The implementation of structural health monitoring (SHM) for management and maintenance of critical transport infrastructures, such as bridges, dams or tunnels, is a widely established approach. Even though, SHM shows various technical limitations (e.g. relating to spatial capabilities of the sensors, high cost, repeatability or interpretation of the sensor measurements to support structural assessment and prediction of the infrastructure condition states). Furthermore, linking SHM with life cycle based methodologies such as life cycle costing (LCC) or life cycle assessment (LCA) is only recently discussed. The SENSKIN EC cofunded research project aims to overcome above mentioned challenges through development of a new sensor system and its integration within a Decision Support System (DSS) for proactive condition-based structural rehabilitation planning during the bridge life cycle. The DSS will include structural assessment models (exclusively based on sensor measurements for assessing the bridge condition and damage states of the main structural and a rehabilitation planning module (RPM) that will enable end-users to assess the life cycle economic and environmental implications of bridge rehabilitation options. Hereby, a tailored submodule for integrated life cycle costing (LCC) and life cycle assessment (LCA) assists, taking into account not only direct impacts of the rehabilitation solutions but also external effects caused by restricted traffic conditions (e.g. due to ongoing construction works). Thus, the SENSKIN project will contribute to a sustainable infrastructure. The following paper will sketch out the main scientific and functional structures of the developed DSS, with focus on the RPM and its LCA/LCC submodule for bridge rehabilitation planning.