Structures such as buildings, bridges, masts and towers are increasingly being fitted with sensors to measure their in-situ performance. There is a huge variety in the types of sensors applied, and they measure parameters from air quality, temperature and environmental conditions to strain, force and acceleration. This project focuses on how information derived from those sensors’ data can improve structural engineering.
 
The opportunity: Engineers could use sensor data from existing structures to make future designs more efficient, and to apply new materials and technologies with confidence.
The problem: Sensor data from structures are difficult to interpret because they contain the mixed response to many unknown actions on the structure.
 
Structural engineering design is subject to a high degree of uncertainty. There is uncertainty in the loads a structure will be exposed to during its life, in the true material and geometric properties of the structure and in the boundary conditions where the structure interacts with its surroundings. This uncertainty leads to structural engineering being a conservative discipline: both numerically conservative in the parameters we use in design, but also conservative in terms of the materials and systems we use. This conservatism leads to increased material usage, and over-reliance on established materials and construction methods with high environmental impact, all of which must change if structural engineers are to contribute to mitigating climate change.
 
Structural sensing can help engineers to reduce uncertainty in design parameters by generating datasets of real loading and in-situ material properties. The process of “model updating” can help engineers recognise which values physical parameters should take so that their models better reflect real behaviour. This results in both a model which can be used to plan future changes to the structure and its use, and a basis for checking for damage. All of this is only possible if the data from sensors can be processed to give meaningful information about the underlying structure, and that’s not as easy as it might sound.
 
In-situ structural measurements are more difficult to process than lab test data. In situ, we tend to only measure the response of the structure, not the actions which caused it. Our measurements are also formed from the effects of various actions acting at once: temperature, wind and traffic loading, for example. In this project, you will have access to in-situ measurements from bridge structures at different scales: a footbridge and a large cable-stay road bridge, and you will generate novel methods to extract useful information sensor data.