A Conceptual Artificial Intelligence and Edge Computing-Based Framework for Rapid Post-Earthquake Management of Bridges in a Smart City
Year: 2026
Type: article
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.
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204802492