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Intelligent Fleet Monitoring System for Productivity Management of Earthwork Equipmentopen access

Authors
Lee, SoominSharafat, AbubakarYoo, Sung-hoonSeo, Jongwon
Issue Date
Jan-2026
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Keywords
earthwork; fleet telematics; GPS; heavy construction equipment; productivity assessment; productivity management
Citation
Applied Sciences, v.16, no.2, pp 1 - 36
Pages
36
Indexed
SCIE
SCOPUS
Journal Title
Applied Sciences
Volume
16
Number
2
Start Page
1
End Page
36
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/210898
DOI
10.3390/app16021115
ISSN
2076-3417
2076-3417
Abstract
Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations.
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