Juan Francisco Correal

The load rating of existing road bridges often requires construction drawings to analytically determine internal forces and member capacities. When such documentation is unavailable, engineers must rely on costly load tests, advanced field surveys, or expert judgment. To support the development of estimation models for unknown structural parameters, this paper presents a curated data set of beam–slab bridges, assembled from actual construction drawings obtained primarily from the archives of the Colombian National Institute of Roads (INVIAS). The data set includes 420 reinforced concrete (RC) decks and 892 RC and post-tensioned prestressed concrete girders, with over 60 parameters covering geometry, materials, and reinforcement details. Records were reviewed, cataloged, and filtered to ensure completeness and consistency. The data set is publicly available for download from a data repository. An exploratory analysis revealed clear relationships between material specifications and construction periods, as well as between span lengths and reinforcement or prestressing amounts. To demonstrate the data set’s utility for analytical bridge assessment, regression models were developed to estimate key reinforcement parameters, primarily using geometric predictors. For example, the best-performing model for total prestressing steel area ( A _ps ) achieved a validation root-mean-square error (RMSE) of 10% of the training set mean, and predictions for bottom steel ( A _{s , bot} ) in RC girders yielded a validation RMSE of 21% of the training set mean. These results confirm the capability of the data set to support data-driven estimation of structural details in bridges lacking documentation. The potential for further applications is also discussed.

Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data.