# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 7 Conclusions  
In the literature, the VoI is presented as an important tool for the optimization of monitoring strategies of structures. Nevertheless, this comes with some computational challenges. In this work, some solutions are proposed to reduce the computational time. One of these solutions is the use of approximate posterior distributions based on MAP estimates instead of the computationally expensive MCMC sampling to calculate the posterior distributions. Another solution is performing part of the optimization of the monitoring strategy outside the VoI calculations. The locations of the sensors can be determined beforehand by applying an optimal sensor placement algorithm. As such, given the number of sensors, the optimal locations of these sensors are fixed. This leads to a reduced number of evaluations of the VoI. Another aspect lacking in the literature is the investigation of the influence of input parameters. Very often, the benefit of the VoI is illustrated by one specific example, where all parameters are assumed to be fixed. If an investigation on the influence of input parameters is performed, this is very often done for simplified conceptual structural models. In a practical case, it might be difficult to decide on the values of these input parameters, especially for complex structures. From the investigation performed in this work, it is concluded that the choice of the input parameters can have a significant impact on the VoI and the optimal monitoring strategy. Hence, caution is required when interpreting results of a VoI analysis, and the results of a VoI analysis should always be evaluated from a critical mindset. It might be beneficial to do the extra effort and calculate the VoI for different scenarios to be sure that the final conclusion (for example with respect to the choice of the monitoring strategy) remains valid. Figure 13: Influence on the uncertainty of the posterior costs when accounting for monitoring data based on the strain mode shapes at different time steps. Furthermore, the following conclusions are found in relation to the investigated structure:
* A lower measurement error leads to a larger VoI. Changing the error models can also affect the decision on the most optimal monitoring strategy. * Changing the cost of repair has a large influence on the VoI. * A higher cost of failure leads to a higher prior and posterior cost but also results in a larger VoI. * Changing the mean and/or COV of the diffusion coefficient has a large influence on the VoI. Moreover, the influence differs depending on the monitoring strategy considered. * Changing the parameters of the prior distribution for the corrosion rate can have a large impact on the VoI, ranging from a large positive to a large negative VoI. Moreover, the optimal monitoring strategy might change, with large differences in VoI between the different strategies. * When changing the distribution (mean value or standard deviation) of the surface chloride concentration, the influence on the VoI is very large and can range from a negative VoI to a large positive VoI. In the last section, a possible way to also account for the uncertainties on the costs in the calculation of the VoI is investigated. Here, a weight factor is used to account for this uncertainty on the costs. Depending on this weight factor, the most optimal monitoring strategy might change. Even though the VoI approach requires a rather high computational cost, these costs are generally very small compared to the costs of the actual monitoring system or the costs of failure of critical infrastructure. The VoI approach seems particularly useful for the management of large and critical bridges, e.g., in terms of traffic flow. For such bridges, large costs can be involved in the closure and/or failure of the bridge, and it can be beneficial to investigate beforehand whether investing in a monitoring strategy is beneficial or whether one can better stick to an a priori determined maintenance schedule. Conceptualization, E.V., W.B., G.L. and R.C. ; formal analysis, E.V.