CONDITION ASSESSMENT OF RC BRIDGES. INTEGRATING MACHINE LEARNING, PHOTOGRAMMETRY AND BIM

Abstract. The survey of building pathologies is focused on reading the state of conservation of the building, composed by the survey of constructive and decorative details, the masonry layering, the crack pattern, the degradation and the color recognition. The drawing of these representations is a time-consuming task, accomplished by manual work by skilled operators who often rely on in-situ analysis and on pictures. In this project three-dimensional an automated method for the condition survey of reinforced concrete spalling has been developed. To realize the automated image-based survey it has been exploited the Mask R-CNN neural network. The training phase has been executed over the original model, providing new examples of images with concrete cover detachments. At the same time, a photogrammetry process involved the images, in order to obtain a point cloud which acts as a reference to a Scan to BIM process. The BIM environment serves as a collector of information, as it owns the ontology to recreate entities and relationships. The information as extracted by neural network and photogrammetry serve to create the pictures which depict the concrete spalling in the BIM environment. A process of projecting information from the images to the BIM recreates the shapes of the pathology on the objects of the model, which becomes a decision support system for the built environment. A case study of a concrete beam bridge in northern Italy demonstrates the validity of the process.</p

Standard quantification and measurement of damages through features characterization of surface imperfections on 3D models: an application on Architectural Heritages

Abstract Reverse Engineering techniques lead to easily obtain, even in case of wide and complex objects, high-resolution 3D models, suitably adoptable in the field of surface analysis and characterization. This research aims to propose innovative quantification and measuring approaches to diagnose and monitor damages affecting artefacts of different nature, from manufacturing to architectural heritage, performing non-destructive analyses with advanced surface metrology instruments and the potential integrations of the existing sectorial standards. General condition assessment is proposed to recognize and classify characterized pathologies by meaningful features in the form of surface imperfections, through the analysis of acquired point clouds. The method is applied to decay phenomena of an architectural artefact

Heritage Building Information Model (BIM) for scientific data

This research aims at enabling usage of Building Information Modelling (BIM) within heritage science, and, specifically, it intends to provide a tool that depicts continuous surface quantities, such as weathering. BIM is a new technology that has been specifically developed for new buildings. BIM consists of several platforms, which enable the digital control and buildings’ design in all their life-cycle phases. It is possible to enrich BIM model’s elements with any data type. Data embedding is crucial in heritage; indeed, heritage buildings are often characterised by large amounts of data, either historical, or performance, i.e., environmental monitoring. Using BIM in heritage buildings could lead to a new way of working, given the critical data management issues in building conservation projects. However, using BIM technology in heritage presents challenges, and research is active on solving heritage-specific issues, such as modelling automation, surveying, data embedding, etc. A literature review enabled research gaps identification in some fields, including weathering depiction, as well as challenges and future steps that the BIM industry could take for the heritage sector. After identifying the need for heritage-specific technical solutions, a dedicated algorithm, which is able to read and depict data that will be useful for heritage, was created. This algorithm was tested in two case studies using Autodesk Revit and related technologies. The BIM models are enriched with data through Revit spreadsheets. A combination of Autodesk Dynamo and Python is used to produce an algorithm that can read, interpolate, and depict moisture in the Jewel Tower case study. In the second case study, Hellens Manor, we use a streamlined version of the Dynamo algorithm to depict the chemical components variation of historic glass panes. These 2 experiments demonstrate that our algorithm is flexible and can be successfully used to depict any surface quantity of interest to heritage