USE OF ARTIFICIAL INTELLIGENCE AND DATA ANALYTICS IN CONSTRUCTION PLANNING: DELAY PREDICTION, SCHEDULE OPTIMIZATION, AND DATA-DRIVEN RISK MANAGEMENT

Abstract

The construction industry faces persistent challenges related to schedule delays, inefficient resource allocation, and uncertainty in risk management. Traditional planning methods often struggle to address the dynamic and data-intensive nature of contemporary construction projects. In this context, the use of Artificial Intelligence and data analytics has emerged as a promising approach to enhance construction planning through predictive, adaptive, and data-driven mechanisms. This study examines the role of Artificial Intelligence in predicting schedule delays, optimizing construction timelines, and supporting proactive risk management based on historical and real-time data. By leveraging machine learning algorithms, predictive analytics, and digital information environments such as Building Information Modeling, AI-enabled planning systems improve decision-making accuracy and project performance. The findings discussed in the literature indicate that Artificial Intelligence transforms construction planning from a static and reactive process into a dynamic and predictive framework, contributing to increased efficiency, reliability, and resilience of construction projects. Despite implementation challenges related to data quality, organizational readiness, and technical expertise, the integration of AI and data analytics represents a significant advancement in modern construction management practices.