Void Defect Identification in Concrete-Filled Steel Tubes Using Percussion Acoustic Signals and a One-Dimensional Convolutional Neural Network

Liang Xuewen; Chen Tianyu; Tian Mingdong; Liao Feiyu; Yu Kunming; Luo Yongpeng

doi:10.13206/j.gjgS26051701

Volume 41 Issue 6

Jun. 2026

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Liang Xuewen, Chen Tianyu, Tian Mingdong, Liao Feiyu, Yu Kunming, Luo Yongpeng. Void Defect Identification in Concrete-Filled Steel Tubes Using Percussion Acoustic Signals and a One-Dimensional Convolutional Neural Network[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(6): 30-35. doi: 10.13206/j.gjgS26051701

Citation:

Liang Xuewen, Chen Tianyu, Tian Mingdong, Liao Feiyu, Yu Kunming, Luo Yongpeng. Void Defect Identification in Concrete-Filled Steel Tubes Using Percussion Acoustic Signals and a One-Dimensional Convolutional Neural Network[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(6): 30-35. doi: 10.13206/j.gjgS26051701

Citation:

Liang Xuewen, Chen Tianyu, Tian Mingdong, Liao Feiyu, Yu Kunming, Luo Yongpeng. Void Defect Identification in Concrete-Filled Steel Tubes Using Percussion Acoustic Signals and a One-Dimensional Convolutional Neural Network[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(6): 30-35. doi: 10.13206/j.gjgS26051701

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Void Defect Identification in Concrete-Filled Steel Tubes Using Percussion Acoustic Signals and a One-Dimensional Convolutional Neural Network

doi: 10.13206/j.gjgS26051701

1. Guangdong Jianke Architectural Design Institute Company, Guangzhou 510145, China;
2. School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China;
3. Nanping Highway Development Center, Nanping 350008, China

Received Date: 2026-05-17
Available Online: 2026-07-06

Abstract

Abstract

Influenced by multiple factors such as construction technology， material shrinkage and creep， and service environment， concrete-filled steel tubes are prone to void defects， which lead to a decrease in bearing capacity and pose a threat to long-term service safety. Traditional manual percussion detection is convenient and fast. However， this method relies on the subjective experience of inspectors and is prone to misjudgment and missed detection. Therefore， a method for identifying void defects in concrete-filled steel tubes using percussion sound signals and a one-dimensional convolutional neural network is proposed. A one-dimensional convolutional neural network is used to construct a model for identifying void defects in concrete-filled steel tubes. With the percussion sound pressure signal as input， defect features in the sound signal are automatically extracted through multi-layer convolution operations for defect identification. A dataset covering the influence of factors such as void position and void depth was constructed using experimental and finite-element data， and was used to verify the feasibility and reliability of the proposed method. The results showed that the average identification accuracy of the constructed defect identification model on the test set was 97.44%. Furthermore， the model offered the advantages of short training time and fast convergence.
- concrete-filled steel tube,
- void defect,
- acoustic signal,
- one-dimensional convolutional neural network

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References(15)

References

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