## 1 Introduction  
A predominant amount of roadway and railway bridges today consist of bridges erected using reinforced concrete. When observing developed countries, the main reasons for that can be traced to the boom in the building and reconstruction of infrastructure after the Second World War, as well as in the enhancement of concrete technology in the 20 th century. Although, when compared with other building materials, concrete is deemed as a durable material, nonetheless, evidence of its ageing can be found in infrastructure all over the world [1,2]. Chloride-induced corrosion is identified as one of main causes of reinforced concrete deterioration, especially in transportation infrastructure [3]. In 2002, the National Association of Corrosion EngineersInternational (NACE) and the Federal Highway Administration (FHWA) stated that approximately 134,000 bridges in the USA require urgent repairs due to structural deficiency caused by rebar corrosion [4]. This mobilized numerous researchers and organizations to invest substantial effort in developing and enhancing chloride ingress models, resulting in many different approaches for describing the time-dependent process of chloride ingress. For an overview of their development, and for a comprehensive description of different chloride ingress models, see [5; 6; 7; 8]. The model employed in the following analysis is supported by the Federation for Structural Concrete (_fib_) in Model Code 2010 [9], _fib_ Bulletin 34 [10], _fib_ Bulletin 59 [3], and _fib_ Bulletin 76 [11]. Projects, such as the BMB [12], HETEK [13], DuraCRETE [14], Lifecon [15], DARTS [16], CHLOROTEST [17], and Life-365 [18], etc., played an important role in shaping the current analytical model of chloride ingress supported by the _fib_. Simultaneously with enhancements in the modelling and understanding of the chloride ingress phenomenon, the testing and monitoring techniques of the chloride content have been developing. However, some studies reported that significant differences between theoretical and measured chloride content profiles still exist, possibly leading to under- or over-estimations of the chloride content and to wrong maintenance decisions [4]. Another research field that is emerging is the use of test and monitoring data of chloride content for decision making by applying Bayesian statistics. [PERSON] et al. [19] developed a probabilistic life-cycle cost model for marine structures exposed to chloride attack based on a Bayesian approach using monitoring data. [PERSON] et al. [20] studied Bayesian updating for the optimization of inspection schedules of chloride ingress into concrete. Moreover, in their more recent works, they presented and improved the Bayesian network configurations for parameter identification of concrete chlorination models [21; 22; 23]. Recently, the most significant effort within the Bayesian decision theory was invested through the work of the COST Action TU1402 \"Quantifying the Value of Structural Health Monitoring (SHM)\", which was active from 2014 until 2019. The effort of the action was directed in the formulation of so-called value of information (VoI) analysis within the Bayesian decision theory as a special case of the pre-posterior decision analysis [24; 25; 26]. It was a joint effort of 29 European countries, China, the USA, and Australia, with participants from academia, industry, and infrastructure owners, operators, and authorities. The aim was to provide (i) scientific evidence of the high value of SHM and its boundary conditions; (ii) an industrial and societal impact for the infrastructure design and management; and (iii) accessibility to the scientific field for quantifying the value of SHM [27]. Driven by the described developments and enhancements in the knowledge pool, this paper aimed to present the use of pre-posterior analysis for determining the value of information (VoI) for measurements of chloride content. Value of information (VoI) is the quantity a decision maker would be willing to pay for information before making a decision.