Fraunhofer Chalmers Research Centre for Industrial Mathematics
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Structural Health Monitoring of Concrete Elements Using Deep Machine Learning
No full textThe 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
Utvändig utrymning vid brand – Förtydligande av lokala variationer - En studie om brandevakuering i Storgöteborg
No full textNär butiker är passé gör City Scenen entré - Att bygga för scenkonst som en del av blandstaden
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Goods flow mapping - the potential of inland waterway transportation in the region around lake Vänern and Göta Älv river
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