On the enhancement of data-driven damage assessment under environmental and operational variabilities using virtual sensing – VSHM


Focus on enhancing the performance of SHM methodologies to build robust damage indicators against environmental and operational variations. This project develop on the scientific topics of structural health monitoring (SHM), damage identification and virtual sensing.

Starting Date: dec 2021

Number of Partners: 1

Total Budget: 13,9K€

This project aims at investigating the development and implementation of virtually expanded vibration-based SHM (VSHM) methodologies as well as assessing their performance.

Data-driven VSHM implementations face issues of incompleteness that may be contained by proper data expansion methods. However, data obtained under environmental and operational variations (EOV) can mislead the damage identification process, as one may not be able to distinguish between sources of experimental variability.

The question is: Is the structural response changing due to EOV or to the existence of damage?

The proposed process requires the development of a continuously updated simplified model for the structure under analysis so one can evaluate the damage appearance and evolution.

As a result of this data-driven damage assessment framework, one will aid the process of sensor placement, giving a quantification on the quality of damage assessment against implementation cost.