Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel...

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Autores principales:	Lorenzo Bernardini, Marco Carnevale, Andrea Collina
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	railway bridges drive-by monitoring truss structures steel structures indirect methods for SHM Continuous Wavelet Transform (CWT) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/2ac21f31d69c47b7bdd02b31c74d99ce
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spelling	oai:doaj.org-article:2ac21f31d69c47b7bdd02b31c74d99ce2021-11-25T16:32:55ZDamage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms10.3390/app1122106052076-3417https://doaj.org/article/2ac21f31d69c47b7bdd02b31c74d99ce2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10605https://doaj.org/toc/2076-3417Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated.Lorenzo BernardiniMarco CarnevaleAndrea CollinaMDPI AGarticlerailway bridgesdrive-by monitoringtruss structuressteel structuresindirect methods for SHMContinuous Wavelet Transform (CWT)TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10605, p 10605 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	railway bridges drive-by monitoring truss structures steel structures indirect methods for SHM Continuous Wavelet Transform (CWT) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	railway bridges drive-by monitoring truss structures steel structures indirect methods for SHM Continuous Wavelet Transform (CWT) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Lorenzo Bernardini Marco Carnevale Andrea Collina Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
description	Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated.
format	article
author	Lorenzo Bernardini Marco Carnevale Andrea Collina
author_facet	Lorenzo Bernardini Marco Carnevale Andrea Collina
author_sort	Lorenzo Bernardini
title	Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
title_short	Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
title_full	Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
title_fullStr	Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
title_full_unstemmed	Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms
title_sort	damage identification in warren truss bridges by two different time–frequency algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2ac21f31d69c47b7bdd02b31c74d99ce
work_keys_str_mv	AT lorenzobernardini damageidentificationinwarrentrussbridgesbytwodifferenttimefrequencyalgorithms AT marcocarnevale damageidentificationinwarrentrussbridgesbytwodifferenttimefrequencyalgorithms AT andreacollina damageidentificationinwarrentrussbridgesbytwodifferenttimefrequencyalgorithms
_version_	1718413125807505408

Damage Identification in Warren Truss Bridges by Two Different Time–Frequency Algorithms

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