Crack detection and leakage monitoring on reinforced concrete pipe

Feng, Qian; Kong, Qingzhao; Huo, Linsheng; Song, Gangbing

doi:10.1088/0964-1726/24/11/115020

Cited by 115 publications

(82 citation statements)

References 18 publications

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“…Structural health monitoring for concrete structures has attracted a lot of attention [3,4,5]. A wide variety of techniques have been reported in the literature that may be suitably employed for monitoring and locating corrosion nondestructively.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Concrete Deterioration Due to Reinforcement Corrosion by Integrating Acoustic Emission and FBG Strain Measurements

et al. 2017

Sensors

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127

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Corrosion of concrete reinforcement members has been recognized as a predominant structural deterioration mechanism for steel reinforced concrete structures. Many corrosion detection techniques have been developed for reinforced concrete structures, but a dependable one is more than desired. Acoustic emission technique and fiber optic sensing have emerged as new tools in the field of structural health monitoring. In this paper, we present the results of an experimental investigation on corrosion monitoring of a steel reinforced mortar block through combined acoustic emission and fiber Bragg grating strain measurement. Constant current was applied to the mortar block in order to induce accelerated corrosion. The monitoring process has two aspects: corrosion initiation and crack propagation. Propagation of cracks can be captured through corresponding acoustic emission whereas the mortar expansion due to the generation of corrosion products will be monitored by fiber Bragg grating strain sensors. The results demonstrate that the acoustic emission sources comes from three different types, namely, evolution of hydrogen bubbles, generation of corrosion products and crack propagation. Their corresponding properties are also discussed. The results also show a good correlation between acoustic emission activity and expansive strain measured on the specimen surface.

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Section: Introductionmentioning

confidence: 99%

Monitoring Concrete Deterioration Due to Reinforcement Corrosion by Integrating Acoustic Emission and FBG Strain Measurements

et al. 2017

Sensors

Self Cite

127

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“…The outstanding feature of wavelet packet analysis is that it enables the inspection of relatively narrow frequency bands during a relatively short time window. [23][24][25] To obtain the leveled hoop strain variation energy, the sensor signal S is decomposed by means of an n-level wavelet packet decomposition into 2 n signal segments {X 1 , X 2 , … X 2 n }, 26,27 where X i is the time-series signal after the wavelet packet decomposition and i is the frequency band index (i = 1, 2, …, 2 n ). Accordingly, the energy corresponding to the hoop strain sensor signal S can be defined as the 1-norm of the above energy vector as follows:…”

Section: Feature Extraction For Leakage Identificationmentioning

confidence: 99%

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Jia

Ren

et al. 2018

Struct Control Health Monit

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Summary A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.

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“…By using SHM technologies, the real-time damage detection of various structures, including concrete structures [2][3][4][5][6], pipeline structures [7][8][9][10][11][12], and steel structures [13][14][15][16][17][18][19], can be investigated in order to provide early warning and hopefully to avoid accidents. In the aerospace industry, several application areas have garnered significant interests.…”

Section: Introductionmentioning

confidence: 99%

Experimental Damage Identification of a Model Reticulated Shell

Hao

et al. 2017

Applied Sciences

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Abstract:The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg-Marquardt algorithm (L-M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

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Crack detection and leakage monitoring on reinforced concrete pipe

Cited by 115 publications

References 18 publications

Monitoring Concrete Deterioration Due to Reinforcement Corrosion by Integrating Acoustic Emission and FBG Strain Measurements

Monitoring Concrete Deterioration Due to Reinforcement Corrosion by Integrating Acoustic Emission and FBG Strain Measurements

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Experimental Damage Identification of a Model Reticulated Shell

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