# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
###### Abstract  
Before implementing a bridge monitoring strategy, a bridge manager would like to know the return on investment. Moreover, in order to spend the available budget as efficiently as possible, the monitoring strategy should be optimized, i.e., the type of measurements but also the time and locations at which these are performed. For this purpose, the Value of Information (VoI) can be used. The VoI represents an estimate of the benefit that can be gained from a monitoring strategy before it is actually implemented. By comparing the VoI of different alternative strategies, the one with the highest VoI can be selected. As such, the VoI is a tool for objective decision-making. The calculation of the VoI is based on pre-posterior analyses, including Bayesian updating of model parameters based on yet unknown monitoring outcomes. When calculating the VoI for an actual case, some challenges arise. First, the calculation of the VoI requires a number of assumptions on different input parameters. Second, the VoI is computed by evaluating life-cycle costs for different possible outcomes of the monitoring strategy, leading to a high computational cost. However, for practical implementations, results are preferably available within an acceptable time span and are robust with respect to the chosen input parameters. In this work, the implementation of the VoI approach for optimization of monitoring strategies is investigated by a problem statement in a case study where a reinforced concrete girder bridge is considered. To perform this optimization, the VoI for different monitoring strategies is compared. The calculation time required for the Bayesian updating of the model parameters based on the available data is limited by using Maximum A Posteriori (MAP) estimates to approximate the posterior distribution. The VoI can be used both to optimize a monitoring strategy or for comparison of different strategies. To limit the number of required (computationally expensive) evaluations of the VoI, optimization of the monitoring strategy itself can be simplified by determining the optimal sensor locations beforehand, based on a different metric than the VoI. For this purpose, the information entropy is used, which expresses the difference between the prior and posterior uncertainty of the model parameters. Finally, the sensitivity of the VoI to different input parameters is investigated. Value of Information (VoI); pre-posterior analysis; corrosion; Bayesian statistics; reinforced concrete 27 April 2021 25 May 2021 28 May 202  
provided by a proposed monitoring strategy [4]. The VoI can be used as a metric to optimize the monitoring strategy, i.e., the type of measurements performed, the time of measuring and the location of the sensors. Moreover, by calculating the VoI for different monitoring strategies, the best strategy can be chosen as the one with the highest VoI. The calculation of the VoI is based on a pre-posterior analysis where a Bayesian framework is used to update the model parameters based on yet unknown monitoring results. The costs in the calculation of the VoI correspond to the life-cycle costs, including costs for maintenance actions and repairs, costs for monitoring and inspection, and failure costs. For all these costs, relevant values should be used as input in the VoI analysis. Moreover, in a pre-posterior analysis, prior distributions of the model parameters are updated to posterior distributions based on yet unknown monitoring data. These prior distributions to be used in the analysis should also be defined. Assigning prior distributions to the model parameters and estimating the different costs required in the analysis can be a difficult task. Investigation is needed to determine whether these parameters influence the resulting VoI and whether a robust result is obtained or not. The relevant input parameters are pointed out in this work and their influence on the VoI is investigated. The calculation of the VoI can be computationally challenging.