A Theoretical Framework for Risk Analysis in Construction Projects Using BIM Data and Machine Learning

The objective of this paper is to develop a theoretical framework for the analysis of data exported from a Building Information Modeling (BIM) model through the application of Artificial Intelligence methods, serving as a foundation for risk assessment in construction projects. The purpose of this study is to investigate the potential of data mining techniques that function independently of biases introduced by predefined labelling. In recent years, a growing body of literature has examined the role of BIM technology in risk management. The most prevalent applications primarily rely on 3D visualization, which facilitates the identification and deeper understanding of potential issues related to design coordination and site safety. A significant contribution in this regard comes from built-in software features that enable automated clash detection and rule-based checking. Another dimension frequently associated with BIM in the context of risk management is 4D modeling, which incorporates construction sequencing to help mitigate risks related to buildability, scheduling, and subcontractor coordination. Based on a review of the relevant literature, this paper first presents a list of risk factors that can potentially be analysed using data extracted from BIM models, followed by an outline of a proposed method for further analysis employing machine learning techniques.