A Conceptual Artificial Intelligence and Edge Computing-Based Framework for Rapid Post-Earthquake Management of Bridges in a Smart City
Abdullah Alariyan1, Mohammed Abdulaal2, Anas Alaryan3, Mohammed Alariyan4, Mahmoud Alhashash5, Abdulhadi Alzabout6, Abdulrahman Ahmed7, Ahed Habib8,*
1 Department of Civil Engineering, Eastern Mediterranean University, Famagusta, Northern Cyprus via Mersin 10, Mersin, Turkey
2 Department of Architecture, Eastern Mediterranean University, Famagusta, Northern Cyprus via Mersin 10, Mersin, Turkey
3 Department of Civil Engineering, Cairo University, Giza, Egypt
4 Department of Civil Engineering, University of Science and Technology Yemen, Aden, Yemen
5 Department of Civil Engineering, Cyprus International University, Famagusta, Cyprus
6 Department of Civil Engineering, Isra University, Amman, Jordan
7 Department of Civil Engineering, Fahd Bin Sultan University, Tabuk, Saudi Arabia
8 Sustainable Systems, Technologies, and Infrastructure Research Center, Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, United Arab Emirates
* Corresponding Author: Ahed Habib. Email: 
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.084371
Received 21 April 2026; Accepted 13 August 2026; Published online 31 August 2026
Abstract
Bridges are critical links in urban transportation networks, and delayed post-earthquake assessment can compromise public safety, emergency access, and network continuity. Although structural health monitoring, artificial intelligence (AI), and edge computing have advanced individually, an integrated bridge-specific workflow that links sensing, decentralized screening, uncertainty-aware decision support, and city-level coordination remains insufficiently developed. This study proposes a conceptual framework based on AI and edge computing for rapid post-earthquake bridge management in a smart city. Using a traceable design-synthesis procedure, requirements identified from the literature were translated into system layers, data flows, edge-cloud task allocation, uncertainty controls, and parameterized decision rules. The framework integrates Internet of Things sensors, local edge analytics, central context and network fusion, AI-based time-series and visual screening, reliability-weighted evidence fusion, confidence assessment, communication-loss operation, and professional assessment triggers. Its principal contribution is a validation-ready design specification that connects technical damage screening with provisional operational states and coordinated response while preserving professional authority over uncertain, severe, and reopening decisions. Because this is a theoretical framework study, no empirical dataset, simulation result, or field deployment is reported; accuracy, latency, false-alarm performance, and operational effectiveness must be established through the proposed staged simulation, hardware-in-the-loop, and pilot validation protocol.
Keywords
Earthquake resilience; artificial intelligence; edge computing; structural health monitoring; smart cities; conceptual framework; time-series analysis
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