Structural health monitoring of civil infrastructures under environmental variations using advanced computing methods

Much of infrastructures such as roads, railways, bridges and utilities have been built up over the last two centuries. Hence investment in “the skeleton” that supports our economy is needed and overdue. Structural health monitoring (SHM) has the potential to improve maintenance (i.e. save money) through early detection and replacement avoidance. However, the bottleneck is data interpretation. This task is even more difficult in the presence of environmental effects such as variations in wind, humidity and temperature.

This project will focus on the development of methodologies for SHM (i.e. structural identification and damage detection) that account for environmental variations. A detailed study will be conducted on relationships between environmental variations and structural behaviour. The project will involve finite element modelling of civil structures and advanced computing methods such as genetic algorithm, artificial neural network and support vector regression.