## 1 Introduction
proposed a rule-based knowledge system to automate the code-checking process for green construction [11]. They found that the domain knowledge is usually dispersed and fragmented, so rule acquisition requires human experts from many professional fields. Therefore, the process of knowledge representation and acquisition is often a complex and time-consuming task [12]. The other is the use of machine learning methods that train classifiers of machine learning through historical data to filter out irrelevant clashes [6]. However, researchers using machine learning on complex problems usually observe that a favorable classification performance often requires a larger training dataset that allows a more complex model with more features [13]. Nevertheless, identifying and labeling a large number of clashes requires tremendous and expensive manpower; therefore, the prediction accuracy of machine learning is often insufficient before a sufficiently large number of cases are collected [7]. The industry is in urgent need of more cost-effective solutions on this issue. In the field of machine learning, many studies applied a combination of two or more sophisticated methods on specific domains and obtained better results than using individual methods. A hybrid method was developed for discretizing continuous attributes to enhance the accuracy of the naive Bayesian classifier [14]. An algorithm based on support vector machine (SVM), 2D fast Fourier transform (FFT), and hybrid fuzzy c-mean techniques was proposed to recognize and visualize the cracking incurred in the structure [15]. The hybrid ML algorithm performs better to recognize cracks with higher accuracy than the traditional SVM. Another hybrid computational model based on genetic algorithm (GA) and support vector regression (SVR) was developed to predict bridge scour depth near piers and abutments [16]. The proposed hybrid model achieved 80% more accurate error rates than those obtained using other methods, such as regression tree, chi-squared automatic interaction detector, artificial neural network, and ensemble models. These studies provide examples that demonstrate the effect of using a hybrid method. This study attempted to combine the techniques of rule-based reasoning and supervised machine learning to develop an algorithm that can automatically filter out irrelevant clashes from the BIM-generated clash detection reports. The main purpose of this study is to explore whether the hybrid method we proposed can enhance the predictive accuracy by machine learning algorithms without a large number of training cases as well as without increasing the development manpower. Unlike most rule-based systems that require an exhaustive knowledge acquisition process, the rule-based reasoning in this study is not intended to obtain accurate results, because this often requires a great amount of efforts regarding knowledge acquisition. Instead, we intend to first obtain a preliminary classification of clashes by applying a simple rule set acquired from the same experts of labeling for the subsequent machine learning process and incorporate these preliminary results in the machine learning process to see if they can help improve the prediction accuracy.