Moving Toward Automated Construction Management: An Automated Construction Worker Efficiency Evaluation System
In the Architecture, Engineering, and Construction (AEC) industry, traditional labor efficiency evaluation methods have limitations, while computer vision technology shows great potential. This study aims to develop a potential automated construction efficiency evaluation framework. We propose a met...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2479 |
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| Summary: | In the Architecture, Engineering, and Construction (AEC) industry, traditional labor efficiency evaluation methods have limitations, while computer vision technology shows great potential. This study aims to develop a potential automated construction efficiency evaluation framework. We propose a method that integrates keypoint processing and extraction using the BlazePose model from MediaPipe, action classification with a Long Short-Term Memory (LSTM) network, and construction object recognition with the YOLO algorithm. A new model framework for action recognition and work hour statistics is introduced, and a specific construction scene dataset is developed under controlled experimental conditions. The experimental results on this dataset show that the worker action recognition accuracy can reach 82.23%, and the average accuracy of the classification model based on the confusion matrix is 81.67%. This research makes contributions in terms of innovative methodology, a new model framework, and a comprehensive dataset, which may have potential implications for enhancing construction efficiency, supporting cost-saving strategies, and providing decision support in the future. However, this study represents an initial validation under limited conditions, and it also has limitations such as its dependence on well-lit environments and high computational requirements. Future research should focus on addressing these limitations and further validating the approach in diverse and practical construction scenarios. |
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| ISSN: | 2075-5309 |