Critical Review of Machine Learning Applications in Construction Cost Forecasting and Resource Optimization

This review critically examines the application of machine learning (ML) techniques in construction cost forecasting and resource optimization. The construction industry, characterized by complex project variables and uncertainty, faces persistent challenges in accurately predicting costs and effectively managing resources. The integration of ML algorithms has gained attention for its potential to enhance prediction accuracy and improve decision-making. This paper explores the various machine learning approaches employed in cost estimation, such as regression models, neural networks, decision trees, and support vector machines, assessing their strengths and limitations. Additionally, the role of ML in optimizing resource allocation, including labor, materials, and equipment, is analyzed in the context of minimizing waste and improving efficiency. Key challenges in applying these techniques to construction projects, such as data quality, model interpretability, and scalability, are discussed. The review also identifies future research directions, emphasizing the need for more robust models that integrate real-time data and adapt to dynamic project conditions. By synthesizing recent studies, this paper highlights the growing potential of ML in transforming construction management practices and offers insights into its practical applications for cost forecasting and resource optimization. The findings suggest that while ML holds promise, further advancements are needed to overcome existing barriers and fully harness its capabilities in construction projects.