## 1 Introduction
In addition, the type of machine learning method suitable for analyzing the collected data depends on the characteristics of the problem of interest. Different machine learning techniques are suitable for processing different types of data; an \"absolutely perfect\" machine learning method does not exist. Furthermore, machine learning is generally combined with other techniques to solve complicated problems and process massive amounts of data. The AHP systematizes complex problems through a hierarchical structure. It divides decision-making elements into multiple dimensions; hierarchically decomposes and structures a problem from multiple dimensions to divide a large, complex problem into multiple small subproblems; and assesses these subproblems individually. This process simplifies the decision-making process for complex problems. In contrast to the multi-criteria decision-making component of the Simple Multi-Attribute Rating Technique (SMART), which adopts a direct rating model, the AHP constructs a pairwise comparison matrix by conducting a pairwise comparison of attributes to determine the weights between criteria. The SMART only evaluates a single attribute, whereas the AHP conducts a pairwise comparison to provide decision makers with a basis for comparing and improving the validity of their models and decisions through consistency tests. In this study, the AHP was used to compute the weights of major risk factors, and an ANN was used to predict the relationships between major risks and the quality of Taiwanese construction projects. The AHP is the most complete and flexible technique for implementing qualitative risk management for construction projects [16]. An ANN is a machine learning method that can overcome the disadvantages of statistic tools, namely their inability to be used for automatic data mining. The integrated AHP-ANN model developed in this study incorporates the advantages of the AHP and an ANN. This model can be used to devise construction risk management strategies and prevent risk occurrence, thereby improving project quality. Moreover, employing multiple methods (Delphi and AHP approaches) can overcome the limitations of a single methodology [17]. Hybrid decision science methods (ANP and quality function deployment) were integrated to improve the ability to consider relationships among the critical factors and their impact [18]. Although the technique for combining the AHP and an ANN has been used in other studies, it is rarely used to predict construction risks and project quality. The present study can fill this gap and provide an alternate project management model for the construction industry. In this study, standard construction inspection data collected by the Taiwanese government were used to identify the risks influencing project quality. The integrated AHP-ANNmodel can rapidly and effectively evaluate construction risks, thereby providing alternative solutions and preventive strategies for construction problems. Since the implementation of the Public Works Construction Quality Auditing Mechanism in 1993, considerable data on construction quality auditing have been collected in Taiwan. Given that construction risks influence project management performance and construction quality, the relationship between the importance of risk factors and construction quality is worthy of examination. The developed model can serve as a powerful tool for project managers to perform systematic evaluations and to mitigate major risks in construction. Machine learning models can be predictive or descriptive. The developed model can be used to obtain knowledge from data and acquire prediction results to enable project managers to devise suitable risk mitigation strategies that can reinforce risk management strategies and effectively improve construction project quality.