NAVIGATING THE LANDSCAPE OF INNOVATIVE TECHNOLOGIES IN CONSTRUCTION PROJECT MANAGEMENT: A COMPREHENSIVE REVIEW

Abstract

This study was initiated with thorough investigation to determine the optimal Market Basket Analysis (MBA) algorithms for the Co-Market Intelligent Application using Association Rule Mining (ARM). An exploration evaluation was conducted to assess their suitability for integration into the Co-Market Intelligent application framework, focusing on three product categories: coconut non-food products, coconut food products, and sarakat products. Employing a model for Apriori with a minimum support of 0.2 and minimum confidence of 0.5 yielded remarkably high accuracies of 140% for coconut non-food and 72% for sarakat products while a model with minimum support of 0.2 and minimum confidence of 0.7 yielded 92% lift ratio for food products. For FP-Growth, a model with minimum support of 0.1 and minimum confidence of 0.5 demonstrated the best performance, achieving accuracies of 92% and 42% for coconut non-food and food products respectively. The findings of this study suggest that the selection of either the Apriori or FP-Growth algorithm, or a combination of both, tailored to the specific product categories, can significantly enhance the efficiency and accuracy of Market Basket Analysis in the Co-Market Intelligent Application, providing valuable insights for optimized decision-making and strategic planning in the targeted market sectors.

Keywords

• construction project management
• technology integration
• building information modeling (bim)
• artificial intelligence (ai) in construction
• emerging technologies in construction