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Deep Machine Learning in Bridge Structures Durability Analysis

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Deep Machine Learning in Bridge Structures Durability Analysis

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dc.contributor.author Tomaszkiewicz, Karolina es_ES
dc.contributor.author Owerko, Tomasz es_ES
dc.date.accessioned 2023-02-13T11:53:21Z
dc.date.available 2023-02-13T11:53:21Z
dc.date.issued 2023-01-27
dc.identifier.isbn 9788490489796
dc.identifier.uri http://hdl.handle.net/10251/191796
dc.description.abstract [EN] According to Eurocode 0 structural durability is next to ultimate and serviceability one of the basic criteria in the structural design process. This article discusses the subject of concrete cracks observation in bridge structures, as one of the factors determining their durability. The durability of bridge structures is important due to both social, economic aspects and also the defense aspects of countries. Cracking of the reinforced concrete structures is a natural effect in concrete. The aim in the design and construction of structures is not to prevent the formation of cracks, but to limit their width to acceptable values. At the same time, there is a need for structure tests that allow for non-contact, fast measurements and algorithms that allow for efficient analysis of large amounts of measurement data. Deep machine learning algorithms can be used here. They can be used to analyse data which are acquired by means of photogrammetric methods (especially helpful during construction to inventory concealed works). Moreover, they can also be applied to standard data acquisition methods, consisting in photographing objects damage during works acceptance or periodic inspections. This paper discusses the application of deep machine learning to assess the condition of bridge structures based on photographs of object damage. The use of this method makes it possible to observe the rate and extent of damage development. Consequently, this method makes it possible to predict the development of damage in time and space in order to prevent failures and take structures out of service. es_ES
dc.format.extent 7 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 5th Joint International Symposium on Deformation Monitoring (JISDM 2022)
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Structural durability es_ES
dc.subject Deep machine learning es_ES
dc.subject Transfer learning es_ES
dc.subject Concrete cracks es_ES
dc.subject Bridge structure es_ES
dc.subject Durability es_ES
dc.title Deep Machine Learning in Bridge Structures Durability Analysis es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/JISDM2022.2022.13884
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Tomaszkiewicz, K.; Owerko, T. (2023). Deep Machine Learning in Bridge Structures Durability Analysis. En 5th Joint International Symposium on Deformation Monitoring (JISDM 2022). Editorial Universitat Politècnica de València. 405-411. https://doi.org/10.4995/JISDM2022.2022.13884 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename 5th Joint International Symposium on Deformation Monitoring es_ES
dc.relation.conferencedate Junio 20-22, 2022 es_ES
dc.relation.conferenceplace València, España es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/JISDM/JISDM2022/paper/view/13884 es_ES
dc.description.upvformatpinicio 405 es_ES
dc.description.upvformatpfin 411 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\13884 es_ES


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