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...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 Internetconnected 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.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(Nos.RS-2023-00241758,2021R1A2C2003696,and RS-2024-00334513).
文摘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 Internetconnected 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.
