## 1 Introduction  
In the global trend towards digitization in all engineering fields, Building Information Modeling (BIM) is currently leading a revolution in the construction sector. From the initial design of a building to its construction, operations and maintenance phases, all the data related to a construction project can now be collected and synchronized into a unified numerical model consisting of a 3D representation enriched with all useful properties (structural, thermal, budget, planning, etc.) [1]. Using the BIM platform, the architectural design team, construction unit, operations department and other parties can exchange engineering information in real time and work together, improving work efficiency, saving resources, reducing costs and ultimately achieving sustainable development [2, 3, 4]. Since the advent of the Industry Foundation Classes (IFC), more integrated methods do share construction data have emerged and have thus become adopted industry-wide [5]. The proliferation of IFC alone has had a major impact on how current tools and methods are developed in research and development [6]. Currently, a simplified procedure named META-FORMA (Mechanical-Typological Approach FOR Masonry Aggregates) is developed to obtain effective results about the seismic vulnerability of masonry aggregates through an automated mechanical-typological approach [7]. However, as a construction project that is constantly evolving, the quality and accuracy of the data related to all the objects of the building must constantly be checked in order to ensure that sufficient data is available at any given moment and for a given task. For example, to perform a structural analysis, the exact geometry of the structure, including all connections and material properties, must be described in the model with sufficient precision. Unfortunately, this data verification for all the concerned objects cannot be easily automated with an explicit set of rules due to the huge number and diversity of possible geometries, objects, etc. [8; 9]. The literature reveals that both unsupervised and supervised learning have already been applied to classification and prediction tasks [10; 11]. In 2015, [PERSON] [4] et al. showed how machine learning methods enable computing systems to derive implicit knowledge from a set of BIM models. In 2018, [PERSON] [10] et al. used machine learning methods to classify building images extracted from Building Information Modeling (BIM) software. In the same year, [PERSON] [11] et al. used machine learning to detect defects in BIM models and proposed a strategy for monitoring the regression of stone walls in historical monuments through reality capture, data processing (including machine learning) and BIM models. In April 2018, [PERSON] [12] et al. proposed an approach to automate the multi-dimensional, objective and quantitative evaluation of BIM model quality. In November 2018, [PERSON] [13] et al. proposed a building space function classification method based on BIM data, which mined BIM related attributes using unsupervised learning. In 2019, [PERSON] [14] performed the automatic classification of Building Information Modeling (BIM) using natural language processing (NLP) and unsupervised learning. Based on the existing body of research, both domestic and international, on this subject [15; 16; 17], it is not difficult to find that the main direction of the current research on BIM technology using machine learning is building classification. Most research papers are concerned with the image reconstruction of geometric objects, using image processing, including image recognition, image classification, etc. In contrast, the motivation of the current work is to try and solve the problem of element classification and detection of abnormal data in BIM models, using machine learning algorithms applied to the IFC (Industry Foundation Classes) file itself; in other words, the standard format of BIM models.