Pre-posterior Analysis of Temperature-Compensated Structural Health Monitoring Data

In monitoring system design, the expected uncertainty of key parameters representing the structural behavior must be smaller than a target uncertainty. As a result, measurements can be used to optimally manage civil infrastructure. An important key parameter in PC-bridges control is the trend over time of structural responses, such as strain, deflection, and rotation purged from temperature effects. In fact, temperature changes cause significant responses that we must filter out to isolate distortions due to rheological effects in concrete, strand relaxation, and long-term prestress losses. We can quantify the uncertainty of temperature-compensated key parameters a posteriori, after the acquisition of data. However, in the design phase, monitoring data are not available yet; therefore, we must estimate the expected uncertainty a pre-posteriori. In this paper, we propose a logical procedure based on Bayesian inference for evaluating the pre-posterior uncertainty of long-term measurements-trend purged from temperature effects. We verify to what extent this quantity depends on: (i) measurement and model uncertainties; (ii) expected monitoring duration; (iii) sampling frequency; (iv) seasonal temperature variation. We validate our approach on a real-life case study: the Colle Isarco viaduct, a box-girder prestressed concrete bridge.