Digital Twin Framework for Real-Time Structural Health Monitoring and Flood Resilience in Urban Smart Infrastructure
DOI:
https://doi.org/10.68099/asnj.2024.77Anahtar Kelimeler:
digital twin- flood resilience- smart city- urban infrastructureÖz
Urban infrastructure systems face compounding threats from structural deterioration and climate-driven flood events. This paper proposes an integrated Digital Twin (DT) framework that couples real-time Structural Health Monitoring (SHM) with urban flood resilience management for smart city environments. The framework leverages a heterogeneous Internet of Things (IoT) sensor network — comprising accelerometers, strain gauges, piezometers, and water-level sensors — feeding continuously into a cloud-hosted digital replica of urban built assets. A hybrid machine learning pipeline combining Long Short-Term Memory (LSTM) autoencoders for anomaly detection with ensemble Kalman filtering for real-time model state updating is presented. The flood resilience module integrates hydrodynamic simulation with sensor-driven early-warning logic, enabling proactive emergency response. Architecture components, data flows, computational requirements, and implementation challenges are systematically described. The proposed framework provides a replicable, scalable foundation for data-driven urban resilience, directly applicable to bridges, drainage networks, and critical urban assets. Key contributions include a unified ontology linking structural and hydrological digital twin layers, a latency-optimised edge-cloud processing pipeline, and a multi-criteria decision support module for infrastructure managers.
Referanslar
Alibrandi, U. (2022). Risk-informed digital twin of buildings and infrastructures for sustainable and resilient urban communities. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3), 04022009. https://doi.org/10.1061/AJRUA6.0001239 DOI: https://doi.org/10.1061/AJRUA6.0001238
Bado, M. F., Tonelli, D., Poli, F., Zonta, D., & Casas, J. R. (2024). Integration of railway bridge structural health monitoring into the digital twin concept. Sensors, 24(1), 55. https://doi.org/10.3390/s24010055 DOI: https://doi.org/10.3390/s24010055
Backhaus, L., & Stamm, J. (2023). Leveraging urban digital twins for enhanced flood risk management and decision-making in smart cities. Book of Extended Abstracts, 41st IAHR World Congress, Vienna, Austria. International Association for Hydro-Environment Engineering and Research.
Baghalzadeh Shishehgarkhaneh, M., Keivani, A., Moehler, R. C., Jelodari, N., & Roshdi Laleh, S. (2022). Internet of Things (IoT), Building Information Modeling (BIM), and Digital Twin (DT) in construction industry: A review, bibliometric, and network analysis. Buildings, 12(10), 1503. https://doi.org/10.3390/buildings12101503 DOI: https://doi.org/10.3390/buildings12101503
Ferré-Bigorra, J., Casals, M., & Gangolells, M. (2022). The adoption of urban digital twins. Cities, 131, 103905. https://doi.org/10.1016/j.cities.2022.103905 DOI: https://doi.org/10.1016/j.cities.2022.103905
Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White Paper, Florida Institute of Technology. Retrieved from https://www.3ds.com/fileadmin/PRODUCTS-SERVICES/DELMIA/PDF/Whitepaper/Grieves-14-Digital_Twin_White_Paper_Dr_Grieves.pdf
Hu, X., Olgun, G., & Assaad, R. H. (2024). An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis. Expert Systems with Applications, 252, 124204. https://doi.org/10.1016/j.eswa.2024.124204 DOI: https://doi.org/10.1016/j.eswa.2024.124204
Kong, X., & Hucks, R. G. (2023). Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures. Automation in Construction, 152, 104928. https://doi.org/10.1016/j.autcon.2023.104928 DOI: https://doi.org/10.1016/j.autcon.2023.104928
Lei, X., Liu, C., Du, Z., Liu, W., & Hu, X. (2023). Machine learning-driven real-time topology optimization under moving morphable component-based framework. Journal of Applied Mechanics, 90(1), 011001. https://doi.org/10.1115/1.4055576 DOI: https://doi.org/10.1115/1.4055576
Li, C. Z., Guo, Z., Su, D., Xiao, B., & Tam, V. W. Y. (2022). The application of advanced information technologies in civil infrastructure construction and maintenance. Sustainability, 14(13), 7761. https://doi.org/10.3390/su14137761 DOI: https://doi.org/10.3390/su14137761
Liu, C., Zhang, P., & Xu, X. (2023). Literature review of digital twin technologies for civil infrastructure. Journal of Infrastructure Intelligence and Resilience, 2, 100050. https://doi.org/10.1016/j.iintel.2023.100050 DOI: https://doi.org/10.1016/j.iintel.2023.100050
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906–1955. https://doi.org/10.1177/14759217211036880 DOI: https://doi.org/10.1177/14759217211036880
Pan, Q., Bao, Y., & Li, H. (2023). Transfer learning-based data anomaly detection for structural health monitoring. Structural Health Monitoring, 22(5), 3077–3091. https://doi.org/10.1177/14759217221142174 DOI: https://doi.org/10.1177/14759217221142174
Rizvi, S. H. M., Abbas, M., & Tayyab, S. M. T. (2023). Anomaly detection and localization using LSTM based autoencoder with maximal overlap discrete wavelet transform for structural health monitoring. Engineering Research Express, 5(3), 032003. https://doi.org/10.1088/2631-8695/acef0d DOI: https://doi.org/10.20944/preprints202306.1007.v1
Sakr, M., & Sadhu, A. (2023). Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling. Journal of Infrastructure Intelligence and Resilience, 2, 100053. https://doi.org/10.1016/j.iintel.2023.100053 DOI: https://doi.org/10.1016/j.iintel.2023.100053
Tanoue, M., Tanaka, T., & Hirabayashi, Y. (2023). Exploring the potential of digital twin technology, 3D city models, and sensor networks for climate resilience in smart cities. Sensors, 23(5), 2659. https://doi.org/10.3390/s23052659 DOI: https://doi.org/10.3390/s23052659
Yuan, Y., Dou, X., Wang, H., & Chen, J. (2022). Community-scale big data for smart flood resilience: Opportunities and challenges. Journal of Hydrology, 610, 127968. https://doi.org/10.1016/j.jhydrol.2022.127968 DOI: https://doi.org/10.1016/j.jhydrol.2022.127968
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Telif Hakkı (c) 2024 Mihran Rushd Gerges, Wahbiyah Fatinah Kassab
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Geliş Tarihi: 2024-07-12
Kabul Tarihi: 2024-11-29