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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Structural health monitoring

ISSN

1475-9217

e-ISSN

1741-3168

Svazek periodika

—

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

32

Strana od-do

1-32

Kód UT WoS článku

001568414800001

EID výsledku v databázi Scopus

2-s2.0-105016874347