Bridges are among the most important structures of any road network. During their service life, they are subject to deterioration which may reduce their safety and functionality. The detection of bridge damage is necessary for proper maintenance activities. To date, assessing the health status of the bridge and all its elements is carried out by identifying a series of data obtained from visual inspections, which allows the mapping of the deterioration situation of the work and its conservation status. There are, however, situations where visual inspection may be difficult or impossible, especially in critical areas of bridges, such as the ceiling and corners. In this contribution, the authors acquire images using a prototype drone with a low-cost camera mounted upward over the body of the drone. The proposed solution was tested on a bridge in the city of Turin (Italy). The captured data was processed via photogrammetric process using the open-source Micmac solution. Subsequently, a procedure was developed with FOSS tools for the segmentation of the orthophoto of the intrados of the bridge and the automatic classification of some defects found on the analyzed structure. The paper describes the adopted approach showing the effectiveness of the proposed methodology.
Bridges constant assessment, monitoring and retrofitting are key aspects to prevent inadequate damage situations. Considering the importance of these processes, a new official guideline for Bridge evaluation, classification and monitoring has been issued in Italy. The usage of BIM methodology comes as a logical solution to store and manage all information related to the bridge surveillance process and create a unique database. In the present work, HBIM methodologies are implemented for the creation of a damage database and new approaches are tested for the application of the guidelines directly on the BIM environment. Using the dismantled structures of Largo Grosseto bridge as a case study and damage information previously recovered as input data, HBIM models are created using two different methodologies: Parametric modelling and Mesh-to-BIM process. Moreover, the utility of the database created is expanded thanks to the usage of visual programming tools. The evaluation of the modelling processes highlights the effectiveness of BIM for infrastructure monitoring and classification. The results obtained demonstrate the way towards a new BIM monitoring standard procedure for infrastructure surveillance processes.
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