Abstract
Abstract. The paper presents DECAI - DEcay Classification using Artificial Intelligence, a novel study using machine learning algorithms to identify materials, degradations or surface gaps of an architectural artefact in a semi-automatic way. A customised software has been developed to allow the operator to choose which categories of materials to classify, and selecting sample data from an orthophoto of the artefact to train the machine learning algorithms. Thanks to Visual Programming Language algorithms, the classification results are directly imported into the H-BIM environment and used to enrich the H-BIM model of the artefact. To date, the developed tool is dedicated to research use only; future developments will improve the graphical interface to make this tool accessible to a wider public.
Highlights
The paper proposes a novel study that involves the use of digital tools and applications for the management and analysis of decays in historical buildings
3.1.3 H-Building Information Modelling (BIM) environment: The Machine Learning (ML) software produces a classified orthophoto, where each class is represented by a different colour and allows exporting the results in a Visual Programming Language (VPL) compatible file
Thanks to the developed VPL algorithms and the integration of Grasshopper with Revit, the information about the identified types of decay can be automatically projected onto the manually built H-BIM model, building the final H-BIM model, enriched with the results coming from the automatic ML classification of areas presenting signs of decay
Summary
The paper proposes a novel study that involves the use of digital tools and applications for the management and analysis of decays in historical buildings. The images collected of the artefact are studied individually: efflorescence, exfoliation, biological patina, cracks, black crusts, detachments, and stains due to rising damp; they are all identifiable "on sight" This information can be digitised using BIM software (Bruno and Roncella, 2019; Malinverni et al, 2019) where the digitisation process consists in the creation of objects that are computerised in the H-BIM environment and that can be created by parametric modelling (Brumana et al, 2017; Chiabrando et al, 2017) or using algorithms (Giovannini and Tomalini, 2021; Lo Turco et al, 2021). The classification results can be imported into the H-BIM environment by using Visual Programming Language algorithms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
