Trinity forces and reactions shaping vision-based smart structural health monitoring

Trinity forces and reactions shaping vision-based smart structural health monitoring

WoS Article

The convergence of deep learning (DL) and the Internet of Things (IoT) is revolutionizing vision-based structural health monitoring (SHM) by enabling unprecedented levels of intelligence and remote operability. However, the effective integration of SHM, DL, and IoT into a synergistic system remains significantly challenged by persistent disciplinary silos and a lack of systematic understanding regarding cross-domain knowledge transfer. This gap impedes the translation of domain-specific knowledge into practical engineering applications. To address this, we propose a vision-based smart structural health monitoring (VS-SHM) system framework and conceptualize the core interdisciplinary integration challenges as six forces. These forces effectively interconnect the three distinct domains of SHM, DL, and IoT: between SHM and IoT lie (1) Efficient Data Acquisition and Uninterrupted Flow, and (2) Fundamental Procedures for Processing Massive SHM Data; between DL and IoT are (3) Techniques for DL Model Light-weighting, (4) Hardware Acceleration for DL Deployment; between DL and SHM exist, (5) Ensuring Model Robustness and Data Augmentation in Real-World Scenarios, and (6) Optimizing DL Models for Specific Defect Characteristics. By synthesizing current research addressing these forces, this review establishes VS-SHM as a distinct interdisciplinary field and a pivotal enabler for intelligent infrastructure management in practical applications.

The convergence of deep learning (DL) and the Internet of Things (IoT) is revolutionizing vision-based structural health monitoring (SHM) by enabling unprecedented levels of intelligence and remote operability. However, the effective integration of SHM, DL, and IoT into a synergistic system remains significantly challenged by persistent disciplinary silos and a lack of systematic understanding regarding cross-domain knowledge transfer. This gap impedes the translation of domain-specific knowledge into practical engineering applications. To address this, we propose a vision-based smart structural health monitoring (VS-SHM) system framework and conceptualize the core interdisciplinary integration challenges as six forces. These forces effectively interconnect the three distinct domains of SHM, DL, and IoT: between SHM and IoT lie (1) Efficient Data Acquisition and Uninterrupted Flow, and (2) Fundamental Procedures for Processing Massive SHM Data; between DL and IoT are (3) Techniques for DL Model Light-weighting, (4) Hardware Acceleration for DL Deployment; between DL and SHM exist, (5) Ensuring Model Robustness and Data Augmentation in Real-World Scenarios, and (6) Optimizing DL Models for Specific Defect Characteristics. By synthesizing current research addressing these forces, this review establishes VS-SHM as a distinct interdisciplinary field and a pivotal enabler for intelligent infrastructure management in practical applications.

Structural health monitoring, deep learning, the internet of things, trinity forces, smart cities

Structural health monitoring, deep learning, the internet of things, trinity forces, smart cities

FU, R.; HUANG, Z.; NOVÁK, D.; CAO, M.

10.09.2025

Structural health monitoring

September

United Kingdom of Great Britain and Northern Ireland

1

32

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https://journals.sagepub.com/doi/epub/10.1177/14759217251365856