Three years after its first 2010 enactment, the French government proposed a timeline for the development of a new metro network providing new rapid transit lines in the Ile de France region. Implemented by the Société du Grand Paris (SGP), the Grand Paris Express (GPE) thus became the largest transport project in Europe. As in any new railway project, a safety record must be established by the SGP. Among all criteria that had to be investigated, the ability of the network manager to prevent and detect broken rails was a particularly sensitive point. Indeed, beyond the obvious consequences induced by a broken rail impacting the safety of passengers, such an event has a very strong impact on the availability of the infrastructure, which is a key point for all metro line automation projects. The SGP therefore required some decision support tools to enable us to evaluate the consequences of a broken rail on the network operating conditions and, moreover, to determine the best monitoring strategy and the adequacy of the maintenance policy to prevent broken rails. Based on a former study commissioned by RATP (the historic operator and infrastructure manager of the Paris metro network) dealing with the evaluation and optimization of detection and prevention of broken rails in a metro line automation context, the SGP wanted to possess an equivalent decision support tool customised to the needs and the characteristics of the GPE. Nevertheless, in the GPE context, the network that was considered does not yet exist. Moreover, no equivalent system exists in the world. No feedback databases are therefore available to estimate reliability parameters necessary for such decision support tools. Therefore, the present paper proposes an original approach combining physical modelling of rail deterioration with statistics to overcome this limitation.