Clash Relevance Prediction in BIM Model Coordination Using Artificial Neural Network

As construction projects become more sophisticated, model coordination is critical to mitigating risk. Even though clash detection is highly automated in existing software systems, reviewing clashes and making corrections are still manual and repetitive workflows. Previous researchers leveraged machine learning and data mining techniques to analyze model coordination data and streamline decision-making. Nonetheless, gaps still remain in the fact that existing studies used limited datasets and mostly focused on MEP systems; additionally, no previous study identified which clash attribute combination is necessary to accurately predict clash relevance. By applying an Artificial Neural Network multilayer perceptron algorithm with different combinations of clashes' attributes in the dataset, the authors achieved a precision of over 80% in predicting clash relevance. Notably, this study contributes to the body of knowledge by identifying the BIM object attributes necessary to predicting clash relevance with high precision using all major disciplines of a construction project.