Cargando…

Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure

Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Inst...

Descripción completa

Detalles Bibliográficos
Autores principales: Epp, Tyler, Svecova, Dagmar, Cha, Young-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948750/
https://www.ncbi.nlm.nih.gov/pubmed/29596332
http://dx.doi.org/10.3390/s18041018
_version_ 1783322621484466176
author Epp, Tyler
Svecova, Dagmar
Cha, Young-Jin
author_facet Epp, Tyler
Svecova, Dagmar
Cha, Young-Jin
author_sort Epp, Tyler
collection PubMed
description Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.
format Online
Article
Text
id pubmed-5948750
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-59487502018-05-17 Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure Epp, Tyler Svecova, Dagmar Cha, Young-Jin Sensors (Basel) Article Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures. MDPI 2018-03-29 /pmc/articles/PMC5948750/ /pubmed/29596332 http://dx.doi.org/10.3390/s18041018 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Epp, Tyler
Svecova, Dagmar
Cha, Young-Jin
Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title_full Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title_fullStr Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title_full_unstemmed Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title_short Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
title_sort semi-automated air-coupled impact-echo method for large-scale parkade structure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948750/
https://www.ncbi.nlm.nih.gov/pubmed/29596332
http://dx.doi.org/10.3390/s18041018
work_keys_str_mv AT epptyler semiautomatedaircoupledimpactechomethodforlargescaleparkadestructure
AT svecovadagmar semiautomatedaircoupledimpactechomethodforlargescaleparkadestructure
AT chayoungjin semiautomatedaircoupledimpactechomethodforlargescaleparkadestructure