Review of Evolution of Studies in Equipment Productivity in Construction

Abstract

The study of equipment productivity in construction has evolved significantly over time, driven by the industry's need to improve efficiency, reduce costs, and enhance overall project performance. Equipment productivity refers to the effectiveness with which construction machinery completes assigned tasks, typically measured in terms of output per unit of input. Early research focused on mechanical efficiency, operator proficiency, and equipment reliability, while recent studies have embraced digital monitoring, automation, and artificial intelligence (AI). The integration of Internet of Things (IoT) technology and predictive analytics has further enabled real-time tracking, proactive maintenance, and optimized resource allocation. This review studies the evolution of methodologies in equipment productivity research, from observational studies to AI-driven techniques. Key factors influencing productivity include operator skills, maintenance practices, site conditions, and technological advancements. Challenges such as data inconsistencies, cost constraints, and resistance to technology adoption remain prevalent. The emerging trends such as machine learning and sustainable practices are critical areas for future research. By synthesizing past and present studies, this review provides valuable insights for researchers, industry professionals, and policymakers, aiming to improve equipment productivity in construction through innovation and data-driven decision-making.