Real-Time Monitoring and Platform Design for Concrete Compactness Using Long Short-Term Memory Networks
Journal Title: Journal of Civil and Hydraulic Engineering - Year 2024, Vol 2, Issue 4
Abstract
To address the complexities and inaccuracies associated with traditional methods of concrete compactness monitoring, in this paper, a real-time monitoring approach based on long short-term memory (LSTM) networks has been developed. Traditional methods often involve cumbersome data processing and yield large errors, especially in complex environments, in contrast, the proposed method leverages the LSTM network's ability to process time-series data, enhancing accuracy in detecting compactness defects within concrete structures, and the ultrasonic wave velocity through concrete under standard conditions has been set as a baseline value. The platform can visualize the curve of ultrasonic propagation speed in the monitored concrete over time, allowing for a direct comparison with the baseline to assess the extent and location of potential defects. The degree of deviation from the baseline indicates the compactness and defect severity, facilitating more accurate monitoring. Additionally, a user-friendly monitoring platform interface has been designed using Mock Plus, enabling rapid prototyping and optimization for enhanced data visualization and user interaction, this design allows for effective real-time monitoring, data processing, and user engagement. By integrating advanced machine learning techniques with intuitive platform design, the proposed method offers a significant improvement in monitoring concrete compactness, potentially benefiting both research and practical applications in structural health monitoring.
Authors and Affiliations
Kenan Zhao, Jie Chen, Yanke Shi, Kai Sun
Keywords
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- EP ID EP752504
- DOI https://doi.org/10.56578/jche020404
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