• Smart Structures and Systems
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• v.22 no.1
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• pp.57-70
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• 2018

For the long-term structural health monitoring of civil structures, the effect of ambient temperature variation has been regarded as one of the critical issues. In this study, a principal component analysis (PCA)-based algorithm is proposed to filter out temperature effects on electromechanical impedance (EMI) monitoring of prestressed tendon anchorages. Firstly, the EMI monitoring via a piezoelectric interface device is described for prestress-loss detection in the tendon anchorage system. Secondly, the PCA-based temperature filtering algorithm tailored to the EMI monitoring of the prestressed tendon anchorage is outlined. The proposed algorithm utilizes the damage-sensitive features obtained from sub-ranges of the EMI data to establish the PCA-based filter model. Finally, the feasibility of the PCA-based algorithm is experimentally evaluated by distinguishing temperature changes from prestress-loss events in a prestressed concrete girder. The accuracy of the prestress-loss detection results is discussed with respect to the EMI features before and after the temperature filtering.

• Journal of the Korea Institute of Building Construction
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• v.12 no.6
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• pp.607-614
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• 2012

In this study, the evolution of the electro-mechanical impedance (EMI) of a piezoelectric (PZT) patch embedded in fresh cement paste was investigated to discuss the possibility of monitoring the setting process of cement-based materials using an EMI sensing technique. A tailored thin square PZT patch was embedded in cement paste before casting, and EMI signatures of the embedded patch were continuously measured from casting up to 12 hours. A standard penetration resistance test was performed to compare and correlate the evolution of EMI during the setting process. The results showed that EMI responses differ according to the age of the cement paste, and that the behavior of the EMI resonance peak has a clear correlation with the penetration resistance of the cement paste. Based on the results, it is concluded that an EMI sensing technique using embedded PZT patch can be effectively applied to monitor the setting process of cement-based materials.

• Smart Structures and Systems
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• v.11 no.4
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• pp.349-364
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• 2013

Recently, researchers in the field of structural health monitoring (SHM) have been rigorously striving to replace the conventional NDE techniques with the smart material based SHM techniques, employing smart materials such as piezoelectric materials. For instance, the electromechanical impedance (EMI) technique employing piezo-impedance (lead zirconate titanate, PZT) transducer is known for its sensitivity in detecting local damage. For practical applications, various external factors such as fluctuations of temperature and loading, affecting the effectiveness of the EMI technique ought to be understood and compensated. This paper aims at investigating the damage monitoring capability of EMI technique in the presence of axial stress with fixed boundary condition. A compensation technique using effective frequency shift (EFS) by cross-correlation analysis was incorporated to compensate the effect of loading and boundary stiffening. Experimental tests were conducted by inducing damages on lab-sized aluminium beams in the presence of tensile and compressive forces. Two types of damages, crack propagation and bolts loosening were simulated. With EFS for compensation, both cross-correlation coefficient (CC) index and reduction in peak frequency were found to be efficient in characterizing damages in the presence of varying axial loading.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.319-338
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• 2015

The electro-mechanical impedance (EMI) technique makes use of surface-bonded lead zirconate titanate (PZT) patches as impedance transducers measuring impedance variations monitored on host structural components. The present experimental work further evaluate an alternative to the conventional EMI technique which performs measurements of the variations in the output voltage of PZT transducers rather than computing electromechanical impedance (or admittance) itself. This paper further evaluates a variant of the EMI approach presented in a previous work of the present authors, suitable, for low-cost concrete structures monitoring applications making use of a credit card-sized Raspberry Pi single board computer as core hardware unit. This monitoring approach is also deployed by introducing a new damage identification index based on the ratio between the area of the 2-D error ellipse of specific probability of EMI-based measurements containment over that of the 2-D error circle of equivalent probability. Experimental results of damages occurring in concrete cubic and beam specimens are investigated under increasing loading conditions. Results illustrate that the proposed technique is an efficient approach for identification and early detection of damage in concrete structures.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.1132-1141
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• 2019

Steel-concrete-steel (SCS) sandwich structures have important advantages over conventional concrete structures, however, bond-slip between the steel plate and concrete may lead to a loss of composite action, resulting in a reduction of stiffness and fatigue life of SCS sandwich structures. Due to the inaccessibility and invisibility of the interface, the interfacial performance monitoring and debonding detection using traditional measurement methods, such as relative displacement between the steel plate and core concrete, have proved challenging. In this work, two methods using piezoelectric transducers are proposed to detect the bond-slip between steel plate and core concrete during the test of the beam. The first one is acoustic emission (AE) method, which can detect the dynamic process of bond-slip. AE signals can be detected when initial micro cracks form and indicate the damage severity, types and locations. The second is electromechanical impedance (EMI) method, which can be used to evaluate the damage due to bond-slip through comparing with the reference data in static state, even if the bond-slip is invisible and suspends. In this work, the experiment is implemented to demonstrate the bond-slip monitoring using above methods. Experimental results and further analysis show the validity and unique advantage of the proposed methods.

• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.203-217
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• 2024

Electro-mechanical impedance (EMI) technique is a low-cost structural damage detection method. It reflects structural damage through the change in admittance signal which contains the structural mechanical impedance information. The ambient temperature greatly affects the admittance signal, which hides the changes caused by structural damage and reduces the accuracy of damage identification. This study introduces a convolutional neural network to compensate for the temperature effect. The proposed method uses a framework that consists of a feature extraction network and a decoding network, and the original admittance signal with temperature information is used as the input. The output admittance signal is eliminated from the temperature effect, improving damage identification robustness. The admittance data simulated by the finite element model of the spatial grid structure is used to verify the effectiveness of the proposed method. The results show that the proposed method has advantages in identification accuracy compared with the damage index minimization method and the principal component analysis method.