A Robust Bayesian Sensor Placement Scheme with Enhanced Sparsity and Useful Information for Structural Health Monitoring

For the application of structural health monitoring in civil engineering structures, one common bane is the need for sensors. Optimizing the type of sensors, the number of sensors, and the location of sensors is therefore important in ensuring that the most optimal amount of information is obtained from measurement data while making the monitoring systems (including the sensors) economical. In this study, the issue of sensor placement is addressed by developing a simple Bayesian scheme based on information entropy and progressive increment or decrement in the number of available sensors. Compared to other conventional placement schemes available in the literature, the proposed scheme offers a simple yet robust configuration optimization, with results almost always the same as a full one-by-one search through all possible configuration candidates. The proposed scheme also provides enhanced sparsity of sensors by incorporating a spatially correlated covariance matrix for the measured data. The enhanced sparsity ensures that more "useful" information is contained in the measured data. To verify the proposed scheme's acclaimed improvement, especially for damage detection purposes, the analysis results for configurations selected by conventional algorithms and those selected by the proposed scheme are compared for a ballasted track system. Results clearly show significant improvement in configurations' optimality, with minimal computational cost.