Deep neural networks for unsupervised damage detection on the Z24 bridge

                                
==Summary==

During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several natural hazards. Following these concerns, many researchers have attempted to develop reliable monitoring strategies, as integration to visual inspections, to efficiently ensure bridge maintenance and early-stage damage detection. In this framework, recent improvements in sensor technologies and data science have stimulated the use of Machine Learning (ML) algorithms for Structural Health Monitoring (SHM). Among unsupervised learning techniques, the potential of autoencoder networks has been attracting notable interest in the context of anomaly detection. In this light, the present paper proposes two different autoencoder-based damage detection techniques, focused on the Multi-Layer Perceptron (MLP) and the Convolutional Autoencoder (CAE) networks, respectively. During the training, the selected ML models learn how reconstructing raw acceleration sequences acquired from sound conditions. Unknown data, including both healthy and damaged bridge responses, are afterwards used to test the implemented networks and to detect damage occurrence. To this aim, a specific index of reconstruction loss is selected as a damage sensitive feature with the aim to quantify the errors between the original and reconstructed sequences. The performance exhibited by the two approaches is compared and evaluated by application to the Z24 benchmark bridge. Results demonstrate the effectiveness of the proposed methodology to perform feature classification and real time damage detection at the level of macro-sequences as new sensor data is collected, resulting suitable for continuous assessment of full-scale monitored bridges.

== Abstract ==
<pdf>Media:Draft_Sanchez Pinedo_9774902651344_abstract.pdf</pdf>

== Full Paper ==
<pdf>Media:Draft_Sanchez Pinedo_9774902651344_paper.pdf</pdf>