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Real-time monitoring of structural health status is very important for supersonic aircraft, since the long-term operation in harsh service environments, such as high temperatures, can cause potential damage. Structural health monitoring (SHM) based on lamb wave has been approved to be a critical technique for the reliable operation of supersonic aircraft, but is limited by the operating temperature of conventional piezoelectric sensors, which makes it difficult to meet the high-temperature monitoring requirements. The BiFeO 3 –BaTiO 3 ceramics exhibit excellent piezoelectric properties and high Curie temperature (T c), making them promising for high-temperature SHM of supersonic aircraft. Here, we fabricated a piezoelectric sensor using 0.67BiFeO 3 -0.33BaTiO 3 -0.25%molMnO 2 (BF33BTMN-025) ceramic, which exhibits a piezoelectric charge constant (d 33) of 135 pC/N at room temperature and a T c of 448 °C. The BF33BTMN-025 ceramic sensor operates effectively at temperatures up to 200 °C and maintains stable damage monitoring sensitivity as well. Compared with Pb(Zr, Ti)O 3 (PZT), BaTiO 3 (BTO) and other piezoelectric sensors commonly used in SHM field, the BF33BTMN-025 ceramic sensor has a higher operating temperature and a comparable sensitivity. Our work demonstrates that the BF33BTMN-025 ceramic sensor can be employed as piezoelectric sensor for high-temperature SHM, which has a wide application potential in the field of real-time monitoring of supersonic aircraft. [ABSTRACT FROM AUTHOR]

High-temperature piezoelectric vibration sensors play a crucial role in the accurate monitoring of the dynamic mechanical conditions in aerospace, automotive, and energy generation systems. However, the use of conventional piezoelectric materials in high-temperature environments is restricted owing to their limited Curie temperatures. In this study, we grew a piezoelectric crystal Bi12SiO20 (BSO) with the crystal cut optimized for high longitudinal piezoelectric coefficient and low piezoelectric crosstalk behaviors. Subsequently, a compression-type piezoelectric vibration sensor utilizing the BSO bulk crystal was developed and fabricated for structural health monitoring under high temperatures. The impact of pre-tightening torques on the sensor performance was investigated. Moreover, the sensor performance was analyzed under temperatures up to 650 °C. The BSO-based sensor exhibited an average sensitivity of ∼3.89 pC/g between 25 and 650 °C under 160 Hz frequency, with a variation of 5.5%. Additionally, the BSO-based sensor demonstrated ultra-stable sensitivity at 600 °C, highlighting its strong sensing capabilities and reliability under high temperatures. Thus, the BSO-based vibration sensor is a promising option for structural health monitoring applications under high temperatures. [ABSTRACT FROM AUTHOR]

With the advent of bonded composites in today's aircraft, there is a need to verify the structural integrity of the bonded joints that comprise their structure. To produce adequate joint integrity, strict process control is required during bonding operations. The latest non-destructive joint inspection techniques cannot quantify the strength of the bond and only indicate the presence of disbonds or delaminations. Expensive and timely proof-load testing of the joints is required to demonstrate structural performance. This work focuses on experimentally evaluating joint-health monitoring with piezoelectric sensors exposed to repeated loadings until failure. Single-lap-shear composite joints are structurally tested while using sensor electromechanical impedance response as a health indicator. Based on these experiments, validation of this novel method is achieved through detailed evaluation of the sensor impedance response characteristics during loading, which enable initial and prognostic joint health assessments. The experimental results indicate that the embedded piezoelectric sensors are able to measure the sensor impedance radial and thickness resonance response changes prior to joint failure, without sacrificing the joints' structural performance. [ABSTRACT FROM AUTHOR]

This study focuses on the effect of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts. We explain the influence of temperature on the basis of the phenomenological thermodynamic theory of ferroelectricity by Landau, Ginsburg and Devonshire. The article illustrates the effect of damping the radial deformation of piezoelectric sensors on the capacitance and electromechanical impedance spectra in structural health monitoring of bolts. We also explains the similarities between the effects of temperature and preload force on the electromechanical impedance spectra. We establish a clear correlation between the mechanical strain in the region of the sensor (here due to a preload force), the capacitance and the electromechanical impedance spectra and thus show that piezoelectric sensors made of lead–zirconate–titanate can be used excellently in areas of variable mechanical strain. The article enhances the understanding of the measurement method and facilitates the transfer of the measurement method to other problems in structural health monitoring. Furthermore, the acquired knowledge serves as a solid basis for verifying the plausibility of data sets containing electromechanical impedance spectra. [ABSTRACT FROM AUTHOR]

Acceleration sensors are vital for assessing engineering structures by measuring properties like natural frequencies. In practice, engineering structures often have low natural frequencies and face harsh environmental conditions. Understanding sensor behavior on such structures is crucial for reliable measurements. The research focus is on understanding the behavior of acceleration sensors in harsh environmental conditions within the low-frequency acceleration range. The main question is how to distinguish sensor behavior from structural influences to minimize errors in assessing engineering structure conditions. To investigate this, the sensors are tested using a long-stroke calibration unit under varying temperature and humidity conditions. Additionally, a mini-monitoring system configured with four IEPE sensors is applied to a small-scale support structure within a climate chamber. For the evaluation, a signal-energy approach is employed to distinguish sensor behavior from structural behavior. The findings show that IEPE sensors display temperature-dependent nonlinear transmission behavior within the low-frequency acceleration range, with humidity having negligible impact. To ensure accurate engineering structure assessment, it is crucial to separate sensor behavior from structural influences using signal energy in the time domain. This study underscores the need to compensate for systematic effects, preventing the underestimation of vibration energy at low temperatures and overestimation at higher temperatures when using IEPE sensors for engineering structure monitoring. [ABSTRACT FROM AUTHOR]

Due to their distinct features, including structural simplicity and exceptional load-carrying capacity, steel box girder bridges play a critical role in transportation networks. However, they are categorized as fracture-critical structures and face significant challenges. These challenges stem from the overloading and the relentless effects of corrosion and aging on critical structural components. As a result, these bridges require thorough inspections to ensure their safety and integrity. This paper introduces generalized approaches based on vibration-based structural health monitoring in response to this need. This approach assesses the condition of critical members in a steel girder bridge and evaluates their sensitivity to damage. A rigorous analytical evaluation demonstrated the effectiveness of the proposed approach in evaluating the Szapáry multi-span continuous highway bridge under various damage scenarios. This evaluation necessitates extensive vibration measurements, with piezoelectric sensors capturing ambient vibrations and developing detailed finite element models of the bridge to simulate the structural behavior accurately. The results obtained from this study showed that bridge frequencies are sufficiently sensitive for identifying significant fractures in long bridges. However, the mode shape results show a better resolution when compared to the frequency changes. The findings are usually sensitive enough to identify damage at the affected locations. Amplitude changes in the mode shape help determine the location of damage. The modal assurance criterion (MAC) served to identify damage as well. Finally, the results show a distinct pattern of frequency and mode shape variations for every damage scenario, which helps to identify the damage type, severity, and location along the bridge. The analysis results reported in this study serve as a reference benchmark for the Szapáry Bridge health monitoring. [ABSTRACT FROM AUTHOR]

Finding damages and their prevalence in structures is a very challenging problem. In order to identify potential structural damage, non‐destructive methods are not very helpful. One of the most popular piezoelectric ceramic materials used worldwide is macromolecular lead zirconate titanate (PZT), also known as Pb[Zr(x)Ti(1‐x)]O3. PZT transducers are proving to be a successful alternative for assessing the structure's health. The Macro‐molecular PZT's self‐actuating and sensing properties are utilized by the electromechanical impedance (EMI) method. In this capacity, the macro‐molecular PZT patches serve as co‐located actuators and sensors and make use of ultrasonic vibrations to produce a distinctive admittance "signature" of the structure. PZT patches perform exceptionally well in terms of damage sensitivity. The higher modes of vibration are locally activated by the local application of an AC source on the PZT transducers attached to the host structure. The alteration of the admittance response is a sign of injury to the transducer's vicinity. In the current study, the damage percentage and its location in a 150 × 150 × 150 mm concrete cube are being examined. Regression analysis is also used to determine the concrete cube's strength. This method also can identify hair‐like cracks at an early stage, which explains how it can shield the building from serious failures. [ABSTRACT FROM AUTHOR]

Civil engineering is not just about construction of buildings, towers, bridges etc. but also of their caring and maintenances. For this purpose all the constructed structures have to be monitored for their structural health in terms of damage detection, severity of damage etc., collectively called structural health monitoring (SHM) of structures in civil engineering. For SHM, electromagnetic impedance (EMI) technique is one of the best methods of NDTs being used in the field of civil engineering. EMI uses lead zirconate titnate (PZT) as its piezoelectric component for the purpose of measurement. PZT based devices are widely used in civil engineering. There is a huge demand for health monitoring now a days and to full fill those demands of health monitoring, PZT is one among the most reliable and cost efficient material. Piezoelectric sensor can be used to measure changes in acceleration, strain, pressure and to ensure the better safety measures. These PZT based sensors can be used for predicting the natural disasters like earthquake that helps to save the thousands of lives and can decrease property lose. The piezoelectric sensors can be used to monitor the civil structure which helps an engineer to analyses the characteristics of that structure. It is also used for collecting the data of force that is being exerted on the ground by the building, temperatures and pressure differences in environment etc. Damage detection is a technique that is used to monitor the civil structure, this can improve safety and ensure durability, and for concrete structures, damage detection plays an important role. For the purpose of SHM and damage detection PZT based sensors can be used. For civil engineering applications, PZT-cement based composites have been developed. The concrete and host structure such PZT-cement based composites shows better matching when compared to normal or other piezoelectric ceramics. In this review work we have discussed about SHM/Damage detection, Vibrational control, PZT embedded cement composites and PZT embedded in concrete. [ABSTRACT FROM AUTHOR]

Aircraft smart skin requires the integration of large-scale, ultrathin, and high-sensitive sensor network on the surface for structural health monitoring (SHM). However, it is fairly difficult to fabricate such large-area flexible sensor networks with low cost and high reliability. Although flexible and monolithic sensors are easy to be fabricated, their applications in large-area impact monitoring have yet to be developed and revealed. In this study, an impact monitoring and localization technology based on monolithic small-area sensors is proposed for large-area structures. The pivotal principle lies in the combination of the flexible piezoelectric sensor array and the Multiple Signal Classification (MUSIC)-assisted Artificial Intelligence Network (ANN) algorithm, where the key sorted feature matrix from the output signals to ANN can be captured by the MUSIC algorithm to achieve the spatial location estimation. The results show that the flexible piezoelectric sensors demonstrate excellent performance to monitor structural impacts in a region of more than 7500% of its area. Secondly, excellent conformation between the sensors and complex surfaces is achieved by the ultrathin thickness almost without affecting the surficial flow field. The overall recognition accuracy and robustness of impact location of machine learning is greatly increased by the integration of MUSIC algorithm, which is immune to the shape, material, thickness, or opening holes of the structure. Except for the impact location, impact energy, impact frequency, impact hardness and other structural health parameters can also be monitored. Therefore, a great prospect in the integrated monitoring of the aircraft's structural and environmental parameters is expected. [ABSTRACT FROM AUTHOR]

In this study, we propose a piezoelectric ceramic sensor-based health monitoring method to monitor the stress state of diagonally braced H-shaped steel structures. Loading experiments were carried out on diagonally braced H-shaped steel under different working conditions. Subsequently, the amplitude and energy of piezoelectric signals under these two working conditions were compared and analyzed, and finite element analysis was performed using ABAQUS software to verify the results. The experimental results showed that with an increase in the web height or load, the time-domain waveform energy index of the H-shaped steel increased. Under different working conditions, such as a diagonally braced H-shaped steel member with a web height of 10 cm, when the pressure value was less than 10 N/mm2, the energy index increased by approximately 15.98% for every 1 N/mm2 increase in the pressure. When the pressure value was greater than 10 N/mm2 and less than 15 N/mm2, the energy index increased by approximately 1% for every 1 N/mm2 increase in pressure. Further, the energy index increased by approximately 8.4% for every 1 cm increase in the height of the web. Simultaneously, it can be seen from the results of the finite element analysis that the stress and strain at the induction position of the piezoelectric ceramic sensor increased with an increase in the external pressure. The study of structural health monitoring for diagonally braced H-shaped steel structures holds significant importance in ensuring the safety and reliability of these structures, achieving predictive maintenance, evaluating structural performance and energy efficiency, and optimizing structural maintenance. [ABSTRACT FROM AUTHOR]

This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch–catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint's borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results. [ABSTRACT FROM AUTHOR]

This article aims to accomplish research's that has been achieved recently in the scientific community on the manufacturing monitoring and structural health monitoring (SHM) of composite materials using in-situ piezoelectric sensors. A series of specimens of flax/Elium composite laminates with and without integrated piezoelectric sensors were subject to static and fatigue tensile tests. The effects of introducing the sensor into the composite materials were studied. Moreover, the Acoustic Emission (AE) method was used during experimental tests to follow the gradual degradation and damage evolution in the bio-composites. The AE data were analyzed by the k-means unsupervised pattern recognition algorithm. Moreover, the characteristics of the acoustic signals detected from specimens with and without integrated sensors were compared. The experimental results show that the integration of piezoelectric sensors within the flax/Elium composite without degrading its mechanical properties is possible. In addition, the integrated sensor shows good results regarding class detection and separation. The in-situ health monitoring methods performed on bio-composites presents an excellent alternative to the conventional technique in which sensors are generally bonded to the material surface. [ABSTRACT FROM AUTHOR]

This study aims to examine the dynamic response of a polyvinylidene fluoride (PVDF) piezoelectric sensor which is embedded into an aluminum coupon using ultrasonic additive manufacturing (UAM). Traditional manufacturing techniques used to attach smart materials to metals on the surface have drawbacks, including the potential of exposing the sensor to adverse environments or physical degradation during manufacture. UAM can avoid these issues by integrating solid-state metal joining with subtractive processes to enable the fabrication of smart structures. A commercial PVDF sensor is embedded in aluminum with a compression technique to provide frictional coupling between the sensor and the metallic matrix. The PVDF sensor's frequency bandwidth and impact detection performance are evaluated by conducting cantilever and axial impact tests, as well as harmonic excitation tests with an electrodynamic shaker. Under axial loading, the embedded sensor displays high linearity with a sensitivity of 43.7 mV/N, whereas impact tests in the cantilever configuration exhibit a steady decay rate of 0.13%. Finally, bending tests show good agreement between theoretical and experimental natural frequencies with percentage errors under 6% in two different clamping positions, and correspond to the maximum voltage output obtained from the embedded PVDF sensor at resonance. [ABSTRACT FROM AUTHOR]

Piezoelectric (PZT) sensors employed in the electro-mechanical impedance/admittance (EMI/EMA) technique are vulnerable to temperature variations when applied to concrete structural health monitoring (SHM). However, in practice, the ambient temperature transmitted from the air or surface to the concrete inner part is time-dependent during its monitoring process, which inflicts a critical challenge to ensure accurate signal processing for PZT sensors embedded inside the concrete. This paper numerically and experimentally investigated the thermal hysteresis effect on EMA-based concrete structure monitoring via an embedded PZT sensor. In the numerical modeling, a 3D finite element model of a concrete cube embedded with a PZT sensor was generated, where thermal hysteresis in the concrete, adhesive coat, and sensor was fully incorporated by introducing a temperature gradient. In the experiment, an equal-sized concrete cube installed with a cement-embedded PZT (CEP) sensor was cast and heated for 180 min at four temperature regimes for EMA monitoring. Experimental results, as a cogent validation of the simulation, indicated that EMA characteristics were functionally correlated to the dual effect of both heat transfer and the temperature regime. Moreover, a new approach relying on the frequency/magnitude of the maximum resonance peak in the EMA spectrum was proposed to effectively compensate for the thermal hysteresis effect, which could be regarded as a promising alternative for future applications. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves (UGW) are widely used in structural health monitoring (SHM) systems due to the sensitivity of their propagation mechanisms to local material changes, i.e., those induced by damage. Post-processing of the signals gathered by piezoelectric sensors, typically used for both the excitation and the sensing of UGW, is a fundamental step to extract all the peculiar features that can be related to both damage location and severity. This research probes the efficacy of machine learning (ML) models in discerning damage location (R-Classification) and size (S-Classification). Seven supervised ML classifiers were examined: Ensemble-Subspace K-Nearest Neighbors (KNN), Ensemble-Bagged Trees, KNN-Fine, Ensemble-Boosted Trees, Support Vector Machine (SVM), Linear Discriminant, and SVM-Quadratic. The experimental dataset comprised measurements from varied reversible damage configurations on a composite panel, represented by wooden cuboids of single and three different sizes. Signal noise was minimized by performing a low-pass filter, and sequence forward selection-aided feature selection. The optimized ensemble classifier proved to be the most precise for R-Classification (95.83% accuracy), while Ensemble-Subspace KNN excelled in S-Classification (98.1% accuracy). This method offers accurate, efficient damage diagnosis and classification in composite structures, promising potential applications in aerospace, automotive, and civil engineering sectors. [ABSTRACT FROM AUTHOR]

Utilization of an Electromechanical impedance (EMI) technique with Piezoelectric (PZT) sensors has showed potential for Structural Health Monitoring (SHM). The changes in mechanical structure via flexural bending and cracking can be detected by monitoring the deviations in electrical impedance signals recorded with embedded PZT sensors. This paper has conducted a comprehensive study on the potential of an EMI technique with embedded PZT sensors with 3D Concrete Printing (3DCP) structures subjected to flexural bending test until plastic failure. The impact of different Piezoelectric housing methods and materials has been studied comprehensively through the monitoring of EMI signals. Experimental results indicate that material housing types and thickness affect the sensitivity of EMI readings but also performed as a reinforcement when a load is directly applied. The embedded PZT sensors with the EMI technique have shown strong potential to address the cost and lifecycle challenges posed by traditional construction methods as the insertion of PZT sensors seamlessly functions with 3DCP workflows. Further developmental work can be carried out to address the sensitivity of the sensor, performance as a reinforcement, and installation automation. The results proved that the coated sensors could detect fractures in 3DCP concrete with decreased sensitivity on thicker coating layers through the variance in materials and coating thickness in the paper. [ABSTRACT FROM AUTHOR]

With demand for the long-term continued use of existing building facilities, structural health monitoring and damage detection are attracting interest from society. Sensors of various types have been practically applied in the industry to satisfy this need. Among the sensors, piezoelectric sensors are an extremely promising technology by virtue of their cost advantages and durability. Although they have been used in aerospace and civil engineering, their application for building engineering remains limited. Remarkably, recent catastrophic seismic events have further reinforced the necessity of rapid damage detection and quick judgment about the safe use of facilities. Faced with these circumstances, this study was conducted to assess the applicability of piezoelectric sensors to detect damage to building components stemming from concrete cracks and local buckling. Specifically, this study emphasizes structural damage caused by earthquakes. After first applying them to cyclic loading tests to composite beam component specimens and steel frame subassemblies with a folded roof plate, the prospective damage positions were also found using finite element analysis. Crack propagation and buckling locations were predicted adequately. The piezoelectric sensors provided output when the concrete slab showed tensile cracks or when the folded roof plate experienced local buckling. Furthermore, damage expansion and progression were detected multiple times during loading tests. Results showed that the piezoelectric sensors can detect the structural damage of building components, demonstrating their potential for use in inexpensive and stable monitoring systems. [ABSTRACT FROM AUTHOR]

Piezoelectric materials are defined by their ability to display a charge across their surface in response to mechanical strain, making them great for use in sensing applications. Such applications include pressure sensors, medical devices, energy harvesting and structural health monitoring (SHM). SHM describes the process of using a systematic approach to identify damage in engineering infrastructure. A method of SHM that uses piezoelectric wafers connected directly to the structure has become increasingly popular. An investigation of a novel pitch-catch method of determining instrumentation quality of piezoelectric wafer active sensors (PWASs) used in SHM was conducted as well as an investigation into the effects of defects in piezoelectric sensors and sensor bonding on the sensor response. This pitch-catch method was able to verify defect-less instrumentation quality of pristinely bonded PWASs. Additionally, the pitch-catch method was compared with the electromechanical impedance method in determining defects in piezoelectric sensor instrumentation. Using the pitch-catch method, it was found that defective instrumentation resulted in decreasing amplitude of received and transmitted signals as well as changes in the frequency spectrums of the signals, such as the elimination of high frequency peaks in those with defects in the bonding layer and an increased amplitude of around 600 kHz for a broken PWAS. The electromechanical impedance method concluded that bonding layer defects increase the primary frequency peak's amplitude and cause a downward frequency shift in both the primary and secondary frequency peaks in the impedance spectrum, while a broken sensor has the primary peak amplitude reduced while shifting upward and nearly eliminating the secondary peak. [ABSTRACT FROM AUTHOR]

Most cracks do not grow straight due to the inhomogeneity of material properties. Furthermore, quantifying structural damage using Lamb waves is difficult due to uncertain crack shape. Here, we present a newly developed finite element model to simulate the propagation of Lamb waves in a cracked plate to quantify the influence of crack size and orientation on the characteristics of Lamb wave propagation. This model includes a piezoelectric actuator and sensor to generate and receive the tone-burst signals of Lamb wave. The damping coefficient of the finite element model was calibrated with the experimental data of plates without a crack. The Lamb wave propagation simulations were performed with various crack sizes, crack orientations, and distances between the actuator and sensor. Our model was utilized to investigate the influence of crack orientation on damage quantification based on the signal phase change and normalized amplitude. We successfully evaluated the effect of the incident angle of the crack, crack size, and distance between the sensors on the change of the plate wave signal characteristics. The signal characteristics obtained from our novel finite element model can be used to develop a quantification model to estimate the crack size, direction, and distance from the sensor. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as low as 28 µm—PVDF) allows embedding the sensors within the laminated composite, creating a smart material. Incorporating piezoelectric sensors within composites has several benefits but presents numerous difficulties and challenges. This paper provides an overview of acoustic emission testing, concluding with a discussion on embedding piezoelectric AE sensors within fibre-polymer composites. Various aspects are covered, including the underlying AE principles in fibre-based composites, factors that influence the reliability and accuracy of AE measurements, methods to artificially induce acoustic emission, and the correlation between AE events and damage in polymer composites. [ABSTRACT FROM AUTHOR]

Crack imaging is attractive in the community of nondestructive testing and structural health monitoring of plate-like structures. However, most existing research focuses on the imaging of single crack, while limited work has been reported for the imaging of multiple cracks. To this end, the application of the Lamb wave-based technique is presented in this paper to image the multiple cracks. The antisymmetric mode (A0) of Lamb wave propagation in the thin plate is simulated using the numerical finite element method, and a circular piezoelectric sensor array is considered to capture the Lamb wave signals. The damage index taking into account the attenuation of the reflected signal amplitude is proposed using the reflection signals from the cracks, and the Hilbert transform is used to extract the information of damage. The damage scanning map is constructed to show the damage location in the plate. The proposed damage identification algorithm based on Lamb wave propagation is also used to detect two cracks in the thin plates. The consideration of wave attenuation improves the identification of multiple cracks. The proposed damage identification algorithm using a phased piezoelectric sensor array shows its potential to locate the cracks in thin plates. [ABSTRACT FROM AUTHOR]

Damage detection is of great importance for structural construction management and safety. In this study, an ultra-sensitive flexible piezoelectret sensor for damage monitoring in structural health monitoring (SHM) is designed and fabricated, based on excellent electromechanical properties and piezoelectric response characteristics of electret films. The charge stability of the piezoelectret sensor was demonstrated by continuous potential measurement experiments. The experimentally measured piezoelectret coefficient is 6610 pC/N, which is about 10 times that of ordinary piezoelectric ceramics and proves the high sensitivity of piezoelectret. A series of comparative experiments on the piezoelectret sensor and piezoelectric ceramics demonstrate the high performance of the former. According to the damage detection test results, the damage index of cement mortar specimens was obtained. The linear relationship between the damage index and the crack widths proves the excellent performance of the piezoelectret sensor. A piezoelectret array is also used to detect cracks in cement mortar specimens, and can roughly estimate the depth of the fracture based on the damage index (DI) value of the sensing array. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) systems have shown the potential to detect damage in various types of structures, thus increasing safety and reducing maintenance costs. Among the damage detection methods are those that use small piezoelectric transducers attached to the monitored structure, such as the electromechanical impedance (EMI) and Lamb waves methods. Although both methods have shown to be effective in detecting structural damage, external disturbances such as noise can alter the signals from piezoelectric sensors in real applications and prejudice the correct diagnosis of the monitored structure. Therefore, this paper presents a comparative analysis of the performance of EMI and Lamb waves methods in noisy environments, which is a condition commonly found in real structures. The obtained results add value to the SHM field by allowing to know the tradeoff in choosing the appropriate method regarding the detection, quantification, and location of structural damage under noise conditions. Experimental tests were performed on aluminum structures to assess the effectiveness of both techniques to detect, quantify, and locate structural damage under noise of different intensities. The experimental results show that each technique has different characteristics in terms of effectiveness in detecting, quantifying, and locating damage under noise effects. [ABSTRACT FROM AUTHOR]

Piezoelectric sensor failure triggered by high structural strain is a critical problem in structural health monitoring (SHM) that cannot be neglected. Thus, a novel island-bridge packaging piezoelectric sensor for SHM in high-strain environments is proposed in this paper. The trapezoidal-section island-bridge structure of the new sensor greatly optimizes the high-strain survivability of the piezoelectric layer. Additionally, good impedance matching characteristics between the monitored structure and the sensor guarantee the transmission strength of high-frequency dynamic sensing signals. The feasibility of the structure and the principle of operation for the novel sensor were demonstrated via theory, simulation and experiment. Besides, an established analytical model and its simulation and experimental verification results displayed that the structural parameters of the sensor could significantly adjust the strain-bearing capacity. Under a specific parameter configuration, the strain transfer coefficient of the novel sensor was only 0.087, which was 12.1% of the conventional sensor, thus could safely operate in the monitoring structure environment of 10,000 με. On the other hand, the proposed sensor's impedance signal strength was satisfactory in the SHM frequency range of 10–110 kHz, where its average impedance value was as high as 397.7 Ω. Moreover, the optimized signal demonstrated very good stability, reliability, and strength in the test, thus ensuring the effective monitoring of crack propagation via the new sensor. Therefore, this work is a good complement to the existing piezoelectric sensors SHM technique. [ABSTRACT FROM AUTHOR]

P(VDF-TrFE)-based nano-fiber reinforced piezoelectric sensors are extensively used for energy harvesting, structural health monitoring and medical applications. However, low efficiency and sensitivity have been major issues to address. The sensor efficiency can be improved by adding ZnO nanoparticles. The aim of the present research is to study and quantify the effect of ZnO nanoparticles (higher percentage) on P(VDF-TrFE)-based nanocomposites. Here, poly(vinylidene fluoride-trifluoroethylene) copolymer and zinc oxide nanoparticle-based nanocomposite are prepared using one-step electrospinning method. ZnO nanoparticles with 18% w/v and 18% w/w concentration are considered for preparing P(VDF-TrFE)/ZnO solution. These concentrations were taken relative to both of P(VDF-TrFE) polymer and dimethylformamide (DMF) solvent. The nanocomposite films were characterized using different techniques and their β phase enhanced with the addition of filler. The viscosity, storage and loss modulus increased with the addition of filler, while damping factor decreased. The piezoelectric nanogenerators (PENG) were fabricated, and their piezoelectric outputs were recorded in terms of voltage under various loading such as pressing, bending and tapping by human hand. The piezoelectric output of the PENGs is enhanced from 704 mV to 1.15 V and 4 to 5.2 µA. It is observed that the addition of ZnO nanoparticles into the P(VDF-TrFE) nanofiber made the composite film more active and sensitive. The findings of this study open the door for developing self-powered wearable electrical systems and devices. [ABSTRACT FROM AUTHOR]

Nowadays, nonlinear vibration methods are increasingly used for the detection of damage mechanisms in polymer matrix composite (PMC) materials, which are anisotropic and heterogeneous. The originality of this study was the use of two nonlinear vibration methods to detect different types of damage within PMC through an in situ embedded polyvinylidene fluoride (PVDF) piezoelectric sensor. The two used methods are nonlinear resonance (NLR) and single frequency excitation (SFE). They were first tested on damage introduced during the manufacturing of the smart PMC plates, and second, on the damage that occurred after the manufacturing. The results show that both techniques are interesting, and probably a combination of them will be the best choice for SHM purposes. During the experimentation, an accelerometer was used, in order to validate the effectiveness of the integrated PVDF sensor. [ABSTRACT FROM AUTHOR]

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This paper concerns optimal placement and number of discrete piezoelectric macro fibre composite (MFC) sensors to optimise SHM systems. Its novelty lies in a two-stage placement methodology for discrete piezoelectric transducers, with fitness and objective functions to optimise the location and number of discrete piezoelectric sensors in order to reduce the cost and complexity of data processing and increase the effectiveness in damage detection. The maximisation of sensor voltage amplitude at multiple modes of vibration and the average of sensor normal damage index RMS D nor measured for several plates artificially cracked at different positions and orientations are proposed as objective functions to optimise the locations and the number of efficient piezoelectric sensors. A non-normalised root-mean-square deviation RMSD is introduced in this study in place of the conventional normalised NRMSD to assess the degree of damage and sensor effectiveness. Furthermore, normal damage indices RMS D nor and NRMS D nor normalised to 100% are proposed as the fitness functions. The placement methodology is utilised and verified for stiffened and unstiffened plates; stiffeners are used to break the dynamic symmetry and increase plate complexity. The performance of the placement methodology is tested for a healthy and 12 damaged plates to optimise SHM system based on the maximisation of sensor voltage and average normal damage index RMS D nor as objective functions. The results show that the placement methodology is efficient in determining the optimum number and placement of piezoelectric sensors at a low computational effort. The optimum placement of two piezoelectric sensors can efficiently monitor the crack's initiation at different positions and multiple modes of vibration. The optimal placement and number of sensors have a positive impact on the cost, data acquisition and processing of the active SHM system [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber–frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions. [ABSTRACT FROM AUTHOR]

Health monitoring of critical structures, that form parts of serial operating objects, is a pressing task. The Operational Modal Analysis (OMA) techniques could be the optimal solution. An inexpensive measurement system, such as the OMA, uses a lot of sensors for structural response assessment. The health monitoring of serial structures has to also consider possible deviations between samples. A solution providing the OMA application includes the compact measurement system based on piezoelectric film sensors and modal passport (MP) techniques. For validation of the proposed approach, a series of five similar composite cylinders, with a network of piezoelectric film sensors, was used. Applying modal tests on the specimens, and using OMA with MP methods, the set of typical modal parameters was determined and analyzed. The results of the study confirmed the feasibility of the sensor network and its applicability for structural health monitoring of serial samples using OMA methods. The proven effectiveness of OMA/MP techniques, combined with a sensor network, provides a prototype of intelligent sensor technology, which can be used for health monitoring of structures, including those that are part of an operating facility. [ABSTRACT FROM AUTHOR]

Introduction: The frequency response function of a structure is an important attribute that should be gathered for vibration-based damage detection in structural health monitoring. However, the function is strongly dependent upon the location of applied load and position of response measurement. So, it needs to use a lot of sensors and exciters for collecting the number of frequency response functions sufficient for the damage detection problem. Therefore, the use of moving load and distributed sensor for gathering the frequency response of a structure is a good idea to improve the solution to the problem of structural health monitoring. The present paper addresses frequency response analysis of a cracked FGM beam bonded with a piezoelectric layer as distributed sensor under moving load with the aim to directly use the frequency response for crack detection. Method: The governing equations of the integrated beam structure are derived with Hamilton's principle and solved by the analytical method in the frequency domain. Solutions have been obtained explicitly in the form of mechanical response (midspan defection) and electrical (sensor output charge) responses that are numerically examined in dependence on material gradient index, load frequency and speed, and crack depth and position. Results: It was shown that vibration behaviour of FGM beam with a piezoelectric layer under moving harmonic load is dominated mainly by two components acknowledged as forced and eigenmode vibrations, amplitudes of which are strongly dependent on load speed and frequency. It has also been demonstrated that amplitudes of the mechanical and electrical responses at the generic resonance, when load frequency equals to natural frequency of undamaged beam, are strongly sensitive to crack. Sensitivity of the eigenmode resonant amplitude to crack increases with increasing thickness of piezoelectric layer and decreasing material gradient index. The obtained numerical results have proved that mechanical and electrical responses are spectrally identical and similarly sensitive to crack. Conclusion: Moving load speed and frequency are the control parameters useful for obtaining the frequency response preferred for crack detection purpose; eigenmode vibration amplitude at generic resonance provides an efficient scalar indicator for crack detection like natural frequency; as a more easily gathered signal in the practice, the electrical response would be a useful alternative signature for crack detection in FGM beams using a distributed piezoelectric sensor. [ABSTRACT FROM AUTHOR]

Composite bonded structure is a prevalent portion of today's aircraft structure. Adequate bond integrity is a critical aspect of fabrication and service, especially since many of today's structural bonds are critical for flight safety. Over the last decade, non-destructive bond evaluation techniques have improved but still cannot detect a structurally weak bond that exhibits full adherend/adhesive contact. The result is that expensive and time-consuming structural proof testing continues to be required to verify bond integrity. The objective of this work is to investigate the feasibility of bondline integrity monitoring using piezoelectric sensors, embedded at different locations within the composite joint, and to assess the benefits of monitoring the thickness mode in addition to the radial mode. Experiments and analyses are performed on single lap shear composite joints, with and without embedded sensors, subjected to incrementally increasing tensile loads. The results indicate that the embedded piezoelectric sensors measure a change in the resonance in both the radial and thickness mode during incremental loading and that the thickness resonance shows enhanced sensitivity to impending failure. Thus, it is demonstrated that monitoring both modes of the piezoelectric sensor provides addition details for prognostic performance evaluation. [ABSTRACT FROM AUTHOR]

Structural health monitoring technology can assess the status and integrity of structures in real time by advanced sensors, evaluate the remaining life of structure, and make the maintenance decisions on the structures. Piezoelectric materials, which can yield electrical output in response to mechanical strain/stress, are at the heart of structural health monitoring. Here, we present an overview of the recent progress in piezoelectric materials and sensors for structural health monitoring. The article commences with a brief introduction of the fundamental physical science of piezoelectric effect. Emphases are placed on the piezoelectric materials engineered by various strategies and the applications of piezoelectric sensors for structural health monitoring. Finally, challenges along with opportunities for future research and development of high-performance piezoelectric materials and sensors for structural health monitoring are highlighted. [ABSTRACT FROM AUTHOR]

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Summary: Structural health monitoring and natural control (SHMC) for post‐earthquake realignment and repairs (PERR) are one of the most challenging issues facing earthquake engineers worldwide. Currently, neither SHMC nor PERR are parts of contemporary curricula and codes of practice. SHMC aims to help achieve a viable degree of structural sustainability (SS) under predictable environmental conditions. In the present context, SHMC refers to the effort that aims at achieving structural operability before, during, and after severe earthquakes. SHMC is generally associated with the use of piezoelectric sensors and similar devices to measure changes in stresses and strains and detect flaws within the elements of engineering structures. Regardless of the effectiveness of the SHMC systems, no structure can lend itself well to PERR unless it has been designed specifically for the purpose; otherwise, it would be disposable with no gains from the SHMC effort. A seismically sustainable structure can prevent actual collapse, overcome residual effects, and lend itself well to PERR. The purpose of the current article is to introduce a practical basis for efficient use of SHMC concepts in multi‐objective earthquake resisting structures (ERSs). Replaceable energy dissipating moment connections (REDMC), rigid rocking cores (RRCs), high strength tendons, and support level grade beams have been introduced as instruments of natural structural control. The use of monitoring devices has been extended to the evaluation of the effects of formations or elimination of plastic hinges and the variations of the global drift of the system. [ABSTRACT FROM AUTHOR]

Accurate fault detection and monitoring are crucial for maintaining photovoltaic (PV) system performance. While previous studies mainly focused on PV system faults, they often lack a comprehensive approach to integrating advanced diagnostic techniques, leading to duplicated research efforts and insufficient exploration of novel methodologies. This paper investigates the use of the finite element method to simulate the electromechanical impedance technique for fault detection and classification in PV systems. A 3D finite element model of a photovoltaic panel was created using ANSYS software to understand the basics of this technique. Studies on different locations of structural cracks were conducted to assess their impact on PV system output. For model verification, various fault and normal state simulation datasets were collected, normalized using data from piezoelectric sensors, and preprocessed. These datasets were then fed into an extreme learning machine (ELM) algorithm designed to predict and classify damage locations. The results highlight the superior efficacy of the ELM algorithm in defect detection, boasting an impressive overall accuracy rate of 85%. [ABSTRACT FROM AUTHOR]

Aluminum structures are routinely used in aircraft due to their lightweight and corrosion resistance properties. Multi-layered aluminum plates are generally joined by rivets forming regions which are prone to fatigue crack formation in an aircraft. Therefore, the detection and monitoring of fatigue cracks at rivet joints in aluminum structures are crucial for ensuring flight safety. In this study, piezoelectric sensors were utilized to generate and detect Lamb waves on aluminum plates with rivet joints to investigate the feasibility of a newly developed Sideband Peak Count (SPC) technique for detecting fatigue cracks around these joints. To overcome the limitations of existing SPC-I (Sideband Peak Count – Index) and SPI (Sideband Peak Intensity) techniques in capturing harmonic and modulating wave frequencies due to material nonlinearity, a comprehensive index, the Sideband Intensity Index (SII) is introduced. Comparative analysis with existing SPC-I and SPI techniques confirm the effectiveness of the SII technique. This investigation shows that the SII technique significantly improves the detection capability of initial fatigue cracks around rivet joints on aluminum plates. This study offers a more efficient method for detecting critical fatigue cracks in rivet joints. [ABSTRACT FROM AUTHOR]

• Acoustoelastic effect of bending prestress determined using the Partial Wave Method. • Wave structure in certain frequency range analyzed. • Change in phase velocity analytically estimated. • Phase shift experimentally validated. • Effect of small temperature change compensated for load estimation. Although guided waves offer great potential for monitoring various structures, interpreting signals from piezoelectric sensors remains a challenging task. One main reason is the significant influence of environmental conditions on the wave propagation. A lot of research has already been done on the influence of temperature effects and recently more attention has been shifted towards loads. While previous publications have mainly focused on uni- or bi-directional loads, this publication expands the developed models to include bending loads. After reviewing the analytical basis of acoustoelasticity, the derived equations are expanded to nonhomogeneous elastic bending loads using the partial wave method. The analysis is completed using recent results developed by C. Hakoda and C. J. Lissenden (2018) [1] , that gave more physical insight in the propagation of guided waves in various frequency-bands. The focus of the experimental analysis is around the fundamental S0- and A0-Modes of Lamb waves. To validate the analytical results an aluminum plate is instrumented using piezoelectric transducers and loaded with varying bending loads. The experimental results are in good agreement with the analytical theory and demonstrate the influence of bending prestress on guided wave propagation. Based on these results an innovative measurement method for bending loads is developed, that is robust to small temperature changes. [ABSTRACT FROM AUTHOR]

Commercially available photopolymer resins can be combined with lead zirconate titanate (PZT) micrometer size piezoelectric particles to form 3D-printable suspensions that solidify under UV light. This in turn makes it possible to realize various non-standard sensor geometries which might bring benefits, such as increased piezoelectric output in specific conditions and less interference with incoming waves due to better acoustical adaptation compared to solid PZT ceramics. However, it is unclear whether piezoelectric composite materials are suitable for guided ultrasonic wave (GUW) detection, which is crucial for structural health monitoring (SHM) in different applications. In this study, thin piezoelectric composite sensors are tape casted, solidified under UV light, covered with electrodes, polarized in a high electric field and adhesively bonded onto an isotropic aluminum waveguide. This approach helps to demonstrate the capabilities of tape casting's freedom to manufacture geometrically differently shaped, thin piezoelectric composite sensors for GUW detection. In an experimental study, thin two-dimensional piezoelectric composite sensors demonstrate successful detection of GUW for frequency-thickness products of up to 0.5 MHz mm. An analytical calculation of the maximum and minimum amplitudes for the ratio of the wavelength and the sensor length in wave propagation direction shows good agreement with the sensor-recorded signals. The output of the piezoelectric composite sensors and occurring reflections as measure for wave interactions are compared to commercial piezoelectric discs to evaluate their performance. [ABSTRACT FROM AUTHOR]

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results. [ABSTRACT FROM AUTHOR]

Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy of these estimates. Therefore, these uncertainties should be considered, as a deterministic approach may lead to erroneous decisions. In this article, a novel data-driven stochastic Kriging-based method for impact location and maximum force estimation, that is able to reliably quantify the output uncertainty is presented. The method utilises a novel modification of the kriging technique (normally used for spatial interpolation of geostatistical data) for statistical pattern matching and uncertainty quantification using Lamb wave features to estimate the location and maximum force of impacts. The data was experimentally obtained from a composite panel equipped with piezoelectric sensors. Comparison with a deterministic benchmark method developed in prior studies shows that the proposed method gives a more reliable estimate for experimental impacts under various simulated environmental and operational conditions by estimating the uncertainty. The developed method highlights the suitability of data-driven methods for uncertainty quantification, by taking advantage of the relationship between data points in the reference database that is a mandatory component of these methods (and is often seen as a disadvantage). By quantifying the uncertainty, there is more information for operators to reliably locate impacts and estimate the severity, leading to robust maintenance decisions. [ABSTRACT FROM AUTHOR]

As extreme events increase in frequency, flow-disrupting large-scale structures become ever more susceptible to collapse due to local scour effects. The objective of this study was to validate the functionality of passive, flow-excited scour sensors that can continue to operate during an extreme event. The scour sensors, or piezo-rods, feature continuous piezoelectric polymer strips embedded within and along the length of slender cylindrical rods, which could then be driven into the soil where scour is expected. When scour erodes away foundation material to reveal a portion of the piezo-rod, ambient fluid flow excitations would cause the piezoelectric element to output a voltage response corresponding to the dynamic bending strains of the sensor. The voltage response is dependent on both the structural dynamic properties of the sensor and the excitation fluid's velocity. By monitoring both shedding frequency and flow velocity, the exposed length of the piezo-rod (or scour depth) can be calculated. Two series of experimental tests were conducted in this work: (1) the piezo-rod was driven into sediment around a mock pier to collect scour data, and (2) the piezo-rod was used to monitor its own structural response by collecting vortex-shedding frequency data in response to varied flow velocities to establish a velocity–frequency relationship. The results showed that the piezo-rod successfully captured structural vortex-shedding frequency comparable to state-of-practice testing. A one-dimensional numerical model was developed using the velocity–frequency relationship to increase the accuracy of voltage-based length prediction of the piezo-rod. Two-dimensional flow modeling was also performed for predicting localized velocities within a complex flow field. These velocities, in conjunction with the velocity–frequency relationship, were used to greatly improve length-predictive capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • The CNN-LSTM model based on acoustic emission signals enables end-to-end prediction for CFRP impact damage diagnosis. • Data reconstruction is performed on the acoustic emission feature signals after convolution operations to extract sequential information from them. • CNNs that incorporate recurrent units (RNN, LSTM, and GRU) outperform basic CNNs. The CNN-LSTM architecture exhibits the best performance. • The sensitivity experiment indicates the CNN-LSTM model obtain best prediction when sub-signal length is 16. This research has developed a real-time end-to-end deep learning model for structural health monitoring (SHM) method for composite impact damage diagnosis based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The acoustic emission (AE) signals collected under low-velocity impacts by means of piezoelectric sensors on composite materials are used for training deep learning networks. Based on the impact load curves, specimens are categorized into minor failure, intermediate failure, and severe failure. The convolved signals are segmented and reconstructed at a given length for the following LSTM module. The average accuracies for basic CNN, CNN– Recurrent Neural Network (RNN), CNN-LSTM, and CNN– Gated Recurrent Unit (GRU) are respectively 88.7 %, 92.6 %, 98 %, and 95.4 %. A sensitivity analysis on sub-signal length was conducted on the CNN-LSTM model, revealing that the model achieved its best performance when the sub-signal length was set at 16. The model attained prediction accuracies of 97.4 %, 100 %, and 100 %, respectively, for minor failure, intermediate failure, and severe failure cases. [ABSTRACT FROM AUTHOR]

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT—LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices. • Nano-enhanced Sensor Accuracy: Precise early-age concrete strength monitoring. • Piezoelectric EMI Application: Tracks concrete curing mechanical changes. • NDT-LSTMs-ANN Hybrid: Strength estimation with enhanced piezoelectric sensors. • Monitoring Precision Boost: Nano-sensors heighten construction safety and efficiency. [ABSTRACT FROM AUTHOR]

Lamb wave-based structural health monitoring requires fast and accurate simulation tools for guided wave propagation in structures. In this article, a computationally efficient multiphysics time-domain spectral element (TDSE) model is presented for actuation, propagation, and sensing of Lamb waves in laminated composite strips (beams and panels) integrated with surface-bonded or embedded patch piezoelectric actuators and sensors. The transducers are physically modelled using an electromechanically coupled formulation, which combines an efficient layerwise zigzag theory for kinematics with a layerwise distribution for the electric potential field. The high-order TDSE has n physical nodes containing mechanical and electric degrees of freedom (DOFs) and a separate virtual electric node for modelling the transducers' equipotential surfaces with significantly reduced DOFs. The continuity of the displacement fields at the intersections (patch fronts) of the hybrid laminate integrated with patch transducers with the host laminate is maintained using the advanced hybrid point-least squares continuity model. The present guided wave predictions show excellent agreement with experimental measurements as well as continuum-based finite element solutions for isotropic and composite strips with surface-bonded piezo patches. The patch front continuity is shown to have a significant effect on the accuracy. The model is used to study the interaction of guided waves with the notch-type damage. The results establish the superiority of the proposed TDSE model over its standard two-node counterpart in terms of accuracy and efficiency. On the other hand, the equivalent single-layer theory-based model with the same number of DOFs yields highly erroneous predictions. • Presents an efficient multiphysics spectral element for smart composite strips. • First electromechanical spectral element based on an efficient layerwise theory. • Enforces continuity of nonlinear displacement field at piezo patch intersection. • Employs virtual electric node to satisfy equipotential conditions on piezo surfaces. • Enables fast, accurate simulation for Lamb wave-based structural health monitoring. [ABSTRACT FROM AUTHOR]

The article presents a literature review of electromechanical impedance spectroscopy for structural health monitoring, with emphasis in adhesively bonded joints. The concept behind electromechanical impedance spectroscopy is to use variable high-frequency structural vibrations with piezoelectric elements to monitor the local area of a structure for changes in mechanical impedance that may indicate imminent damage. Various mathematical models that correlate the structural impedance with the electric response of the piezoelectric sensors are presented. Several algorithms and metrics are introduced to detect, localize, and characterize damage when using electromechanical impedance spectroscopy. Applications of electromechanical impedance spectroscopy to study adhesive joints are described. Research and development of alternative hardware for electromechanical impedance spectroscopy is presented. The article ends by presenting future prospects and research of electromechanical impedance spectroscopy–based structural health monitoring, and, while advances have been made in algorithms for damage detection, localization, and characterization, this technology is not mature enough for real-world applications. [ABSTRACT FROM AUTHOR]

Structural health monitoring has become an important factor in the assessment of defects/damage in material components. Ultrasonic methods generally incorporate a sparse array of sensors/transducers as they provide a low number of piezoelectric sensors per area, thus providing savings with regard to system costs and weight. Many structural health monitoring techniques rely on linear ultrasonic effects such as reflections, amplitude changes, time of arrival and wave scattering effects which rely on precise baseline measurements. In this work, a nonlinear ultrasonic method based on a sparse array of surface-bonded ultrasonic transducers was used to evaluate the second harmonic and modulated elastic responses from a damaged medium. A complex composite stiffened panel with barely visible impact damage was evaluated. The points closest to damage are found on the paths between transmitter–receiver pairs through a reciprocal relationship of nonlinear elastic parameters and a statistical approach was used to select a cloud of points so that a two-dimensional image of the damaged region is created. Experimental results revealed that the second-order nonlinear parameter provided accurate damage localisation and imaging and the use of modulation bands further improved imaging accuracy and damage localisation. The maximum error between the calculated and the real damage area centres was only 1.3 mm. The proposed nonlinear elastic multi-path imaging technique based on higher harmonic generation and modulation coupled with a statistical approach provides damage detection without a priori knowledge of the material characteristics, which is in direct contrast to conventional linear ultrasonic methods which rely on precise measurement of elastic wave effects both before and after the initiation of damage. [ABSTRACT FROM AUTHOR]