| KEYWORD |
Machine learning applications to structural monitoring of bridges
keywords BRIDGES, MACHINE LEARNING, MONITORING, STRUCTURES, SENSORS, STRUCT ANALYSIS
Reference persons MARCO DOMANESCHI
External reference persons Prof. JR Casas, UPC BarcelonaTECH
Thesis type EXPERIMENTAL APPLIED
Description This thesis proposal is focused on a methodology for detecting and localizing damage in bridges under traffic loads and environmental variability using real data. It is a second step of a previous study that was developed at the numerical level, where the environmental effects were removed from damage-sensitive features using Principal Component Analysis. Then, the damage was detected and localized using clustering technique (K-means Machine Learning algorithm).
This new development is intended to apply the previous step to a real world case study. The thesis will be developed in cooperation with UPC BarcelonaTECH, where the candidate will have the opportunity to spend a period.
The thesis presents several complexities and uncertainties related to application to the real bridge case that will have to be overcome by the student in cooperation with the advisors. However, overcoming the difficulties and achieving the objectives would represent a scientific achievement of interest.
Required skills Programming, structural analysis
Deadline 15/07/2023
PROPONI LA TUA CANDIDATURA