Grouting Sleeve Fullness Detection Method Based on Microwave Radio Frequency S Parameter

Zheng, Xiaoqiu; Qi, Jiamin; Shi, Wenxuan; Wang, Chao; Yuan, Bin

doi:10.1109/piers-fall48861.2019.9021824

Cited by 4 publications

(2 citation statements)

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“…However, the embedded sensors or probes may not work correctly during the grouting process. Conventional nondestructive methods, including ultrasonic waves [15,16], impact echo [17], and microwave [18], require the detection point's constant movement to complete the whole structure's inspection. For example, Li et al [16] performed an ultrasonic pointby-point inspection on the exterior of precast concrete with sleeves and found that the ultrasonic energy increased with grouting defects.…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Li et al [22] established a piezoelectric impedance model for a prefabricated structure, where grouting defects were identified qualitatively using a statistical method showing that piezoelectric impedance analysis can effectively identify defects in the structure nodes. By comparing the actual experimental results and the simulation parameter model, a method for detecting the fullness of a grouting sleeve based on microwave radio frequency S parameters was proposed by Zheng et al [23], which provided a valuable reference for the actual engineering inspection. Based on the differences between the guided wave signals of a healthy specimen and defective specimens, Li et al [24] explored the influence of the grouting defects on the guided wave signal through experiments and simulations, where the results showed that there was a positive correlation between the proposed damage index and grouting defects.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Zhang

Tang

Zhou

et al. 2020

Sensors

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Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

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Research status and future directions of defect detection in grouted splice sleeves: A review

Liu,

Li,

Cui

2023

Construction and Building Materials

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Grouting Sleeve Fullness Detection Method Based on Microwave Radio Frequency S Parameter

Cited by 4 publications

References 0 publications

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

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

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Research status and future directions of defect detection in grouted splice sleeves: A review

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