## 7 Conclusions  
Previous studies stated that the clash detection reports produced by most BIM software are prone to present a huge number of clashes, many of which belong to irrelevant or ignorable clashes. Manually filtering out serious clashes from the long list in the clash detection report is both time and cost consuming; thus, automatic filtering out those irrelevant clashes by algorithms is a crucial need for the current industry. This study proposed a hybrid method that combines simple rule-based reasoning and supervised machine learning to automatically filter out irrelevant clashes from those conflicts detected by BIM software. The experiment results showed that the hybrid method can obtain a rise in the prediction accuracy of machine learning by 15%-17% for individual classifiers and 6%-8.5% for both DT and ensemble learning classifiers. The proposed method conquered the difficulties of purely developing a rule-based system considering complex relationships or merely implementing a machine learning to filter out irrelevant clashes. The former requires lots of effort for knowledge acquisition, while the number of cases collected often limits the latter's performance. It indicated that when the predictive accuracy of the conventional supervised machine learning for design clash classification is unfavorable and more training cases cannot be collected shortly, adding a feature of prediction results obtained by rule-based reasoning to the original training dataset provides an alternative to improve the prediction performance. However, the extent of improvement may depend on how well the rule-based reasoning performs. In other words, there exists a trade-off between the accuracy improvement and efforts to acquire domain knowledge when implementing the rule-based reasoning system. The ultimate goal of identifying clash types is to resolve those errors or serious clashes before the construction phase to avoid delays and costs incurred. The resolution of serious clashes is the most important task worthy of time and effort. Even though the average predictive accuracy we obtained by the hybrid method is as high as 95%, we still conducted an analysis of the misclassification of serious clashes by our method, where actual serious clashes are wrongly classified as pseudo or deliberate clashes. One of 30 tests with 98 cases from testing dataset #2, the serious clashes misclassification rate by the bagging classifier is up to 11% (out of actual serious clashes in testing cases). How to reduce and avoid the misclassification of serious clashes remains one of the important issues in future work. Since the training data used to conduct the machine learning process contains only clashes between structural and piping components, the current models and their predictive results can only be applied to those clash detection reports with a similar setting. The training data needs to include more MEP components, such as ducts, conduits, fire alarm devices, or lighting devices, to extend the practical value of this study. Several individual classifiers also have overfitting issues. More training data are required for more experiments in the future. Another issue of this study is that labeling the dataset highly relied on manual work. Future studies can consider applying unsupervised machine learning based on those labeled training data to build up larger training data. **Author Contributions**: Funding acquisition (W.Y.L. ); Conceptualization (W.Y.L. ); Methodology (W.Y.L. ); Data Collection (Y.-H.H. ); Labeling (Y.-H.H. ); Label Adjustment (W.Y.L. ); Knowledge Acquisition (Y.-H.H. ); Rule-based reasoning (W.Y.L. ); Machine Learning Process (W.Y.L. ); Validation (Y.-H.H. ); Visualization (W.Y.L. ); Writing--Original draft (W.Y.L. ); Writing--Review and editing (Y.-H.H.). **Funding**: This research was funded by the Ministry of Science and Technology, Taiwan, under grant MOST 107-2221-E-035-039.