Structural Health Monitoring of Concrete Elements Using Deep Machine Learning
Rapport, 2019

The 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.

machine learning

structural health monitoring

anomaly detection

deep autoencoders

distributed optic fiber

concrete structures

Författare

Dimitrios Karypidis

Chalmers, Fysik

SensIT – Sensorstyrd molnbaserad förvaltningsstrategi av infrastruktur

NCC, 2018-07-01 -- 2020-08-31.

Trafikverket, 2018-07-01 -- 2020-08-31.

Microsoft Research, 2018-07-01 -- 2020-08-31.

WSP Sverige, 2018-07-01 -- 2020-08-31.

Thomas Concrete Group, 2018-07-01 -- 2020-08-31.

Styrkeområden

Informations- och kommunikationsteknik

Building Futures (2010-2018)

Ämneskategorier

Transportteknik och logistik

Husbyggnad

Datavetenskap (datalogi)

Drivkrafter

Innovation och entreprenörskap

Utgivare

Chalmers tekniska högskola

Mer information

Senast uppdaterat

2019-07-02