Bridges consist in fundamental structures to the transport infrastructure, developing essential links that contributes to the economy and social well-being. These structures, whether in railways or roadways, are subjected to damages developed due to their use over the course of time, especially caused by material deterioration process and cyclic loading. Therefore, the programmed inspection of this type of system is important to ensure serviceability and safety conditions. Current inspection intervals are calendar based and does not take into account the inspection needs for a given bridge based on its condition. Thus, the application of an inspection methodology that considers the characteristics and the deterioration process of these type of structures improves bridge safety and serviceability and optimize the use of resources for inspection. This method can be achieved by the application of the reliability theory and a risk-based inspection methodology, which requires efficient computational procedures and the analysis of a large amount of data obtained through inspection and monitoring. The adoption of new technologies, such as digital twins and techniques for identification of patterns of big data, characterizes efficient tools for carrying out risk analysis. Therefore, this work aims to develop a risk-based inspection methodology supported by new digital technologies, in order to assess the safety and serviceability conditions of bridge structures in real time.
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