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<p>Earthwork productivity analysis is essential for successful construction projects. If productivity analysis results can be accessed anytime and anywhere, then project management can be performed more efficiently. To this end, this paper proposes an earthwork productivity monitoring framework via a real-time scene updating multi-vision platform. The framework consists of four main processes: 1) site-optimized database development; 2) real-time monitoring model updating; 3) multi-vision productivity monitoring; and 4) web-based monitoring platform for Internet-connected devices. The experimental results demonstrated satisfactory performance, with an average macro F1-score of 87.3% for continuous site-specific monitoring, an average accuracy of 86.2% for activity recognition, and the successful operation of multi-vision productivity monitoring through a web-based platform in real time. The findings can contribute to supporting site managers to understand real-time earthmoving operations while achieving better construction project and information management.</p>

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Web-based multi-vision platform for earthwork productivity on construction sites using real-time model updating

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月26日

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  • Jeongbin Hwang,
  • Insoo Jeong,
  • Junghoon Kim,
  • Seokho Chi

摘要

Earthwork productivity analysis is essential for successful construction projects. If productivity analysis results can be accessed anytime and anywhere, then project management can be performed more efficiently. To this end, this paper proposes an earthwork productivity monitoring framework via a real-time scene updating multi-vision platform. The framework consists of four main processes: 1) site-optimized database development; 2) real-time monitoring model updating; 3) multi-vision productivity monitoring; and 4) web-based monitoring platform for Internet-connected devices. The experimental results demonstrated satisfactory performance, with an average macro F1-score of 87.3% for continuous site-specific monitoring, an average accuracy of 86.2% for activity recognition, and the successful operation of multi-vision productivity monitoring through a web-based platform in real time. The findings can contribute to supporting site managers to understand real-time earthmoving operations while achieving better construction project and information management.