Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark
A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained t...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251451 |