- Machine Learning for Structural Monitoring and Anomaly Detection
- Favarelli, Elia <1993>
Subject
- ING-INF/03 Telecomunicazioni
Description
- Autonomous structural health monitoring (SHM) of a large number of structures became a topic of paramount importance for maintenance purposes and safety reasons in the last few decades. Civil infrastructures are the backbone of modern society, and the assessment of their conditions is of renowned importance. This aspect is even more exacerbated because of the existing system that are fast approaching their service life. Since the replacement of those structures is functionally and economically demanding, maintenance and retrofitting operations must be planned wisely. Moreover, the increasing amount and variety of data generated by users and sensors interconnected to the future 6G network requires new strategies to manage several types of data with highly different characteristics and also requires solutions to power the wireless network with renewable energies. In this scenario, the adoption of artificial intelligence and in particular machine learning (ML) strategies represents a flexible and potentially powerful solution that must be investigated. To manage the big and widespread amount of data generated by the extensive usage of multiple types of sensors, several ML techniques can be investigated, with the aim to perform data fusion and reduce the amount of data. Furthermore, the usage of anomaly detection techniques to identify potentially critical situations in infrastructures and buildings represents a topic of particular interest that still needs a significant investigation effort. In this research activity, we provide the fundamental guidelines to perform automatic damage detection, which combines SHM strategies and ML algorithms capable of performing anomaly detection on a wide set of structures. In particular, several algorithms and strategies capable of extracting relevant features from large amounts of data generated by different types of sensors are investigated. Finally, to effectively manage such an amount of data in communication constraints, we obtained some design rules for the acquisition system for bridge monitoring.
Date
- 2021-05-21
Type
- Doctoral Thesis
- PeerReviewed
Format
- application/pdf
Identifier
urn:nbn:it:unibo-27695
Favarelli, Elia (2021) Machine Learning for Structural Monitoring and Anomaly Detection, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Monitoraggio e gestione delle strutture e dell'ambiente - sehm2