 100466. https://doi.org/10.1016/j.dibe.2025.100466

@article{1718906,
      author = {Syed Qalab Abbas},
      title = {Sustainable Smart Bridge Systems for Saudi Vision 2030 Infrastructure Development},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {1897-1906},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718906.pdf},
      abstract = {Background: Saudi Arabia is expanding roads, rail links, logistics corridors, smart cities and giga-project infrastructure under Vision 2030. Bridges within these networks are not only civil structures; they are mobility, safety, economic and environmental assets whose failure or closure can disrupt trade, tourism, emergency response and public confidence.
Aim: This review develops a Saudi-focused conceptual framework for sustainable smart bridge systems that integrates Structural Health Monitoring (SHM), Internet of Things (IoT) sensing, Artificial Intelligence (AI), Digital Twins and lifecycle sustainability assessment.
Methodology: A structured narrative review was conducted using peer-reviewed and policy literature published mainly between 2020 and 2025. The analysis synthesized evidence across five themes: sensing and monitoring, digital representation, predictive analytics, sustainability performance and governance.
Results: The review shows that smart bridge value is created when physical asset durability, sensor networks, digital intelligence and decision governance operate as one lifecycle system. AI-supported monitoring can improve anomaly detection, maintenance prioritization and service-life planning, while digital twins can connect inspection records, BrIM/BIM models, traffic exposure and climate data.
Contribution: The paper proposes a four-layer sustainable smart bridge framework, a Smart Bridge Maturity Index, a Saudi implementation roadmap and a Vision 2030 alignment matrix. These outputs provide a practical pathway for bridge owners, consultants and policy makers to move from isolated monitoring technologies toward integrated, low-carbon and resilient bridge asset management.
Conclusion: Sustainable smart bridge systems can strengthen Saudi infrastructure resilience, reduce unplanned closures, improve lifecycle carbon performance and support local digital engineering capability. Future research should validate the proposed framework through Saudi bridge case studies, pilot monitoring data and lifecycle cost-carbon modelling.},
      keywords = {Smart Bridges, Structural Health Monitoring, Digital Twin, IoT, Artificial Intelligence, Sustainable Infrastructure, Saudi Vision 2030.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718906}
  }