Development of a Deformation-Based Structural Health System with Contactless Sensors and Machine Learning for Health Characterization and Failure Prediction

Abstract

This dissertation presents the design and development of a structural health monitoring (SHM) system specifically tailored for transportation infrastructure components, such as bridges. The proposed system collects data by using contactless sensors and performs health characterization and failure prediction. It is capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project in order to validate the proposed system. Computer simulation in ANSYS and the application of gradient boosting neural networks were performed to produce a comparative and predictive analysis of the behavior of transportation infrastructures, which can be used to understand the health of the structure and make informed decisions.