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image of Recent Techniques for Automated Construction Site Monitoring and Management

Abstract

This paper explores the advancements in construction site monitoring and management facilitated by Convolutional Neural Networks (CNNs). Through a comprehensive review of existing literature and emerging trends, the study identifies several key findings and contributions. Firstly, Techniques have greatly improved safety measures on construction sites by automatically detecting potential hazards in real-time, thereby reducing the risk of accidents and injuries. Secondly, these techniques offer a more efficient alternative to traditional progress monitoring methods by automating the analysis of construction site images, enabling timely project delivery. Thirdly, CNNs play a crucial role in quality control by detecting defects and deviations from design specifications during the construction process, thus avoiding costly rework. Finally, they optimize resource allocation by monitoring equipment usage and condition, enabling predictive maintenance and minimizing downtime. While CNN-based solutions offer transformative potential, challenges such as data scarcity and model interpretability need to be addressed to fully realize their benefits in the construction industry.

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2025-04-16
2025-08-16

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/content/journals/cms/10.2174/0126661454355422250331021930
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Recent Techniques for Automated Construction Site Monitoring and Management
Bentham Science Publishers ; https://doi.org/10.2174/0126661454355422250331021930
/content/journals/cms/10.2174/0126661454355422250331021930
/content/journals/cms/10.2174/0126661454355422250331021930
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  • Article Type:     Review Article
Keywords: construction ; neural networks ; safety measures ; progress monitoring ; Construction site monitoring ; convolutional neural networks (CNNs)

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