| Once a structure comes into service, the most important concern is its ability to keep providing steady and safe service. Assessment of structural health is a tool that can be used to ensure such. However, most of the available evaluation strategies in structural health monitoring are labor intensive such as visual inspection, which is highly economically inefficient, especially for large scale structures. As a result, structural health monitoring (SHM) and smart sensor techniques are of great interest that have brought the attention of scientists recently.;As an economic surface sensor for large scale structural surface, a soft elastomeric capacitor (SEC) was proposed in previous studies. However, the previous application of the SEC network was only able to monitor uniaxial strain filed due to the implicit directional strain measurement. To extend the applicability of the SEC sensor network into real-time biaxial strain field monitoring, an algorithm has been developed in this thesis based on a strain mapping approach using least square estimator. With the algorithm formulation based upon the classical plate theory, the biaxial sensing ability of the SEC sensor network with the proposed algorithm has been verified on both a rectangular cantilever plate under three types of load cases and an irregular laminated cantilever plate under simulated wind pressure. The proposed algorithm showed a computing speed around 0.1 s in a specific application which enables real-time monitoring and illustrated stability under noise level up to 5%. It can be concluded that the proposed algorithm possess the potential to be applied for wind turbine monitoring. |
