On the impact of prior perception on bridge health diagnosis

We illustrate an application of Bayesian logic to analysis of monitoring data and to inference of structural condition. The case study is a cable-stayed bridge, having a composite steel-concrete deck, supported by 12 cables. To monitor load redistribution, the owner installed a monitoring system that includes fiberoptic sensors. These sensors measure changes in deformation with respect to the value at installation. After one year of operation, which included maintenance on the interrogation unit, the data showed an apparent contraction of the cables. We discuss to what extent a rational agent is prone to accept the sensor response as the result of the real mechanical behavior of the bridge versus a mere malfunction of the interrogation unit. Using Bayesian logic as a tool to combine prior belief with sensor data, we highlight how the extent of prior knowledge can alter the final engineering perception of the current state of the bridge.