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This is a Quantitative Study study.

October 22, 2025

Advancing SHM: Leveraging AI-Driven Algorithms to Integrate Multimodal Sensor Data, Models, and Structures at a Territorial Scale

Key Points

Advanced AI techniques enhance structural health monitoring, revolutionizing risk management for aging infrastructure.
Key innovations like self-sensing materials and deep learning drive improvements in damage classification and assessment.
Multiscale frameworks integrating diverse sensor data offer comprehensive insights into interconnected structural networks.
Strategies like Bayesian inference improve computational modeling, ensuring better-informed structural assessments and decisions.

Abstract

Advancements in AI and sensing technologies are transforming structural health monitoring (SHM), a crucial tool for maintaining aging infrastructure and cultural heritage structures. Despite progress in AI-driven analysis and selfsensing materials, effective prognosis in civil engineering remains challenging due to the gap between data-driven insights and physics-based models. To enable risk-informed decision-making on a larger scale, SHM must evolve into a multi-scale framework that integrates diverse sensor data with computational modeling, shifting from individual structures to interconnected networks. This talk explores cutting-edge SHM research, focusing on the integration of advanced sensing, AI analytics, and computational models. Key topics include self-sensing materials for smart masonry, deep learning and statistical pattern recognition for damage classification, and metamodeling with Bayesian inference for improved structural assessment. These innovations pave the way for smarter, data-driven infrastructure and built heritage management.

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