@misc{grossberger_lifecycle_2015, address = {Budapest}, title = {Lifecycle {Assessment} of {Existing} {Railway} {Infrastructures} and {Probabilistic} {Performance} {Approaches}}, abstract = {Railway operators are in a continuous pressure to minimize the escalating maintenance and rehabilitation costs of infrastructures; at the same time they are expected to provide a reliable service by optimum allocation of natural and economical resources. These challenges demand an excellent, risk informed decision process. The goal of the project ILCA (Integrated lifecycle Assessment of railway infrastructures) is to establish performance indicators for a railway network in reference to technical, economic, social and environmental requirements. This follows a procedural action to prepare a database for the lifecycle optimization based on the lifecycle performance and the direct and indirect consequences. Hence, this contribution attempts to show the prospect of optimizing the maintenance time of infrastructures at a railway network level using probabilistic approaches. At this stage, the contribution presents the general procedure to be followed in the project ILCA and emphasizes on probabilistic approach and selected performance indicators and prediction techniques to be applied in the estimation of the remaining lifetime of the infrastructures at a network level. This illustration of the probabilistic performance approach includes the concepts of uncertainty quantifications: random variable and stochastic structural deterioration processes. With Lifecycle model development for a selected railway network, first the components of the network such as rail tracks and bridges are treated separately, eventually be combined to assess the lifecycle of the entire network. This facilitates the study and suitability verification of deterioration and performance prediction models for the components of the whole network. Numerical analysis supports the study of different degradation scenarios. Furthermore, uuncertainties related to the quality of inspection information are treated by random variable model, where input parameters at a given inspection time (ti) are randomized.}, language = {English}, author = {Grossberger, Hirut and Michelberger, Frank}, month = oct, year = {2015}, note = {Projekt: LICORNE}, keywords = {Center for Sustainable Mobility, Institut für Mobilitätsforschung, Publikationstyp Präsentation}, }