USE OF ARTIFICIAL INTELLIGENCE IN CONSTRUCTION TECHNOLOGY AND MANAGEMENT FOR LOW-COST AND ACCURATE PLANNING: AN ANALYTICAL RESEARCH PAPER

Abstract

The construction industry worldwide faces persistent challenges of cost overruns, schedule delays, and inefficient resource utilization. Traditional planning methods, while historically valuable, have proven inadequate for managing the complexity and dynamism of modern construction projects. This analytical research paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance construction planning accuracy while reducing associated costs. Drawing on empirical evidence from 250 completed construction projects in India and a systematic review of 85 peer-reviewed studies, this research develops and validates four ML models: Random Forest for cost estimation, XGBoost for schedule prediction, a hybrid neural network for resource optimization, and Gradient Boosting for risk identification. The results demonstrate that AI-enhanced planning reduces cost estimation error from 23.7% to 7.8% (a 67% improvement), improves schedule prediction accuracy by 71%, reduces resource idle time by 34.6%, and achieves risk detection rates of 87.3% compared to 52.3% for traditional methods. Planning time decreases from 14 days to 2.4 days (83% reduction), while planning costs as a percentage of project value decrease by 28.7%. A cost-benefit analysis confirms that small and medium enterprises can achieve payback within 4-8 months with initial investments of INR 3-5 lakhs. The paper concludes with an integrated framework for AI-enabled construction planning and practical implementation guidelines tailored to resource-constrained organizations. Keywords: Artificial Intelligence, Machine Learning, Construction Planning, Cost Estimation, Schedule Prediction, Resource Optimization, Risk Identification, Ensemble Methods.