Identifying structural damage using a convolutional neural network from time-domain dynamic response data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0199036" target="_blank" >RIV/00216305:26110/26:0199036 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.taylorfrancis.com/chapters/edit/10.1201/9781003677895-236/identifying-structural-damage-using-convolutional-neural-network-time-domain-dynamic-response-data-šplíchal-lehký?context=ubx&refId=cb1c3bec-e03f-4a50-8413-573212493c95" target="_blank" >https://www.taylorfrancis.com/chapters/edit/10.1201/9781003677895-236/identifying-structural-damage-using-convolutional-neural-network-time-domain-dynamic-response-data-šplíchal-lehký?context=ubx&refId=cb1c3bec-e03f-4a50-8413-573212493c95</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1201/9781003677895-236" target="_blank" >10.1201/9781003677895-236</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identifying structural damage using a convolutional neural network from time-domain dynamic response data

Popis výsledku v původním jazyce

Ageing transport infrastructure brings increased economic burden and uncertainties regarding the reliability, durability and safe use of structures. Early damage detection to locate incipient damage provides an opportunity for early structural maintenance and can guarantee structural reliability and continuing serviceability. Structural Health Monitoring (SHM) is essential for assessing structural conditions, using sensor data to detect potential issues. SHM complements predictive maintenance in modern industry, reducing downtime and costs by addressing problems before they escalate. Machine learning techniques are increasingly employed to analyse vibration data, extracting valuable insights often based on prior structural knowledge, further enhancing the accuracy and effectiveness of SHM efforts. This paper describes a method for identifying the location and extent of structural damage using a convolutional neural network (CNN). The time-domain dynamic response of the structure is provided as input data to CNN. The method is used to identify damage to an existing riveted truss bridge. The effect of damage rate and location on the identification speed and solution accuracy is investigated and discussed. The method is also compared to an artificial neural network-based inverse analysis method, where the input data is the dynamic response of the structure in the frequency domain.

Název v anglickém jazyce

Identifying structural damage using a convolutional neural network from time-domain dynamic response data

Popis výsledku anglicky

Ageing transport infrastructure brings increased economic burden and uncertainties regarding the reliability, durability and safe use of structures. Early damage detection to locate incipient damage provides an opportunity for early structural maintenance and can guarantee structural reliability and continuing serviceability. Structural Health Monitoring (SHM) is essential for assessing structural conditions, using sensor data to detect potential issues. SHM complements predictive maintenance in modern industry, reducing downtime and costs by addressing problems before they escalate. Machine learning techniques are increasingly employed to analyse vibration data, extracting valuable insights often based on prior structural knowledge, further enhancing the accuracy and effectiveness of SHM efforts. This paper describes a method for identifying the location and extent of structural damage using a convolutional neural network (CNN). The time-domain dynamic response of the structure is provided as input data to CNN. The method is used to identify damage to an existing riveted truss bridge. The effect of damage rate and location on the identification speed and solution accuracy is investigated and discussed. The method is also compared to an artificial neural network-based inverse analysis method, where the input data is the dynamic response of the structure in the frequency domain.

Druh

D - Stať ve sborníku

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 statě ve sborníku

Engineering Materials, Structures, Systems and Methods for a More Sustainable Future

ISBN

9781003677895

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1403-1408

Název nakladatele

CRC Press

Místo vydání

London

Místo konání akce

Kapské město

Datum konání akce

1. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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