Structural Health Monitoring of Concrete Elements Using Deep Machine Learning

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dc.contributor.authorKarypidis, Dimitrios
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Physics (Chalmers)en
dc.date.accessioned2019-07-05T11:53:35Z
dc.date.available2019-07-05T11:53:35Z
dc.date.issued2019
dc.description.abstractThe unique nature of Structural Engineering allows the field to integrate fresh innovations in its applications only at a slow pace. However, recent advancements in networking and artificial intelligence can greatly upgrade the current processes. This thesis reports the early findings of an ongoing project aimed at developing new methods to upgrade the current maintenance strategies of the civil and transport infrastructure. As part of these new methods, the use of Machine Learning (ML) algorithms is being investigated to constitute the core of a new generation of more accurate and robust structural health monitoring (SHM) systems for concrete structures. Unlike most of the existing SHM systems, relying on the analysis of the natural frequencies of the structure based on data obtained from accelerometers, the present study uses a distributed optic fiber system to monitor the strain distribution along steel reinforcing bars. The preliminary results of the study indicate that a semi-supervised Deep Autoencoder algorithm (DAE) can successfully quantify the damage attributable to transverse cracks in a reinforced concrete beam subjected to three-point loading. Future applications will feature the determination of crack locations, early detection of reinforcement corrosion as well as other types of damage such as splitting cracks or surface spalling.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256959
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectFysik
dc.subjectPhysical Sciences
dc.titleStructural Health Monitoring of Concrete Elements Using Deep Machine Learning
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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