Integrated electromechanical impedance technique with convolutional neural network for concrete structural damage quantification under varied temperatures

2021 ◽  
Vol 152 ◽  
pp. 107467
Author(s):  
Hedong Li ◽  
Demi Ai ◽  
Hongping Zhu ◽  
Hui Luo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Structural Damage ◽  
Electromechanical Impedance ◽  
Damage Quantification ◽  
Impedance Technique

Non Destructive Evaluation Based on Impedance Method Using Embedded PZT Sensor

2009 ◽  
Vol 79-82 ◽  
pp. 35-38 ◽  
Author(s):  
Dong Yu Xu ◽  
Xin Cheng ◽  
Shi Feng Huang ◽  
Min Hua Jiang
Keyword(s):  
Structural Damage ◽  
Crack Depth ◽  
Impedance Method ◽  
Impedance Spectra ◽  
Electromechanical Impedance ◽  
Non Destructive Evaluation ◽  
Increasing Trend ◽  
Impedance Technique ◽  
Impedance Curve ◽  
Non Destructive

The structural damage of mortar caused by simulated crack was evaluated using embedded PZT sensor combining with dynamic electromechanical impedance technique. The influence of embedded PZT sensors layout on detecting structural damage induced by the simulated cracks was also investigated. The results indicate that with increasing the simulated crack depth, the impedance real part of PZT sensors shift leftwards accompanying with the appearance of new peaks in the spectra. When more simulated cracks occur, the shift of the impedance curve becomes more obvious, and the amounts of new peaks in the impedance spectra also increase. RMSD indices of the structures with PZT sensors embedded in them with different layout can show the structural incipient damage clearly. With increasing more simulated cracks in the mortar structures, RMSD values of the structures with different PZT sensors layout become larger, under the same depth, RMSD indices of the structures with PZT sensor embedded transversely and horizontally in them show the increasing trend.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

scholarly journals Damage Identification in Plate Structures Using Sparse Regularization Based Electromechanical Impedance Technique

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7069
Author(s):  
Xingyu Fan ◽  
Jun Li
Keyword(s):  
Resonance Frequency ◽  
Structural Damage ◽  
Damage Identification ◽  
Physical Parameters ◽  
Inverse Identification ◽  
Plate Structures ◽  
Frequency Shifts ◽  
Sparse Regularization ◽  
Electromechanical Impedance ◽  
Damage Quantification

This paper proposes a novel structural damage quantification approach using a sparse regularization based electromechanical impedance (EMI) technique. Minor structural damage in plate structures by using the measurement of only a single surface bonded lead zirconate titanate piezoelectric (PZT) transducer was quantified. To overcome the limitations of using model-based EMI based methods in damage detection of complex or relatively large-scale structures, a three-dimensional finite element model for simulating the PZT–structure interaction is developed and calibrated with experimental results. Based on the sensitivities of the resonance frequency shifts of the impedance responses with respect to the physical parameters of plate structures, sparse regularization was applied to conduct the undetermined inverse identification of structural damage. The difference between the measured and analytically obtained impedance responses was calculated and used for identification. In this study, only a limited number of the resonance frequency shifts were obtained from the selected frequency range for damage identification of plate structures with numerous elements. The results demonstrate a better performance than those from the conventional Tikhonov regularization based methods in conducting inverse identification for damage quantification. Experimental studies on an aluminum plate were conducted to investigate the effectiveness and accuracy of the proposed approach. To test the robustness of the proposed approach, the identification results of a plate structure under varying temperature conditions are also presented.

Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning

2019 ◽  
Vol 23 (10) ◽  
pp. 4493-4502 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Shuang Wang ◽  
Yonglong Li ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Structural Damage ◽  
Deep Convolutional Neural Network ◽  
Structural Damage Detection

Research on Miniaturized and Portable Impedance-Measuring Device for Structural Health Monitoring

2011 ◽  
Vol 230-232 ◽  
pp. 587-591
Author(s):  
Yu Xiang Zhang ◽  
Dong Dong Wen ◽  
Hua Cheng Li ◽  
Fu Hou Xu
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Electric Impedance ◽  
Measuring Device ◽  
Line System ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Impedance Technique ◽  
On Line

Electromechanical impedance technique which based on smart material is a new method for structural damage detection, and it could be widely applied in structural health monitoring field. However, a very expensive and bulky analyzer is being used to measure the impedance, which is not practical for on-line system. Therefore, this paper developed a device that can measure the electric impedance using small modular electric components and reasonable circuit. Experiments are carried out to test the aluminum beam crack. Results indicate that the device can measure the electric impedance and detect the damage effectively. The proposed method provides a solution to miniaturize the impedance-measuring equipment and reduce the cost of measurement.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

2018 ◽  
Author(s):  
Mario A. de Oliveira ◽  
Andre V. Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

scholarly journals Piezoelectric Active Sensor Self-Diagnosis for Electromechanical Impedance Monitoring Using K-Means Clustering Analysis and Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xie Jiang ◽  
Xin Zhang ◽  
Yuxiang Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Analysis ◽  
Structural Damage ◽  
Principal Component ◽  
Piezoelectric Sensor ◽  
Sensor Faults ◽  
Ann Model ◽  
Electromechanical Impedance ◽  
Artificial Neural

Piezoelectric sensor is a crucial part of electromechanical impedance technology whose state will directly affect the effectiveness and accuracy of structural health monitoring (SHM). So carrying out sensor self-diagnosis is important and necessary. However, it is still difficult to distinguish sensor faults from structural damage as well as identify the cases and degrees of sensor faults. In the study, three characteristic indexes of admittance which have different indication intervals for damages of structure and sensors were selected from six indexes after comparison. To improve the discrimination effect, three principal components (PC) were extracted by principal component analysis (PCA). And the damage information represented by PCs was clustered by the K-means algorithm to identify the cases of damage. Then, the degrees of sensor damages were classified with the artificial neural network (ANN). The results show that the K-means clustering analysis based on admittance characteristics can accurately distinguish and identify the structural damage and four kinds of sensor damages, namely, pseudosoldering, debonding, wear, and breakage. The trained ANN model has a good recognition effect on the damage degrees and the accuracy of recognition reaches 100%. This study has a certain reference value for piezoelectric sensor self-fault identification.

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