## 7 Decision Making
### Value of Information and Uncertainty Quantification  
Structural health monitoring is a process aimed at providing accurate and real-time information concerning the condition and performance of structures, which can serve as an objective basis for making decisions regarding operation and maintenance. Nevertheless, in reality structure managers often make decisions based on their experience or on common sense, regardless of the action suggested by instrumental damage detection [216]. One reason is that damage detection is normally affectedby modeling errors and environmental noise such that the damage features are not deterministically related to the actual state and thus decision makers will differently weigh the detection outcomes. Another reason is that managers are very concerned with the consequences of wrong action and thus will make decisions considering the possible effects of the action they can undertake [217]. [PERSON] and [PERSON] [218] realized the need to evaluate the benefit of SHM on the decision making of owners or managers. [PERSON] and [PERSON] [219] were the first to calculate the value of information (VOI) for SHM by simulating an observable linear degradation law. [PERSON] et al. [217] introduced a rational framework to assess the impact of SHM on decision making, which address the effect of prior perception and quantify the economic impact of a wrong action.  
As discussed earlier, SHM is less used on real structures and one reason is the lack of understanding of the value of information obtained from SHM. Meanwhile, most of the decisions made in damage detection in civil structures are subject to a significant level of uncertainty. Due to these uncertainties, there is a risk of making an inappropriate decision, e.g., too little, too much, too soon, or too late. Therefore, uncertainty quantification plays a major role in the value of information and decision making [220]. There are various uncertainty quantification (UQ) methods developed for dealing with the uncertain parameters, which can be divided into two types as follows [221; 222].  
Probabilistic approaches assume that the input parameters of the model are random variables with a known probability density function. Three probabilistic UQ techniques are the Monte Carlo simulation method that considers the aleatory uncertainty, the Point estimate method that is based on the concept of moments of uncertain input parameters, and the scenario based method.  
Possibilistic approaches describe the input parameters using the membership function. This function only characterizes the bounds where all parameters lie in without knowing the actual distribution, which is able to consider the epistemic uncertainty and applies well in finite element model updating. The possibilistic UQ techniques include the evidence theory, interval analysis, fuzzy theory, Bayesian theory, convex model theory, and information gap decision theory for severe uncertainty.  
The main difference between these methods is in line with the different techniques used for describing the uncertainty of input parameters. The probabilistic UQ methods use probability density functions while the possibilistic UQ methods use membership functions to describe an uncertain parameter. The objective of UQ research is to reduce the computation complexity and corresponding costs, and to provide robust decision-making information.