Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Abstract: 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.

  AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)