| Popis: |
Construction equipment is one of the most significant resources in large construction projects, accounting for a considerable portion of the project budget. Improving the performance of heavy machinery can increase efficiency and reduce costs. However, research on boosting the machine efficiency is limited. This study adopts a mixed review methodology (systematic review and bibliometric analysis) and evaluates emerging technologies such as digital twin, cyber physical systems, geographic information systems, global navigation satellite systems, onboard instrumentation systems, radio frequency identification, internet of things, telematics, machine learning, deep learning, and computer vision for machine productivity, and provides insights into how they can be used to improve the performance of construction equipment. This study defined three major equipment operating areas—monitoring and control, tracking and navigation, and data-driven performance optimization—classified technologies and explored how they can increase machine productivity. Other circumstantial issues affecting machine operation and loopholes in the existing innovative tools used in machine processes have also been highlighted. This study contributes to the goal of deploying digital tools and outlines future directions for the development of automated machines to optimize project efficiency. |
