Construction equipment monitoring has shifted from manual logbooks and early telematics toward intelligent digital systems, driven by the need to reduce delays in obtaining equipment data and improve decision-making on construction sites. This study examines research on construction equipment monitoring and tracking published between 2000 and 2026 to identify the evolution, major themes, and emerging directions in the field. A scientometric and bibliometric analysis was conducted on 1093 bibliographic records retrieved from Scopus and Web of Science on 20 May 2026. The annual publication trend was evaluated through 2025, the last complete publication year, while records indexed in 2026 were retained for the remaining corpus-level analyses. The datasets were preprocessed and analyzed using Bibliometrix, VOSviewer, and CiteSpace to examine publication trends, keyword networks, collaboration patterns, citation structures, and research clusters. The keyword network was interpreted through six major thematic clusters, which were synthesized into four broader knowledge streams covering operations and sensing, AI-based perception, safety monitoring, and BIM/digital-twin integration. The results show a substantial increase in the representation of AI- and perception-related research across the later publication periods, reflecting a transition from basic sensor-based approaches toward more intelligent and connected site systems. The study identifies leading contributors and comparatively underdeveloped research priorities, particularly real-time idle-state detection, multimodal data fusion, multi-site validation, and the integration of monitoring outputs with BIM and digital-twin decision-support environments.

Effective construction-equipment monitoring requires persistent equipment-level information linking visual observations to utilization and planning decisions. This study develops and evaluates an identity-centered framework integrating ten-class YOLOv8m detection, ByteTrack tracking, AprilTag-assisted Global ID reconciliation, scale-normalized motion-state estimation, utilization accounting, and rule-based reallocation support. The selected detector achieved 90.80% micro-F1 on the held-out ConstructionXC7C benchmark (757 images, eight annotated categories). In a matched comparison, selected-checkpoint micro-F1 reached 92.52%, exceeding the original checkpoint by 16.11 percentage points. Across 6000 experimental video frames, 5757 of 5992 annotated excavator observations were matched, with 99.81% adjacent camera–local ID consistency. A controlled 192-pair simulated-transition study associated all 144 same-object pairs using ideal deterministic identity tokens, compared with 99 using a tuned appearance-only baseline; regression tests verified preservation of accumulated equipment histories. In a separate matched analysis of 1040 observations, temporal activity modeling improved idle-state discrimination and reduced video-level sampled-utilization mean absolute error by 41.92 percentage points, achieving an error of 15.11 percentage points. Controlled verification further demonstrated state-service functionality and reviewable zone-reallocation advisories. These findings provide quantitative evidence of component-level performance and traceable monitoring-to-decision functionality, establishing a technical foundation for subsequent construction-site evaluation.