###### Abstract  
Currently, there is extensive research focused on automatic strategies for the segmentation and classification of 3D point clouds, which can accelerate the study of a landmark and integrate it with heterogeneous data and attributes, useful to facilitate the digital management of architectural heritage data. In this work, an automated image-based survey has been exploited a Region- Based Convolutional Neural Network. The training phase has been executed providing examples of images with the anomalies to be detected. At the same time, a laser scanning process was conducted to obtain a point cloud, which acts as a reference for the BIM process. In a final step, a process of projecting information from the images onto the BIM recreates the pathology shapes on the model's objects, which generates a decision support system for the built environment. The innovation of this research concerns the development of a workflow in which it is possible to automatize the recognition and classification of defects in historical buildings, to finally interpolate this geometric and numerical information with a BIM methodology, obtaining a representation and quantification of the information adapted to the facility management process. The use of innovative techniques such as artificial intelligence algorithms and different plug-ins becomes the main strength of this project. survey metadata; R-CNN neural network; database design; cultural heritage; information management Article  
Application of Deep Learning Approach for the Classification of Buildings' Degradation State in a BIM Methodology. Appl. Sci.2022127403. [[https://doi.org/10.3390/app12157403](https://doi.org/10.3390/app12157403)]([https://doi.org/10.3390/app12157403](https://doi.org/10.3390/app12157403))  
13 June 202221 July 202221 July 2022223 July 202221  
the need for the AECO sector to manage a large amount of data, where digitalization is the current solution [5]. In this scenario, the building digitalization process offers the opportunity to create virtual databases able to collect data from different disciplines in an efficient way [6; 7; 8]. This work aims to explore the potential of machine learning and deep learning approaches for the supervised classification of degradation pathologies applied to a case study, which can be reproduced in other building typologies. The research activity aims to develop an innovative workflow, applicable to existing buildings, where it is possible to automatize the recognition and classification of degradation pathologies and their characteristics, to allow integrated FM/AM through a BIM model. The particularity of the approach resides in the use of artificial intelligence (AI) solutions to automatically classify anomalies, not only by type but also to include their parameters, such as surface, gravity and the urgency of the intervention, and, finally, to combine these results in a BIM platform oriented to FM and AM. The established workflow can be divided into four steps. First, data acquisition with the application of a Terrestrial Laser Scanner (TLS) was carried out, which is a type of digital survey and a non-destructive technique. As a result, partial point clouds were obtained at each station, and after a subsequent data elaboration step through the application of software algorithms, an integral point cloud of the whole complex with the building geometry assembly was obtained. Then, by applying the scan to the BIM process, a 3D modeling step was developed, in which it was possible to achieve the reconstruction of complex geometries and creation of automatic families using programming software and plug-ins such as Dynamo. On the other hand, a data processing step with the identification of pathologies based on the photos of several building defects was performed, applying deep learning (DL) algorithms. Finally, the integration of the DL model in Revit as a plug-in was performed, to proceed with the automatic anomaly detection.