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Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to...

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Autores principales: Ghahremani, Behzad, Enshaeian, Alireza, Rizzo, Piervincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322960/
https://www.ncbi.nlm.nih.gov/pubmed/35890852
http://dx.doi.org/10.3390/s22145172
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author Ghahremani, Behzad
Enshaeian, Alireza
Rizzo, Piervincenzo
author_facet Ghahremani, Behzad
Enshaeian, Alireza
Rizzo, Piervincenzo
author_sort Ghahremani, Behzad
collection PubMed
description This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios.
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spelling pubmed-93229602022-07-27 Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis Ghahremani, Behzad Enshaeian, Alireza Rizzo, Piervincenzo Sensors (Basel) Article This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios. MDPI 2022-07-10 /pmc/articles/PMC9322960/ /pubmed/35890852 http://dx.doi.org/10.3390/s22145172 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ghahremani, Behzad
Enshaeian, Alireza
Rizzo, Piervincenzo
Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title_full Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title_fullStr Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title_full_unstemmed Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title_short Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
title_sort bridge health monitoring using strain data and high-fidelity finite element analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322960/
https://www.ncbi.nlm.nih.gov/pubmed/35890852
http://dx.doi.org/10.3390/s22145172
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