Research Article A Cost-Benefit Methodology for Selecting Analytical Reinforced Concrete Corrosion Onset Models N. Rakotovao Ravahatra, 1,2 T. de Larrard, 2 F. Duprat, 2 E. Bastidas-Arteaga , 1 and F. Schoefs 1 1 Universit´ e de Nantes, Research Institute in Civil and Mechanical Engineering (GeM), UMR CNRS 6183, Nantes, France 2 Universit´ e de Toulouse, UPS, INSA, Materials and Durability of Constructions Laboratory (LMDC), Toulouse, France Correspondence should be addressed to E. Bastidas-Arteaga; emilio.bastidas@univ-nantes.fr Received 12 December 2019; Revised 20 May 2020; Accepted 15 June 2020; Published 7 August 2020 Academic Editor: Zhongguo John Ma Copyright © 2020 N. Rakotovao Ravahatra et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is work focuses on predicting corrosion onset induced by concrete carbonation or chloride ingress when using analytical predictive models. e paper proposes a procedure that helps building and infrastructure managers to select an appropriate model depending on the available information and the means granted to auscultation campaigns. e approach proposed combines the costs of input parameters, their relative importance, the benefits brought through obtaining parameters, and the maintenance strategy of the manager. Costs represent the intellectual investment to obtain parameters. is encompasses the time spent to obtain and analyze a result and the required expertise. Relative importance and benefits are obtained from sensitivity analysis. e effect of the maintenance strategy is introduced through a scalar called efficiency of the model. e proposed methodology is illustrated with two case studies where it is supposed that more or less extended information is available. ree concrete qualities are also considered in the case studies. e results highlight that the available data and concrete type have significant impacts on the selection of the most appropriate model. 1. Introduction It is widely accepted that a suitable maintenance strategy helps to lengthen the service life cycle of structures [1]. Corrosion of steel is known as one of the phenomena that significantly reduces the life cycle of reinforced concrete structures [2, 3] increasing failure risks [4]. Some studies have been conducted to improve maintenance strategies against this pathology and taking into account uncertainties [5–11]. e theory of value of information (VoI) shows how information could improve the performance of a given system [12, 13]. Some other studies considered expected value of perfect information (EVPI) such as Daneshkhah et al. [14] and Zitrou et al. [15]. Within a maintenance strategy, the prediction of the evolution along time of the degradation provided by models is an unavoidable crucial information. Indeed, this is necessary for helping to schedule repair or maintenance actions [16]. On the contrary, obtaining the values of input parameters could involve more or less im- portant financial resources. Depending on their strategies, managers would not be willing to pay the same amount to obtain such model parameters. Consequently, in order to provide better help to managers for decision-making, quantification of benefits brought by obtaining parameters and hence using a given model should be provided. A selection of degradation models is required for some purposes. First, models must be user-friendly (complex fi- nite element models for instance are not always convenient for the daily practice of building managers, and their use is rather intended for specific problems). Second, the owners of structures and engineers working for them are prone to use models presented in standards and recommendations be- cause these are generally recognized by insurance compa- nies. ird, the prediction of carbonation and chloride ingress is improved by accounting for the uncertainties related to material properties, exposure, and specific Hindawi Advances in Civil Engineering Volume 2020, Article ID 3286721, 22 pages https://doi.org/10.1155/2020/3286721