Trinity forces and reactions shaping vision-based smart structural health monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0200261" target="_blank" >RIV/00216305:26110/26:0200261 - isvavai.cz</a>
Result on the web
<a href="https://journals.sagepub.com/doi/epub/10.1177/14759217251365856" target="_blank" >https://journals.sagepub.com/doi/epub/10.1177/14759217251365856</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/14759217251365856" target="_blank" >10.1177/14759217251365856</a>
Alternative languages
Result language
angličtina
Original language name
Trinity forces and reactions shaping vision-based smart structural health monitoring
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Structural health monitoring
ISSN
1475-9217
e-ISSN
1741-3168
Volume of the periodical
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Issue of the periodical within the volume
September
Country of publishing house
GB - UNITED KINGDOM
Number of pages
32
Pages from-to
1-32
UT code for WoS article
001568414800001
EID of the result in the Scopus database
2-s2.0-105016874347