Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

Abstract

Construction equipment is one of the most significant resources in every large construction project, accounting for a considerable portion of the project budget. Improving heavy machinery performance can increase efficiency and reduce costs. However, research on boosting machine efficiency is limited. This study adopts mix review methodology (systematic review and bibliometric analysis) and evaluates emerging technologies like Digital twin, Cyber physical systems, Geo-graphic information systems, Global navigation satellite system, Onboard instrumentation system, 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. The article defined three major equipment operating areas-monitoring and control, tracking and navigation, and data-driven performance optimization-classified technologies respectively, and explored how they can increase machine productivity. Other circumstantial issues affecting machine operation as well as loopholes in existing innovative tools used in machine processes were also highlighted. This study contributes to the goals of deploying digital tools and outlines future directions for the development of automated machines to optimize project efficiency.

Scientometric Insights of Digital Twins in Industry 4.0

“…The primary citing article within this cluster is authored by Kinsner, W (2022.0-JAN) [47]. The most cited members within this cluster are from Germany (44), the United Kingdom (41), and Mexico (13). The second largest cluster, labeled #1, includes 14 members with a silhouette value of 0.772 and is characterized as a "comprehensive review" by LLR.…”

“…Cluster #2, identified as a "hybrid approach" by LLR, is the third largest cluster with 46 members and a silhouette value of 0.601. The primary citing article is authored by Khan, Au (2022.0-JAN) [44]. The most cited topics within this cluster are "e-learning" (72), "learning systems" (38), and "forecasting" (21).…”