Bridge Data: A cloud-based platform for the assessment of the Class of Attention of existing bridges

The Italian Guidelines for the classification of existing bridges introduced a procedure based on a multi-Level approach. The first three Levels (from 0 to 2) allow identifying a Class of Attention for the infrastructure and require the acquisition and elaboration of a considerable amount of data based on surveys and visual inspections. To facilitate the data entry, a cloud-based software for the automatic management of inspections of existing bridges was implemented. The developed multi-platform management system, named Bridge Data, allows an easy real time access with any device (PC, tablet, phone) to the database via a user-friendly web app interface, allowing the concurrent consultation, data entry and generation of statistics, also considering different privileges for the users. Every bridge is surveyed considering basic data (e.g. coordinates, constructive typology), but also classifying the sub-elements of the super- and sub-structures (piers, abutments, longitudinal beams, slabs and so on), and organizing the documents in categories. A crucial aspect of the inspections lays on the identification of defects in each element of the bridge and requires an appropriate number of pictures (both general of the element and specific for each defect) and the need to fill out convenient forms, then further automatically elaborated to obtain comprehensive structural and seismic Levels of Defectiveness. Geometric, structural, geological, hydraulic, transportation data are also collected in the database, and finally processed to provide an automatic estimation of the Class of Attention consistent with the prescribed rules and logical flows. Bridge Data is not only the core for developing a complete Bridge Management System (BMS) but is also an organized database of pictures and documents. For every bridge it is also possible to generate a photographic survey and defectiveness forms as well as a detailed report describing the logical flow for the determination of the Class of Attention.