Experimental study on micro-damage identification in reinforced concrete beam with wavelet packet and DIC method

Zhao, Binna; Dong, Li Min; Fu, Jianjun; Yang, Liqun; Xu, Wenxiang

doi:10.1016/j.conbuildmat.2019.03.175

Cited by 48 publications

(22 citation statements)

References 17 publications

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“…2 Grout defects in the precast concrete frame structure were successfully identified based on the proposed multiple-variable regression model, with results showing that the total damage indicators of nodes near defects were greater than those of other nodes. 3 The total damage indicator displayed robustness against different levels of noise with SNRs of 1 dB, 5 dB, and 10 dB inputted, but attention still needs to paid to avoid the significant environmental noise that was present during the experiment to obtain good identification results. 4 The proposed method has the limitations that the damage indicator was calculated based on two working conditions, where the structural design and boundary conditions were the same.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Due to overloading, environmental corrosion, material aging, operating loads, fatigue, and other unexpected events, engineering structures inevitably sustain different kinds of defects that can lead to structural failure. Therefore, it is necessary to develop a defect identification method to guarantee structural safety [1][2][3][4][5].…”

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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“…Compared with WT, its advantage is the complete level-by-level decomposition of nonstationary signals both in high-frequency regions and low-frequency regions [40]. WPT has been widely used to analyze non-stationary vibrational and ultrasonic signals in the field of structural health monitoring [25,31,[41][42][43]. Here, WPT was introduced to decompose the received stress wave signals into different frequency scales and to explore the energies of different components.…”

Section: Wptmentioning

confidence: 99%

Grouting Quality Evaluation in Post-Tensioning Tendon Ducts Using Wavelet Packet Transform and Bayes Classifier

Sun

et al. 2019

Sensors

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The grouting quality of tendon ducts is very important for post-tensioning technology in order to protect the prestressing reinforcement from environmental corrosion and to make a smooth stress distribution. Unfortunately, various grouting defects occur in practice, and there is no efficient method to evaluate grouting compactness yet. In this study, a method based on wavelet packet transform (WPT) and Bayes classifier was proposed to evaluate grouting conditions using stress waves generated and received by piezoelectric transducers. Six typical grouting conditions with both partial grouting and cavity defects of different dimensions were experimentally investigated. The WPT was applied to explore the energy of received stress waves at multi-scales. After that, the Bayes classifier was employed to identify the grouting conditions, by taking the traditionally used total energy and the proposed energy vector of WPT components as input, respectively. The experimental results demonstrated that the Bayes classifier input with the energy vector could identify different grouting conditions more accurately. The proposed method has the potential to be applied at key spots of post-tensioning tendon ducts in practice.

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“…In recent years, researchers around the world have carried out studies regarding the identification of RC structural damages and have proposed a variety of identification methods [4][5][6]. ese methods mainly include static, dynamic, and dynamic-static combination methods.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study of the Feasibility of Identifying the Impact Damages of Reinforced Concrete Piers Using a Modal Frequency Method

Zhou

Zhang

Gao

et al. 2020

Advances in Civil Engineering

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In this research study, horizontal impact tests were carried out on five reduced scale pier models using China’s most advanced multifunctional ultrahigh heavy drop hammer impact test system and DHDAS dynamic signal acquisition and analysis system. Due to the fact that the traditional measurement method can only be used for local measurement damage, and the volatility is high, this paper proposes a test method for the modal frequency identification of the overall damage of reinforced concrete pier and applies the ultrasonic damage measurement method to verify the results. The tests analyzed the modal frequencies and ultrasonic velocity identifications for the purpose of evaluating the impact damages of bridge piers, as well as the relationship between them. The results showed that the modal frequencies were consistent with the ultrasonic waves in identifying and evaluating the damages to the piers. Also, the modal frequency damage factors were determined to be functions of the ultrasonic wave velocity damage factors. Therefore, the results of this study confirmed that it was feasible to characterize the impact damages of piers using a modal frequency method.

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Experimental study on micro-damage identification in reinforced concrete beam with wavelet packet and DIC method

Cited by 48 publications

References 17 publications

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

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

Grouting Quality Evaluation in Post-Tensioning Tendon Ducts Using Wavelet Packet Transform and Bayes Classifier

An Experimental Study of the Feasibility of Identifying the Impact Damages of Reinforced Concrete Piers Using a Modal Frequency Method

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