Intelligent Fleet Monitoring System for Productivity Management of Earthwork Equipment

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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Institutions

Publication Details

Journal
Applied Sciences
Published
2026-01-21
DOI
https://doi.org/10.3390/app16021115
Citations
1
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
25.98

Funders

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article

Intelligent Fleet Monitoring System for Productivity Management of Earthwork Equipment

Abubakar Sharafat, Sung-Hoon Yoo, S Lee, Jongwon Seo
1 citations
Applied Sciences
BIM and Construction Integration
25.98
article

Intelligent Fleet Monitoring System for Productivity Management of Earthwork Equipment

Abubakar Sharafat, Sung-Hoon Yoo, S Lee, Jongwon Seo
article en
1 citations

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.

Applied SciencesVol. 16(2)
Hanyang University (KR)
National Research Foundation, Ministry of Trade, Industry and Energy, Ministry of Land, Infrastructure and Transport, National Research Foundation of Korea, Korea Agency for Infrastructure Technology Advancement, Ministry of Science and ICT, South Korea
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
BIM and Construction Integration
25.98
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