Empirical validation of bayesian dynamic linear models in the context of structural health monitoring

Bayesian Dynamic Linear Models (BDLM) are traditionally employed in the fields of applied statistics and Machine Learning. This paper performs an empirical validation of BDLM in the context of Structural Health Monitoring (SHM) for separating the observed response of a structure into subcomponents. These sub-components describe the baseline response of the structure, the effect of traffic, and the effect of temperature. This utilization of BDLM for SHM is validated with data recorded on the Tamar Bridge (UK). This study is performed in the context of large-scale civil structures where missing data, outliers and non-uniform time steps are present. The study shows that the BDLM is able to separate observations into generic sub-components allowing to isolate the baseline behavior of the structure.

Uncontrolled Keywords

• Structural Health Monitoring (SHM)
• Bayesian
• Dynamic Lineal Models
• Kalman Filter
• Bridge
• infrastructure
• Tamar Bridge