dc.contributor.author | Bučinskas, Vytautas | |
dc.contributor.author | Dzedzickis, Andrius | |
dc.contributor.author | Šešok, Nikolaj | |
dc.contributor.author | Iljin, Igor | |
dc.contributor.author | Šutinys, Ernestas | |
dc.contributor.author | Šumanas, Marius | |
dc.contributor.author | Morkvėnaitė-Vilkončienė, Inga | |
dc.date.accessioned | 2023-09-18T16:12:28Z | |
dc.date.available | 2023-09-18T16:12:28Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1475-9217 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/112351 | |
dc.description.abstract | Paper provides an attempt to create a methodology for automated structure health monitoring procedures using vibration spectrum analysis. There is an option to use autoregressive (AR) spectral analysis to extract information from frequency spectra when conventional Fast Fourier transformation (FFT) analysis cannot give relevant information. An autoregressive spectrum analysis is widely used in optics and medicine; however, it can be applied for different purposes, such as spectra analysis in electronics or mechanical vibration. This paper presents an automated structural health monitoring approach based on the algorithm-driven definition of the first resonant frequency value from a noisy signal, acquired from trafficcreated bridge vibrations. We implemented the AR procedure and developed a peak detection algorithm for experimental data processing. The functionality of the proposed methodology was evaluated by performing research on six bridges in Vilnius (Lithuania). We compared three methods of data processing: FFT, filtered FFT and AR. Bridges vibrations under different excitation conditions (wind, impulse and traffic) in normal direction were measured using accelerometers. AR provided one peak representing the lowest resonant frequency in all cases, while FFT and filtered FFT provided up to 12 peaks with similar frequency values. Such results allow implementing our method for remote automated structures health monitoring and ensure structures safety using a convenient and straightforward diagnostic method. | eng |
dc.format | PDF | |
dc.format.extent | p. 2505-2517 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | Civil Engineering Abstracts | |
dc.relation.isreferencedby | Engineered Materials Abstracts | |
dc.relation.isreferencedby | Scopus | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://journals.sagepub.com/doi/pdf/10.1177/14759217211061518 | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:115133351/datastreams/MAIN/content | |
dc.title | Automatic quality detection system for structural objects using dynamic output method: Case study Vilnius bridges | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 68 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Mechanikos fakultetas / Faculty of Mechanics | |
dc.subject.researchfield | T 009 - Mechanikos inžinerija / Mechanical enginering | |
dc.subject.vgtuprioritizedfields | MC0101 - Mechatroninės gamybos sistemos Pramonė 4.0 platformoje / Mechatronic for Industry 4.0 Production System | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | Fast Fourier transformation | |
dc.subject.en | auto-regression | |
dc.subject.en | structural diagnostics | |
dc.subject.en | vibration spectrum | |
dc.subject.en | peak detection | |
dc.subject.en | automated structural health monitoring | |
dcterms.sourcetitle | Structural health monitoring | |
dc.description.issue | iss. 6 | |
dc.description.volume | vol. 21 | |
dc.publisher.name | SAGE | |
dc.publisher.city | London | |
dc.identifier.doi | 000738361300001 | |
dc.identifier.doi | 10.1177/14759217211061518 | |
dc.identifier.elaba | 115133351 | |