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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Grouting compactness evaluation in post-tensioning tendon ducts based on deep learning

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基于深度学习的预应力管道灌浆密实度检测

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Sept.2023, Vol.46 No.9: 1210-1216

DOI: 10.3969/j.issn.1003-5060.2023.09.010

Authors

YAN Laizhang

(China Railway 24th Bureau Group Anhui Engineering Co., Ltd., Hefei 230011, China)

Abstract and Keywords

Abstract: To ensure the efficiency of post-tensioning tendons in bridge structures during the long-term service lifetime, it is critical to evaluate the grouting quality in post-tensioning tendon ducts. Due to the concealed positions and complex geometries, it is not an easy task to accurately detect the grouting defects in the tendon ducts. To this end, this study proposes a grouting compactness evaluation method based on deep learning of ultrasonic signals. Firstly, piezoelectric ceramic (PZT) transducers are embedded to generate and collect ultrasonic signals propagating along the tendon ducts. Then, wavelet packet transform is applied to obtaining the multi-scale time-frequency features of ultrasonic signals for different grouting cases. Finally, a convolutional neural network deep learning model is established to extract grouting defects related time-frequency features of the signals and to classify different grouting cases. Through finite element numerical simulation of the post-tensioning tendon ducts, the distribution of wave field in different grouting cases with partial grouting and cavity defects was investigated. The simulation results show that the proposed method is feasible and efficient for grouting compactness evaluation in post-tensioning tendon ducts.

Keywords: post-tensioning tendon duct; grouting compactness; ultrasonic signal; wavelet packet transform; deep learning

作者信息

严来章

（中铁二十四局集团安徽工程有限公司，安徽合肥 230011）

摘要和关键词

摘要: 为了确保预应力钢绞线在桥梁中长期服役期间发挥作用,测定管道的灌浆质量至关重要。文章针对灌浆质量隐蔽难测的问题,提出基于超声信号深度学习的密实度检测方法。首先通过预埋锆钛酸铅压电陶瓷(PZT)换能器实现沿管道纵向的超声波激励和接收;然后利用小波包变换算法对接收到的超声信号进行分析,得到不同灌浆工况下超声信号的多尺度时频特征;最后建立卷积神经网络深度学习模型自动提取信号的时频能量特征,进行不同灌浆工况的分类评估。通过预应力管道的有限元数值模拟,探究不同程度的部分灌浆和空洞缺陷工况的超声波场,验证该文所提出的预应力管道灌浆密实度检测方法的可行性和有效性。

关键词: 预应力管道；灌浆密实度；超声信号；小波包变换；深度学习

Funding

国家自然科学基金资助项目（51708164）；中铁二十四局集团有限公司科技研发资助项目（2019-02）

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