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Quantifying uncertainty in structural condition with Bayesian deep learning: A study on the Z-24 bridge benchmark
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Kvantifiering av osäkerhet i strukturella tillstånd med Bayesiansk djupinlärning (Swedish)
Abstract [en]

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 to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data.

Abstract [sv]

En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-EECS-EX ; 101
Keywords [en]
Bayesian Deep Learning, Autoencoders, Bridge Structural Health Monitoring, Bridge Damage Detection
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-251451OAI: oai:DiVA.org:kth-251451DiVA, id: diva2:1315691
Supervisors
Examiners
Available from: 2019-05-24 Created: 2019-05-14 Last updated: 2019-05-24Bibliographically approved

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