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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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 |
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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 |
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