Development of a Group Decision Making Method for Ranking Alternatives: Selection of most Preferred Data Mining Algorithm for a Construction Project

This study presents a methodology for selecting the most preferred data mining algorithm for a construction project, leveraging the Analytical Hierarchy Process (AHP) .AHP, known for its application to complex decision-making problems, is adapted in this research to fit the context of data mining.The methodology involves significant modifications, including creating a collective decision-making environment that accommodates participants from diverse backgrounds and establishing a suitable data collection method tailored for AHP.The study contributes in two key areas. First, it designs and develops the methodology, enabling AHP to be effectively used for selecting data mining algorithms in construction projects. This adaptation considers the specific needs of the domain, allowing experts from different fields to contribute without requiring a comprehensive understanding of the entire model. Second, the methodology is applied to the problem, addressing existing limitations in the literature.By incorporating all relevant performance measures and leveraging expert knowledge, it facilitates informed decision-making even in the absence of extensive model testing data.The study's data was collected from two distinct participant groups: construction practitioners and machine learning experts, focusing on their personal preferences. This approach enhances the methodology's robustness and relevance to real-world applications. The proposed methodology demonstrated its effectiveness through various applications. A preference for Artificial Neural Networks (ANN) was observed in predicting concrete compressive strength, with a 59.4% weighting due to their capability to handle large datasets and non-linear relationships. In cost estimation tasks, Support Vector Machines (SVM) outperformed other models, receiving a 64.9% preference and achieving a lower mean absolute percentage error (MAPE) of 7.06%. The AHP-based approach maintained consistency across evaluations, with consistency ratios below 0.10, confirming the reliability of group judgments in the algorithm selection process.