## 2 Related Work
With the aim of reducing the occurrence of clashes through automated coordination, [PERSON] and [PERSON] [17] constructed a sematic schema for MEP coordination that was used to present and acquire the experience and knowledge hidden behind the coordination issues. Both [PERSON] [19] and [PERSON] [20] re-examined the BIM model via a more organized work breakdown structure (WBS) to reduce the number of irrelevant clashes. However, scholars believe that this approach undoubtedly increases the burden on the design teams [7]. Collaboration would be difficult to implement in many projects because project participants may not have a mutual contractual relationship [6; 9]. Meanwhile, some scholars consider that improving the clashes detection algorithms in BIM software can increase the accuracy of its detection, thereby reducing the number of irrelevant clashes [5]. These methods include the sphere-trees method [21], approximate polyhedra with spheres and bounding volume hierarchy [22; 23], oriented bounding boxes or OBB-trees method [24], and ray-triangle intersection algorithm [5]. These algorithms are continually improved to increase the accuracy of clash detection. Yet, as pointed out by scholars, the refined clash detection algorithms still cannot effectively reduce irrelevant clashes [10], especially those caused by human errors [7]. The third method is to directly identify and filter out irrelevant clashes in the clash detection report of BIM software. One of the popular methods of identification or diagnosis on a certain domain is rule-based systems [25]. Rule-based systems, also known as rule-based expert systems, have been commonly used in many fields such as medical, engineering, manufacturing, education, etc. since the 1980s and have been proved to be effective in pattern recognition, diagnosis, decision-making, control, planning, and so on due to the transparency of knowledge reasoning and consistency of reasoning results [12]. However, despite their advantages, rule-based systems require a considerable amount of time to acquire the knowledge that is needed for reasoning. A rule-based system was proposed to automate the code checking process for green construction. Still, the researchers found that the domain knowledge is dispersed and fragmented, and rule acquisition requires human experts from many professional fields [11]. [PERSON] et al. [7] applied the rules to identify the dependency relationship of conflicting/clashing components, thereby constructing a component-dependent network. This network can be used to identify the central components of clashes, group those repetitive clashes, and finally filter out irrelevant clashes. However, the number of irrelevant clashes being filtered out depends on the components' dependency relationships and their query algorithms, which are similar to a rule-based knowledge base, which requires a lot of effort to capture and maintain those rules. Besides, the rules developed by their study may not necessarily fit other projects. Another method that also is popular for complex problems and does not require too many efforts on knowledge acquisition is machine learning. Machine learning algorithms use computational methods to predict results directly from historical data without relying on predetermined rules or equations on domain knowledge. Besides, the algorithms adaptively improve their performance as the number of training cases increases [13; 15]. Despite its ease of identifying trends and patterns without human intervention, researchers often argued that machine learning requires a sufficiently large training dataset that allows a more complex model in order to obtain favorable results [7; 15]. In the field of identifying design clashes, [PERSON] and [PERSON] [6] used a historical dataset of 204 clashes from a three-story building and implemented six different machine learning classifiers including J48-based decision trees, random forest, Jrip, binary logistic regression, naive Bayesian, and Bayesian network to filter out irrelevant clashes.