Ma Yuanshan, Tian Zhenghong, Xu Xiaobin, Liu Hengrui, Li Jiajie, Fan Haoyue
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China.
State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China.
Materials (Basel). 2023 Apr 7;16(8):2958. doi: 10.3390/ma16082958.
The vibration process applied to fresh concrete is an important link in the construction process, but the lack of effective monitoring and evaluation methods results in the quality of the vibration process being difficult to control and, therefore, the structural quality of the resulting concrete structures difficult to guarantee. In this paper, according to the sensitivity of internal vibrators to vibration acceleration changes under different vibration media, the vibration signals of vibrators in air, concrete mixtures, and reinforced concrete mixtures were collected experimentally. Based on a deep learning algorithm for load recognition of rotating machinery, a multi-scale convolution neural network combined with a self-attention feature fusion mechanism (SE-MCNN) was proposed for medium attribute recognition of concrete vibrators. The model can accurately classify and identify vibrator vibration signals under different working conditions with a recognition accuracy of up to 97%. According to the classification results of the model, the continuous working times of vibrators in different media can be further statistically divided, which provides a new method for accurate quantitative evaluation of the quality of the concrete vibration process.
施加于新拌混凝土的振捣过程是施工过程中的重要环节，但由于缺乏有效的监测与评估方法，导致振捣过程质量难以控制，进而使得所形成的混凝土结构的结构质量难以保证。本文根据内部振捣器在不同振捣介质下对振动加速度变化的敏感性，通过实验采集了振捣器在空气、混凝土拌合物和钢筋混凝土拌合物中的振动信号。基于一种用于旋转机械负载识别的深度学习算法，提出了一种结合自注意力特征融合机制的多尺度卷积神经网络（SE-MCNN）用于混凝土振捣器的介质属性识别。该模型能够以高达97%的识别准确率对不同工况下振捣器的振动信号进行准确分类和识别。根据模型的分类结果，可进一步对振捣器在不同介质中的连续工作时间进行统计划分，为混凝土振捣过程质量的精确量化评估提供了一种新方法。
