Experimental study of grout defect identification in precast column based on wavelet packet analysis

Zhang, Xuan; Zhou, Deyuan; Tang, Hesheng; Han, Xiao

doi:10.1177/1550147719889590

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Symlets function is usually denoted as symN (N = 2, 3,..., 8). Since the symN wavelet function has a strong localization capability in the time and frequency domains in the processing of digital signals [ 36 , 37 ], the sym8 wavelet function was chosen for the processing of acoustic emission signals in this experiment.…”

Section: Basic Analysis Methodsmentioning

confidence: 99%

Detection of Sleeve Grouting Compactness Based on Acoustic Emission Technology

et al. 2023

Materials

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Sleeve grouting compactness has a significant effect on the mechanical properties of rebar connections. However, a detection method for the grouting compactness inside the sleeve is still lacking. Therefore, the aim of this paper is to propose a new acoustic emission (AE) detection technology for horizontal defects and vertical defects in sleeves with different grout compactness. The basic waveform characteristic of the AE signal is analyzed. The results show that the count of acoustic emission signals decreases with the increase of grouting compactness, and the reduction rate of vertical defects is larger than that of horizontal defects. The acoustic emission waveform is further processed through wavelet packet decomposition. It is found that with the increase of grouting compactness, the composition of approximately 125–187.5 kHz in the signal is accelerated to approximately 62.5–125 kHz. The grouting compactness index is constructed by wavelet packet energy ratio. With the increase of grouting compactness, the compactness index decreases exponentially, indicating that the presence of defects can greatly reduce the attenuation of elastic wave energy. The compactness index is highly consistent with the size of defects and has little relationship with the distribution of grout materials. Experiments show that the proposed method is effective when grout defects reach a certain degree and provides a new method for sleeve grouting compactness detection.

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Section: Basic Analysis Methodsmentioning

confidence: 99%

Detection of Sleeve Grouting Compactness Based on Acoustic Emission Technology

et al. 2023

Materials

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“…When installing a frame column at a construction site, the reserved steel bar between the frame column and the floor may be uncompacted during the connection, resulting in delamination defects, which in turn affect the overall safety performance of the building [1,2]. Among the non-destructive testing techniques commonly used to evaluate the structural conditions of concrete, the impact-echo (IE) method is an effective method for detecting the internal working conditions of the concrete, the thickness of the steel protective layer, the thickness of the slab, and the bonding conditions of the lower interface.…”

Section: Introductionmentioning

confidence: 99%

Study on Impact–Echo Response of Concrete Column near the Edge

Liu

et al. 2023

Applied Sciences

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The impact–echo method is a superior method for detecting the health of concrete structures, but it has the disadvantage of significant errors when identifying structural boundaries. In order to overcome this limitation, this paper proposes a calculation method using a cross-sectional vibration mode in combination with the impact–echo method to detect concrete columns. The variation of the predominant frequency in the mid-column region and the critical boundary is studied. The influence of the edge on the detection results is analyzed. The differences in eigenvalues under different cross-section vibration modes are revealed. A quantitative method for evaluating concrete health using the impact–echo method is further established. Through field tests and finite element simulation calculations, it was verified that the eigenvalues in the fixed mode are very consistent with the predominant frequency measured near the edge region. This makes up for the defect of inaccurate measurements when the impact–echo method is used to detect the edge area of concrete members. The impact–echo method can be better used for the non-destructive testing of concrete members.

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“…Accurate extraction and selecting damage-sensitive features are prerequisites for effective monitoring methods [23]. Researchers have achieved promising results for grouted sleeve detection of single defects (without considering the time dimension) by using single conventional features, such as time of flight [24], amplitude [15,16,25], and energy decay [22,26]. However, the existing researchers have mainly focused on grouted sleeves with a single defect type and have not considered multiple defect types simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Liu,

Yi,

et al. 2023

Smart Mater. Struct.

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Structural health monitoring of grouted sleeves is one of the assembly industry’s huge challenges. In this study, a combined two-level damage detection was introduced. It comprises defect classification (healthy, rebar eccentricity, and grout leakage) and severity evaluation for early-age grouted sleeves using guided waves. Multiple features (MF) from time-, frequency-, and time-frequency domains were extracted and defined according to the diverse defects and ages of grouted sleeves to represent complex damage characteristics. Moreover, the egret swarm algorithm optimization–extreme learning machine (ESAO-ELM) models were proposed to avoid the influence of subjective experience and judgment from experts. ESAO optimized the initial random parameters (input weights and hidden layer bias) of ELM. Then, two MF-ESAO-ELM models were trained for two-level damage detection on the experimental dataset. The performance of the proposed models was comprehensively evaluated using accuracy, recall, precision, and confusion matrix. MF-ESAO-ELM performs better than ELM and PSO-ELM in accuracy. In this strategy, the defect classification model works in the outer layer to distinguish the state and types of defects of grouted sleeves (healthy, eccentric, or leakage). In comparison, the inner layer starts predicting the severity only if the defect type is leakage. MF-ESAO-ELM offers advantages in terms of accuracy, strategy, and calculation time.

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Experimental study of grout defect identification in precast column based on wavelet packet analysis

Cited by 11 publications

References 22 publications

Detection of Sleeve Grouting Compactness Based on Acoustic Emission Technology

Detection of Sleeve Grouting Compactness Based on Acoustic Emission Technology

Study on Impact–Echo Response of Concrete Column near the Edge

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

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