In Structural Health Monitoring, there are usually many strain sensors installed in different places of a single structure. The raw measurement of a strain sensor is generally a mixed response caused by different excitations such as moving vehicle loads, ambient temperature, etc. Monitoring data collected by different strain sensors are usually correlated with each other, correlation structures of responses caused by different excitations for different sensor pairs are quite diverse and complex. In Structural Health Monitoring, quantitatively describing and modeling complicated dependence structures of strain data is very important in many applications. In this article, copulas are exploited to characterize dependence structures and construct joint distributions of monitoring strain data. The constructed joint distribution is also applied in missing data imputation.
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