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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20101 - Civil engineering

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)

Publication year

2025

Confidentiality

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

Article name in the collection

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

ISBN

9781003677895

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

1403-1408

Publisher name

CRC Press

Place of publication

London

Event location

Kapské město

Event date

Sep 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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