Electro-mechanical Impedance Signatures Research Articles

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

Identification of structural parameters in a prestressed concrete beam under chloride-induced corrosion using embedded and smart-probe-based piezo sensors

In prestressed concrete (PSC) structures, corrosion in prestressing wire is one of the main problems affecting its service life. Therefore, structural health monitoring (SHM) is essential for performance evaluation and safety maintenance to prevent and reduce engineering accidents. In SHM, corrosion monitoring using the piezo sensor-based electro-mechanical impedance (EMI) technique has become a research hotspot. This paper presents the effectiveness of embedded and smart-probe-based piezo sensors (SPPS) for identifying structural parameters such as stiffness, mass, and damping in a prestressed concrete beam subjected to chloride-induced corrosion using electro-mechanical impedance (EMI) technique. The accelerated corrosion tests were conducted on a PSC beam in which SPPS was indirectly bonded, and an embedded piezo sensor (EPS) was attached to the prestressing wire inside the beam to monitor the variation in the EMI signature during the exposure of corrosion. Further, a physical model was developed in the form of spring, mass, and damper combinations to identify the deterioration in terms of structural parameters during exposure to corrosion. Based on the experimental results, it is found that EPS is effective in identifying the corrosion initiation phase, while SPPS is for the corrosion propagation and cracking phase. In terms of percentage loss, the identified stiffness loss from SPPS and EPS in the propagation phase of corrosion is about 61.56 % and 5 %, respectively.

Automated estimation of early-age concrete compressive strength using EMI signature-driven deep learning technique

Concrete is a vital construction material for civil infrastructure, whose early-age compressive strength estimation plays a critical role in determining the proper time for formwork removal and the readiness of the structures for service. Electromechanical impedance (EMI) technique has been proven to be a non-destructive and effective method for monitoring concrete early strength gain. Nonetheless, such a technique heavily relies on effective frequency band selection and statistical parameter-based analysis, which may prevent the technique from a real-time operation. This study proposes an EMI signature-driven deep learning (DL) technique to enable the automated estimation of early-age concrete compressive strength. A novel DL framework called multi-scale learning residual denoising network (MLRD-Net) was developed to autonomously learn sensitive features from raw EMI signatures and ultimately estimate the compressive strength. The EMI signatures were collected during the strength development process of concrete specimens produced from two different mix designs based on strength. Two independent databases were constructed from recorded EMI signatures to test the effectiveness of the MLRD-Net. The results demonstrated the excellent performance of the proposed model with coefficients of determination exceeding 0.99 for both databases. In addition, the MLRD-Net showed superior performances over other methods in terms of prediction accuracy, noise resistance and fitness capacities, indicating its great potential for real-time and accurate estimation of the strength gain of early-age concrete.

EMI-based monitoring of prestressed concrete beam under chloride-induced corrosion using an embedded piezo sensor

The service life and durability of the prestressed concrete (PC) structures mainly depend on the corrosion resistance of the prestressing strands, wires, or tendons. Once corrosion occurs in the strands, identifying the location and quantifying the phases of corrosion is a very challenging task. Thus, this paper presents a novel way to monitor the corrosion process and identify the deterioration in a PC beam using an embedded piezo sensor (EPS) based on the electro-mechanical impedance (EMI) technique. The accelerated corrosion tests were conducted on a PC beam in which EPS was attached to the prestressing wire inside the beam to monitor the changes in the EMI signature during the corrosion progression. In addition, statistical damage indices such as root mean square deviation is identified to quantify the damage due to corrosion. Further, this study identifies the deterioration in mechanical changes such as stiffness, mass, and damping by extracting the equivalent structural parameters from the raw EMI signatures. Based on qualitative and quantitative results, it is found that the EPS effectively senses the changes at different time intervals during the corrosion process and identifies the damage of corrosion. Also, the equivalent stiffness parameter identified from raw EMI signatures is realistically measuring the material deterioration under corrosion and identifying the corrosion phases. Hence, it is concluded that the EMI technique based on EPS in structural health monitoring of PC beam can accurately assess the deterioration under the effect of corrosion.

A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers.

Effective damage identification is paramount to evaluating safety conditions and preventing catastrophic failures of concrete structures. Although various methods have been introduced in the literature, developing robust and reliable structural health monitoring (SHM) procedures remains an open research challenge. This study proposes a new approach utilizing a 1-D convolution neural network to identify the formation of cracks from the raw electromechanical impedance (EMI) signature of externally bonded piezoelectric lead zirconate titanate (PZT) transducers. Externally bonded PZT transducers were used to determine the EMI signature of fiber-reinforced concrete specimens subjected to monotonous and repeatable compression loading. A leave-one-specimen-out cross-validation scenario was adopted for the proposed SHM approach for a stricter and more realistic validation procedure. The experimental study and the obtained results clearly demonstrate the capacity of the introduced approach to provide autonomous and reliable damage identification in a PZT-enabled SHM system, with a mean accuracy of 95.24% and a standard deviation of 5.64%.

Solar cell micro crack detection technique using electromechanical impedance and finite element analysis

Electromechanical impedance (EMI) is one of the modern techniques employed in the field of structural health monitoring (SHM) and has witnessed a great expansion in many domains due to its ability to ensure continuous monitoring and high reliability with a lower cost. Hence, the purpose of this study is to investigate the effectiveness of EMI in monitoring and detecting pre-existing cracks in the silicon layer of the photovoltaic (PV) solar cells. Indeed, a detailed finite element analysis (FEA) on the effect of piezoelectric lead zirconate titanate (PZT) patch shape on the EMI of crystalline-silicon (c-Si) layer of the PV solar cells were performed. The typical structure (c-Si + PZT) of different scenarios were stimulated by giving a lower voltage and high frequency as input data to extract the EMI signature as an explainable output result. Different models were investigated based on PZT patch types and crack position in the silicon layer. The results indicate that the EMI technique is effective in detecting crack, even in its incipient stage. The analysis suggests using a higher frequency range (350–650 kHz) for characterizing damage with an invisible depth due to the higher sensitivity of the EMI technique in this specific frequency.

Temperature Compensation for Reusable Piezo Configuration for Condition Monitoring of Metallic Structures: EMI Approach.

This paper presents a novel algorithm for compensating the changes in conductance signatures of a piezo sensor due to the temperature variation employed in condition monitoring using the electro-mechanical impedance (EMI) approach. It is crucial to consider the changes in an EMI signature due to temperature before using it for comparison with the baseline signature. The shifts in the signature due to temperature can be misinterpreted as damages to the structure, which might also result in a false alarm. In the present study, the compensation values are calculated based on experiments on piezo sensors both in a free boundary condition and in a bonded condition on a metallic host structure. The values were further validated experimentally for damage detection on a large 2D steel plate structure. The variation in first natural frequency values for the unbonded piezo sensor at different temperatures has been used to develop the compensation algorithms. Whereas, in the case of the bonded sensor, the shift in structural peaks has been used. The developed compensation relations showed promising results in damage detection. Lastly, a finite element-based study has also been performed, supporting the experimental findings. The outcome of this study will aid in the compensation of the signatures in the structure due to temperature variation in the conductance signature.

Monitoring the wear of turning tools with the electromechanical impedance technique

In this study, Inconel 718 and Ti6Al4V alloys were machined using ceramic (WG 300), and carbide (G20M Grade) inserts, respectively. The focused application was grooving. The tool wear was observed with traditional methods and with a new approach, the electromechanical impedance (EMI) technique. First, different machining durations were determined to obtain a different level of worn tools, and the wear amounts of tools were measured using a specialized optical microscope. Then, the electromechanical impedance signature of the new and worn tools was measured with two different piezoelectric sensors. It was understood that the wear level of the tools could also be determined with the EMI method. In order to evaluate the damage quantitatively, RMSD and CCD Damage Indexes (DI) were used. With this proposed method, it was thought that the tool wear level and the remaining machining time from the tool could be predicted with calculated DI. Additionally, a second study was conducted on irregularly worn carbide tools; it was understood that sensors location has a significant role in determining the wear zone; for the current setup, the flank wear could be detected more effectively with the EMI method than the wear on the rake side of the cutting tool.

A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring.

The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel monitoring techniques for structural monitoring and evaluation. This study aims to determine the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test. The concrete cylindrical samples with embedded steel bars were prepared, cured for 28 days, and a pull-out test was performed to measure the interfacial bond between them. The piezo coupled signatures were obtained for the PZT patch bonded to the steel bar. The damage qualification is performed through the statistical indices, i.e., root-mean-square deviation (RMSD) and correlation coefficient deviation metric (CCDM), were obtained for different displacements recorded for axial pull. Furthermore, this study utilizes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based hybrid model, an effective regression model to predict the EMI signatures. These results emphasize the efficiency and potential application of the deep learning-based hybrid model in predicting EMI-based structural signatures. The findings of this study have several implications for structural health diagnosis using a deep learning-based model for monitoring and conservation of building heritage.

Monitoring of prestressed concrete beam under corrosion using embedded piezo sensor based on electro-mechanical impedance technique

<h2>Abstract</h2> The service life and durability of the prestressed concrete (PC) structures mainly depend on the corrosion resistance of the prestressing strands, wires, or tendons. Once corrosion occurs in the strands, identifying the location and quantifying the phases of corrosion is a very challenging task. Thus, this paper presents a novel way to monitor the corrosion process in a PC beam using an embedded piezo sensor (EPS) based on the electro-mechanical impedance (EMI) technique. The accelerated corrosion tests were conducted on a PC beam in which EPS was attached to the prestressing wire inside the beam to monitor the changes in the EMI signature during the corrosion progression. Further, this study identifies the deterioration in mechanical changes such as stiffness, mass, and damping by extracting the equivalent structural parameters (ESPs) from the raw EMI signatures. Based on qualitative and quantitative results, it is found that the EPS effectively senses the changes and identifies the different phases of corrosion. Also, the ESPs are realistically measuring the material deterioration under corrosion. Hence, the EMI technique based on EPS in structural health monitoring of PC beam can accurately assess the deterioration under the effect of corrosion.

Machine learning-based monitoring and predicting the compressive strength of different blended cementitious systems using embedded piezo-sensor data

This paper presents strength monitoring and prediction of blended cementitious systems using embedded piezo sensor via machine learning (ML) models. Experiments were conducted on three cementitious systems such as ordinary Portland cement (OPC), fly-ash blended cement (FA), and limestone calcined-clay cement (LC3) in which piezo sensor is embedded inside the cement pastes to acquire electro-mechanical impedance (EMI) signatures in the form of conductance and susceptance during strength development. Further, an equivalent stiffness parameter (ESP) was extracted from EMI signatures by developing physical model based on spring and damper element. Furthermore, ML models were developed to predict the compressive strength based on ESP and destructive compressive strength data. Experimental result indicates ESP (non-destructively) and compressive strength (destructively) value of LC3 exhibits higher than other two cementitious systems (OPC and FA). The developed ML models show promising results in prediction of compressive strength for all the cementitious systems with R2 = 0.99.

Longitudinal modes of a surface bonded piezoelectric wafer active transducer (PWaT) and their influences on PWaT resonances

This work investigates the longitudinal modes of surface-bonded piezoelectric wafer active transducers (PWaTs) and how they influence the PWaT resonances. Unlike conventional one-dimensional bar structures that essentially have only one non-dispersive longitudinal mode, the bonded PWaT has two dispersive longitudinal modes, with one evanescent (non-propagating) wave mode below a cut-off frequency. Their propagation characteristics, such as wave numbers, attenuations, and mode conversion at the PWaT edges, clarify two research questions raised from the time–frequency analysis of the PWaT electromechanical impedance (EMI) signature and the associated broadband pitch-catch signal. The study also revealed that the PWaT resonances stem from the Fabry-Perot interference of the waves reflected at the two edges of the PWaT, providing a sound theoretical foundation that further explains the influence of the adhesive on the PWaT resonances. These theoretical discoveries will lead to physics-based interpretation of EMI and ultrasonic pitch-catch signals generated by surface bonded PWaTs, which could lead to new sensing mechanisms in future.

Embedded Piezo-Sensor-Based Automatic Performance Monitoring of Chloride-Induced Corrosion in Alkali-Activated Concrete

The primary goal of the construction industries worldwide is to improve material durability and achieve sustainability. In recent years of sustainable cement industry innovation, alkali-activated cement has emerged as one of the most promising alternatives to ordinary Portland cement (OPC). In terms of durability, corrosion of steel is a significant problem and has become a major cause of deterioration of reinforced concrete structures worldwide. Thus, structural health monitoring techniques are essential to monitor the corrosion in real-time to avoid unexpected failure since civil engineering structures serve as a crucial pillar of the economy. This paper presents through an experimental campaign a novel method of automatically monitoring the performance of alkali-activated concrete (AAC) and ordinary Portland cement concrete (OPCC) under chloride-induced corrosion conditions using an embedded piezo sensor (EPS) based on the electro-mechanical impedance (EMI) technique. AAC was produced using alkali silicate-activated fly ash and ground granulated blast furnace slag. The accelerated corrosion tests were conducted on reinforced AAC and OPCC specimens in which the EPS was attached to reinforcing steel bars inside the specimens to monitor the changes in the EMI signature during the corrosion progression. To quantify the damage due to chloride-induced corrosion, statistical damage indices such as root mean square deviation were calculated. Further, the deterioration in structural parameters was identified by extracting the equivalent structural parameters (ESPs) such as stiffness, mass and damping from the raw EMI signatures. Based on qualitative and quantitative results, it can be seen that the changes in raw signature and damage in AAC were lower than OPCC. The deterioration in term of stiffness loss was found to be 39.35% in OPCC and 12.73% in AAC. Hence, it is demonstrated that the AAC exhibits a superior corrosion resistance to OPCC.

Electromechanical impedance-based embeddable smart composite for condition-state monitoring

In the present study, an alternating current (AC) based electro-mechanical impedance (EMI) technique was employed to evaluate the performance of smart cementitious composite-based impedance sensor (SCC- i S) over a frequency range, and then was employed as embeddable sensor for monitoring and assessing the health of concrete structure. For this purpose, a method for developing SCC-iS by reinforcing the carboxylic group (COOH-) multi-walled carbon nanotube (COOH-MWCNT) in the cementitious composite matrix is described. The optimal CNT concentration in SCC- i S was obtained based on the AC impedance measurement technique over a specified frequency range. After finding the optimal CNT concentration in SCC- i S, their piezoresistive behaviour (in terms of change in electrical impedance or electrical conductance) was examined using an electro-mechanical impedance measurement approach under uniaxial compressive loading. Finally, the developed sensor is employed as an embedded sensor for damage monitoring of structures under flexural loading. The acquired electrical impedance- and conductance- responses from the SCC- i S were utilized to evaluate the occurrence and progression of damage in real-time. The damage metric and the shift in frequency band were used to quantify the level of damage in the structures. From the experimental study, 0.50 wt% CNT concentration is found to be optimum for developing the efficient SCC- i S. Under the uniaxial compressive loading, the electrical variation in impedance (EVZ) and electrical variation in conductance (EVC) in SCC- i S exhibit excellent correlation with compressive strain and are capable of detecting the damage. The acquired signals from embedded SCC- i S show the effectiveness and sensitivity (in both qualitative and quantitative terms) for damage monitoring of structures; even they are capable of tracing the early-stage damage initiation in concrete structures. The electrical conductance spectra within a specified frequency range show a leftward shift, indicating the degradation in stiffness of concrete structure. Overall, the developed SCC- i S is the first of its kind and is found to be very effective and durable for real-time monitoring of structures, especially in the aggressive environment. • Development of smart nanoengineered composite using functionalized CNT. • Embedded sensors for capturing the electro-mechanical impedance (EMI) signature. • Sensor characterization and evaluation of piezoresistive behavior in frequency bands. • Demonstration for damage monitoring of structures under flexural loading. • Damage metric and shift in frequency band for accurate damage quantification.

Health monitoring of high-damping viscoelastic materials using sub-resonator enriched electro-mechanical impedance signatures

This article presents a recent progress of the electromechanical impedance spectroscopy methodology for monitoring high-damping viscoelastic materials (solid rocket motor propellant for instance). It employs piezoelectric wafer active sensors (PWAS) with sub-resonators which can create additional resonance peaks for enriching the diagnostic information. In order to develop an in-depth understanding of the mechanism behind the sub-resonator sensor system, an analytical investigation is conducted first. The theoretical formulation treats the sub-resonator PWAS as a mass-in-mass model, which can be seamlessly merged into the traditional 1D constrained PWAS case. The analytical solution for the impedance signature of the new sub-resonator PWAS transducer is derived. Meanwhile, parametric case studies are carried out to analyze the influence of material damping and additional mass of the sub-resonators on the impedance signature. Moreover, numerical simulations are performed to demonstrate the effectiveness of the new sensor creating additional resonance peaks. Comparative investigations are carried out with both the conventional PWAS and the sub-resonator PWAS. EMIS damage indices based on the spectral amplitude and frequency variation features are used to quantify the material degradation. Additionally, the performance of the proposed sub-resonator PWAS is demonstrated via a material creep experiment. The article finishes with summary, concluding remarks, and suggestions for future work.

Crack healing in concrete by microbially induced calcium carbonate precipitation as assessed through electromechanical impedance technique

In the present study, Electromechanical Impedance (EMI) technique is utilized to assess the application of the microbially induced calcium carbonate precipitation (MICCP) to restore the integrity of cracked concrete. The artificial crack width of approximately 0.5 mm is healed autonomously using a bacteria-based healing agent, using flyash (FA) as a carrier material. Lead Zirconate Titanate sensors were bonded on the cracked specimen to monitor the continuous healing process. The EMI signatures were captured over the healing period using an impedance analyser in the frequency range of 100 to 250 kHz. Statistical crack healing indicator, root mean square deviation (RMSD) was employed for evaluating crack efficiency based on extracted signatures. Results revealed that the RMSD values were effective in quantifying the progressive healing achieved due to MICCP precipitation. This is the first study with a successful implementation of the EMI technique to monitor the crack sealing in concrete through MICCP. Healing capacity was also correlated with the crack width reduction that was evaluated by optical imaging of repaired cracked surface. Also, the bacterial mineralization products were analyzed by FESEM, EDX, XRD and TGA. The results give clear proof that the FA-based carrier material can be effectively used in healing the cracks.

Effect of mechanical properties and geometric dimensions on electromechanical impedance signatures for adhesive joint integrity monitoring

Adhesive joints are prone to damage, which may be difficult to detect using Nondestructive Test. Structural Health Monitoring techniques, such as the Electromechanical Impedance Spectroscopy (EMIS), allow for continuous monitoring and early detection of structural defects. Developing efficient damage detection algorithms requires simulations of adhesive structures, which require elastic and damping properties under high frequency range. Tests were performed for crash-resistant and silicone adhesives. Numerical simulations with said properties were compared with experimental impedance measurements from Single Lap Joints (SLJ). This allowed for the estimation of adhesive properties, but numerical results for SLJs with silicone adhesive diverged from experimental results.

Monitoring of drying-induced soil moisture loss with PZT-based EMI technique

Air-drying process of soil is a crucial procedure in geological and geotechnical engineering. Drying-induced ground subsidence and damage to overlying buildings is a widespread and urgent problem. Monitoring of drying-induced water evaporation in soil is of great importance. In this paper, soil moisture loss monitoring based on lead zirconate titanate (PZT) transducer using electro-mechanical impedance (EMI) technique was investigated. A physical model test in our laboratory was conducted to study the feasibility and applicability. In the experimental research, three identical PZT transducers that were wrapped with waterproof insulation glue were pre-embedded inside a cohesive soil specimen. In addition, another PZT transducer was embedded in a sandy soil specimen to explore the application effect in soil with different composition. EMI signatures of these four PZT patches during the air-drying process were collected and analyzed. Experimental results indicated that the peak frequency in the conductance signatures presented a rightward shift as the water evaporates. Moreover, the corresponding peak magnitude keep decreasing with the continuous development of soil moisture loss. To better quantify the variations, two statistical metrics including root mean square deviation (RMSD) and mean absolute percentage deviation (MAPD) were employed to study the changing characteristics of the EMI signatures. All these two metrics increase coincidentally in the process. Experimental results demonstrate that cohesive and sandy soil moisture loss monitoring by using the embedded PZT transducer is feasible and reliable. This work also serves as a proof-of-concept study to demonstrate the performance of the EMI technique in monitoring the soil moisture content.

Inverse analysis for damage detection in a rod using EMI method

The electro-mechanical impedance (EMI) method has been demonstrated to be a powerful tool to detect the small local damages in various engineering structures, due to its high-frequency vibration characteristics. However, this technique is difficult to accurately determine the degree and location of structural damages. In this paper, an inverse analysis combining the measured EMI signatures and the Transfer Matrix Method (TMM) is proposed to identify the damages in the steel rods. A milled groove is introduced into each steel specimen to simulate the local damage. By measuring the EMI signatures of the piezoelectric wafers bonded symmetrically on the upper and lower surfaces of the rectangular specimens respectively, the damage can be detected clearly, as well as natural frequencies being extracted in the relatively lower frequency range. The damage sizes and locations are further determined by connecting the frequency equations and any two measured natural frequencies. Tests on three samples show both damage sizes and locations are accurately detected, which indicates that the present method is quite feasible and effective for damage identification in the bar-type structures.

Detection of multiple structural damages from drive point and cross electro-mechanical impedance signatures

A damage detection scheme using the electro-mechanical impedance based signatures for detection of multiple damages is proposed. A beam and a stiffened plate are considered as two example structures in this study. Cracks of 1 mm width and varying depth have been considered as damages for the beam structure. On the other hand, disbonds between the plate and stiffener along the stiffener length have been considered as damage in the stiffened plate structure. Drive point and cross conductance at two piezoelectric patch transducers in a chosen frequency range have been obtained using the finite element based software ANSYS. Result of ANSYS has been validated through experimental result. Root mean square deviation (RMSD) and the correlation coefficient (CC) of the conductance of the structure from the baseline conductance have been used as damage features. An artificial neural network with one hidden layer has been trained considering the damage features as input and crack depths/disbond lengths at different locations as output. Accuracy of the trained network in detecting crack depths/disbonds has been observed through a set of test cases.