Predictive Analytics for Construction Cost Management Using Machine Learning Techniques

Abstract

Construction projects frequently encounter considerable obstacles in achieving cost efficiency due to intricate variables, fluctuating site conditions, and unreliable forecasting techniques. This research examines the use of predictive analytics combined with sophisticated machine learning methods to enhance construction cost management. By utilizing historical project data, essential cost factors such as material costs, labor hours, changes in project scope, and scheduling delays are identified and scrutinized. Machine learning algorithms, including Random Forest, Gradient Boosting, and Support Vector Regression, are trained and assessed for their predictive precision. The suggested methodology shows a significant enhancement in the reliability of cost estimations and supports real-time, data-driven decision-making. The incorporation of intelligent systems into construction management practices presents a transformative approach to minimizing budget overruns, optimizing resource distribution, and improving project outcomes. This study offers important insights into the creation of scalable, automated cost management solutions for the construction sector.

KEYWORDS: Construction cost prediction, predictive analytics, machine learning, cost management, data-driven decision-making, project budgeting, resource optimization.