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Research on Response Parameters and Classification Identification Method of Concrete Vibration Process.
Yuanshan Ma1, Zhenghong Tian1,2, Xiaobin Xu3
1College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China.
Materials (Basel, Switzerland)
|April 28, 2023
Summary
This study introduces a deep learning model to monitor concrete vibrator performance. The SE-MCNN accurately identifies vibrator conditions, enabling better control over concrete vibration quality and structural integrity.
Area of Science:
- Civil Engineering
- Materials Science
- Artificial Intelligence
Background:
- Effective monitoring of concrete vibration is crucial for structural quality.
- Current methods lack precision in evaluating the vibration process.
- Controlling vibration quality ensures the integrity of concrete structures.
Purpose of the Study:
- To develop a novel method for monitoring concrete vibrator performance.
- To accurately classify vibrator working conditions in different media.
- To enable quantitative evaluation of the concrete vibration process quality.
Main Methods:
- Experimental collection of vibrator signals in air, concrete, and reinforced concrete.
- Application of a deep learning algorithm for load recognition.
- Development of a multi-scale convolution neural network with self-attention (SE-MCNN).
Main Results:
- The SE-MCNN model achieved up to 97% accuracy in classifying vibrator signals.
- The model successfully identified vibrator vibration under diverse working conditions.
- Classification results allowed for statistical division of continuous working times.
Conclusions:
- The proposed SE-MCNN offers a new, accurate method for evaluating concrete vibration quality.
- This approach enhances the control and assurance of structural integrity in concrete construction.
- The study provides a foundation for intelligent monitoring in concrete engineering.
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