Understanding Impedance Response Characteristics of a Piezoelectric‐Based Smart Interface Subjected to Functional Degradations

Electromechanical impedance technique has been widely used in the area of structural health monitoring. However, both damage and variation in environmental temperature can cause the changes in electromechanical impedance signature, which may cause false damage diagnosis. The temperature effect on the electromechanical impedance-based method has been one of its main drawbacks in practical application. This article proposes a new approach based on cointegration to eliminate temperature interference in the electromechanical impedance technique. The augmented Dickey–Fuller test is used to analyze the stationary characteristics of the time series and determine the degree of non-stationarity. The Johansen test is used to obtain the cointegrating residuals instead of the direct electromechanical impedance responses for damage detection. The proposed method is verified on the undamaged and damaged steel plates with the consideration of environmental temperature variations. The damage detection was based on the electromechanical impedance technique in which the peak frequency is chosen as a cointegrated variable. The experimental results show that the cointegration method can remove the temperature effect on the electromechanical impedance responses, and the cointegrating residuals are effective indices to indicate the occurrence of damage.

This research proposed and verified an innovative method to identify and locate structural damage using only the response of operational vibration, that is the displacement acquired by a non-contact optical method. The most efficient and economical way to detect damage within the structure is to monitor its structural health while in operation. However, the uncertainties and the randomness of ambient vibrations due to the operation and environments cause a challenge in conducting the operational analysis. Current technology limits the ability to collect data on the properties of the structure without the interruption of operation. Frequencies and mode shapes have been widely used in structural damage detection, but they are not sensitive enough and cannot provide sufficient information for identifying damage locations and their quantification. Therefore, the goal of this research is to design and verify a method to detect the damage, as well as its location and severity, of structures in operation without any physical contacts for data acquisition (i.e., non-contact based structural health monitoring (SHM)). Three algorithms are integrated into this SHM process. The first algorithm is the determination of structural characteristics (frequencies and mode shapes) of a vibrating structure from output-only data. Stochastic Subspace Identification (SSI) method is applied to measured displacements over time to extract the structural characteristics. The second algorithm is to estimate the scaling factor. The mode shapes obtained from the output-only model analysis are unscaled due to the absence of the information of input excitation forces. Mass Change Modal Scale (McMS) algorithm is used to estimate the modal scaling factors and determine the scaled mode shapes. The third algorithm is to estimate the structural system matrices (i.e., mass and stiffness matrices) and assess the damages. A Finite Element Model Updating (FEMU) is applied and the system matrices are updated from frequencies and scaled mode shapes. The damage within the structure can then be detected by analysing changes in mass and stiffness matrices. All three phases are verified by numerical simulation and laboratory experiments with deflections acquired by non-contact optical methods through video system. At last, to achieve the non-contact based SHM, a modal scaling method based on temperature change is proposed and verified by numerical simulation. Experimental program reveals that the proposed algorithm using McMS method is applicable to detect damage locations and their mass losses. With proposed non-contacted based SHM, the limitations of contact based sensor can be addressed, and the structural damage

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

Artificial neural network (ANN), a powerful technique, has been used widely over the last decades in many scientific fields including engineering problems. However, the backpropagation algorithm in ANN is based on a gradient descent approach. Therefore, ANN shows high potential in local stagnancy. Besides, choosing the right architecture of ANN for a specific issue is not an easy task to deal with. This paper introduces a simple, effective hybrid approach between an optimization algorithm and a traditional ANN for damage detection. The global search-ability of a heuristic optimization algorithm, namely grey wolf optimizer (GWO), can solve the drawbacks of ANN and also improve the performance of ANN. Firstly, the grey wolf optimizer is used to update the finite element (FE) model of a laboratory steel beam based on the vibration measurement. The updated FE model of the tested beam then is used to generate data for network training. For an effective training process, GWO is utilized to identify the optimal parameters for ANN, such as the number of the hidden nodes, the proportion of dataset for training, validation, test, and the training function. The optimization process provides an optimal structure of ANN that can be used to predict the damages in the beam. The obtained results confirm the accuracy, effectiveness, and reliability of the proposed approach in (1) alleviating the differences between measurement and simulation and (2) damage identification including damage location and severity, in the tested beam considering noise effects. For both applications, dynamic characteristics like natural frequencies and mode shapes of the beam derived from the updated FE model, are collected to calculate the objective function

A method for continuous box-girder bridge structure finite element (FE) model updating based on static-load testing is proposed. The procedure includes field static-load testing, FE modeling, FE model (parameter) updating, etc. Based on this procedure, the structure FE model updating for an expressway 5-span pre-stressed concrete continuous box-girder bridge is conducted. And the bridge structure static FE model, which is consistent with the static-load testing actual measure response, is gained. The updated FE model (parameter) is foundation of structural performance evaluation for the bridge.

• Optimal reduced-order vehicle-bridge interaction model. • Reliable Structural Health Monitoring (SHM) and damage detection. • VBI computational framework with significantly reduced computational cost. • Enriched model with realistic rail irregularities. • Experimental validation over multiple operating speeds, loads, and damage scenarios. Bridge structures subjected to moving vehicle loads exhibit complex dynamic behavior influenced by structural flexibility and rail surface irregularities. The correct approach and optimal modeling of vehicle-bridge interaction effects are crucial for the accurate computational determination of dynamic bridge vibrations, for reliable Structural Health Monitoring (SHM) and damage detection. The purpose of this work is twofold. First, a novel computational framework is introduced to generate an optimal reduced-order Vehicle-Bridge Interaction (VBI) model that captures the essential dynamics of the system with significantly reduced computational cost. This process begins with the development of a high-fidelity finite element (FE) model of the bridge which is updated using experimental modal data. This model is then reduced using Component Mode Synthesis (CMS) to enable efficient simulation while preserving the essential dynamic behavior and is combined with a simplified vehicle model enriched with realistic rail irregularities, producing an accurate and computationally efficient VBI system. Second, the study introduces a classification framework based on a lightweight Convolutional Neural Network (CNN) trained on numerically generated data from the validated VBI model. The CNN operates on frequency-domain features derived from bridge acceleration responses, which reflect damage-induced changes in stiffness and energy distribution of the structure. Using these features and an ensemble of independently trained CNNs, the framework classifies the structural health condition of the bridge.

This study introduces a Bayesian model updating approach for analyzing the long-term performance of steel–concrete composite beams (SCBs). Two nominally identical SCBs (SCB-1 and SCB-2) were designed, fabricated, and subjected to modal testing. Despite their identical design parameters, notable differences were observed in their frequencies and mode shapes during testing. Initial finite element model (FEM) analyses, developed under testing conditions, revealed notable discrepancies between theoretically computed values and the results of modal testing. To resolve these differences, the FEM was updated using the Bayesian approach, integrating the dynamic test data to enhance model accuracy. The updated FEM was subsequently employed to assess the long-term performance of the SCBs, with a particular focus on the time-dependent effects of concrete shrinkage and creep in deflection calculations. The findings reveal substantial differences in the long-term deflection predictions between the updated and initial FEM. Specifically, the long-term deflection of SCB-1 increased by 13.1%, whereas that of SCB-2 decreased by 9.8%, leading to an overall difference of 25.3% between the two beams. These findings underscore the considerable impact of fabrication errors and material inhomogeneities on structural performance, highlighting the limitations of initial FEM based solely on test parameters in accurately capturing actual behavior. Consequently, the study emphasizes the critical role of model updating in accurately predicting the long-term performance of SCBs.

Details of the investigations on an unexplored application of Electro Mechanical Impedance (EMI) technique using smart piezoelectric (PZT) sensors for damage detection of concrete shear wall structures under crack opening and closing is presented in this paper. The behavior and the ability of this method to detect damages, in a heterogeneous quasi-brittle material is studied for its effective utilization in structural health monitoring. The paper discusses the experimental investigations conducted on a concrete shear wall using PZT patches. Conductance data is acquired at different applied lateral displacements of shear wall. Damage in- dex is calculated using Root Mean Square Deviation (RMSD), Mean Absolute Per- centage Deviation (MAPD) and Correlation Coefficient (CC) computed between pristine and damaged states. It is found that compared to a metallic specimen, concrete specimens do not show a large variation in these damage metrics, due to reduced modal density and high damping. However, results and observations from these experimental case studies establish that EMI signature from surface bonded PZTs can identify damages even for a heterogeneous material like concrete, with improved and modified damage metrics.

Electromechanical impedance (EMI) method is an effective and powerful technique in structural health monitoring (SHM) which couples the mechanical impedance of host structure with the electrical impedance measured at the piezoelectric wafer active sensor (PWAS) transducer terminals. Due to the electromechanical coupling in piezoelectric materials, changes in structural mechanical impedance are reflected in the electrical impedance measured at the PWAS. Therefore, the structural mechanical resonances are reflected in a virtually identical spectrum of peaks and valleys in the real part of the measured EMI. Multi-physics based finite element method (MP-FEM) has been widely used for the analysis of piezoelectric materials and structures. It uses finite elements taking both electrical and mechanical DOF’s into consideration, which allows good differentiation of complicated structural geometries and damaged areas. In this paper, MP-FEM was then used to simulate PWAS EMI for the goal of SHM. EMI of free PWAS was first simulated and compared with experimental result. Then the constrained PWAS was studied. EMI of both metallic and glass fiber composite materials were simulated. The first case is the constrained PWAS on aluminum beam with various dimensions. The second case studies the sensitivity range of the EMI approach for damage detection on aluminum beam using a set of specimens with cracks at different locations. In the third case, structural damping effects were also studied in this paper.. Our results have also shown that the imaginary part of the impedance and admittance can be used for sensor self-diagnosis.

In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite element model. The proposed method is applied in a linear steel substructure of a lignite grinder assembly at a Public Power Corporation power plant. A finite element model of the steel base is developed and updated to match the dynamic characteristics measured in real operating conditions. This is achieved through coupled use of numerical and experimental methods for identifying, updating, and optimizing a high-fidelity finite element model. The full stress time histories of the complex mechanical assembly are estimated, at critical locations, by imposing operational vibration measurements from a limited number of sensors in the updated finite element model. Fatigue damage and remaining lifetime is subsequently estimated via commonly adopted engineering approaches, such as Palmgren–Miner damage rule, S–N curves, and rainflow cycle counting. Incorporation of a numerical model of the structure in the response estimation procedure permits stress estimation at unmeasured locations, thereby enabling the drawing of a complete and substantially dense fatigue map consistent with the vibration measurements. Fatigue predictions via the proposed framework are highly correlated to experimental fatigue results, proving the efficiency and applicability of the framework.

This thesis is devoted to present some novel techniques in Structural Health Monitoring (SHM). SHM is a developing field that tries to monitor structures to make sure that they remain in their desired condition to avoid any catastrophe. SHM includes different levels from damage detection area to prognosis field. This work is dedicated to the first level, which might be considered the main and most important level. New techniques presented in this work are based on different statistical and signal processing methods such as Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc. These techniques are applied on the propagated waves that are activated and captured in the structure using appropriate transducers. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. To guarantee the efficiency of the suggested techniques, they are tested on different laboratory and real scale test benchmarks, such as aluminum and composite plates, fuselage, wing skeleton, tube, etc. Because of the variety of tested benchmarks, this thesis is called damage detection in smart structures. This variety may promise the ability and capability of the proposed methods on different fields such as aerospace and gas/oil industry. In addition to the normal laboratory conditions, it is shown in this work that environmental changes can affect the performance of the damage detection and wave propagation significantly. As such, there is a vital need to consider their effect. In this work, temperature change is chosen as it is one of the main environmental fluctuation factors. To scrutinize its effect on damage detection, first, the effect of temperature is considered on wave propagation and then all the proposed methods are tested to check whether they are sensitive to temperature change or not. Finally, a temperature compensation method is applied to ensure that the proposed methods are stable and robust even when structures are subjected to variant environmental conditions. La presente tesis doctoral se dedica a la exploración y presentación de técnicas novedosas para la Monitorización y detección de defectos en estructuras (Structural Health Monitoring -SHM-) SHM es un campo actualmente en desarrollo que pretende asegurarse que las estructuras permanecen en su condición deseada para evitar cualquier catástrofe. En SHM se presentan diferentes niveles de diagnóstico, Este trabajo se concentra en el primer nivel, que se considera el más importante, la detección de los defectos. Las nuevas técnicas presentadas en esta tesis se basan en diferentes métodos estadísticos y de procesamiento de señales tales como el Análisis de Componentes Princpales (PCA) y sus variaciones robustas, Transformada wavelets, lógica difusa, gráficas de Andrew, etc. Estas técnicas de aplican sobre las ondas de vibración que se generan y se miden en la estructura utilizando trasductores apropiados. Dispositivos piezocerámicos (PZT's) se han escogido para este trabajo ya que presentan características especiales tales como: alto rendimiento, bajo consumo de energia y bajo costo. Para garantizar la eficacia de la metodología propuesta,se ha validado en diferentes laboratorios y estructuras a escala real: placas de aluminio y de material compuesto, fuselage de un avión, revestimiento del ala de un avóin, tubería, etc. Debido a la gran variedad de estructuras utilizadas, su aplicación en la industria aeroespacial y/o petrolera es prometedora. Por otra parte, los cambios ambientales pueden afectar al rendimiento de la detección de daños y propagación de la onda significativamente . En este trabajo , se estudia el efecto de las variaciones de temperatura ya que es uno de los principales factores de fluctuación del medio ambiente . Para examinar su efecto en la detección de daños, en primer lugar, todos los métodos propuestos se prueban para comprobar si son sensibles a los cambios de temperatura o no. Finalmente , se aplica un método de compensación de temperatura para garantizar que los métodos propuestos son estables y robustos incluso cuando las estructuras se someten a condiciones ambientales variantes

Purpose This study aims to evaluate the feasibility of using piezoelectric lead zirconate titanate (PZT) sensors to monitor the effects of adding low-density polyethylene (LDPE) beads, made from waste plastic, on carbonation mineralization of modified concrete during accelerated CO2 curing. Design/methodology/approach Two concrete mixtures (P20 and P40) were prepared by replacing 20% and 40% of coarse aggregates with LDPE beads. PZT patches installed in both surface-bonded and embedded configurations were employed to capture electromechanical impedance (EMI) responses experimentally, in the user-defined 250–350 kHz frequency ranges at ten different curing ages of 0, 1, 3, 7, 10, 14, 18, 21, 24 and 28 days from plastic waste admixed-concrete mixes, and were further validated using finite element analysis. Findings The results indicate that carbonation curing significantly influences the EMI responses with increasing plastic waste content in concrete. Embedded PZT sensors exhibited higher sensitivity than surface-bonded sensors, showing maximum root mean square deviation (RMSD) and mean absolute percentage deviation (MAPD) values of up to 39.23% and 75.90%, respectively, for P40 mixes at 28 days curing duration. In contrast, surface-bonded sensors demonstrated lower deviations, with maximum RMSD and MAPD values of 7.69% and 14.28%. Originality/value This study demonstrates that the inert solid plastic additions up to 40% significantly increase the sensitivity of health indices, conclusively showing an intensified carbonation effect due to increased porosity and weak interfaces, which are ultimately correlated to strength gains with higher confidence levels.

An efficient method for constructing multiple candidate finite element (FE) models which are consistent with the measured dynamic properties of a civil structure is presented. A parameterised sensitivity-based FE model updating method was developed to permit a feasible FE model set for the target structure to be produced. In this method, an aggregated multi-objective function, which is defined by the weighted sum of the error functions, is parameterised by its relative weighting factors. By introducing multiple parameter sets for the weighting factors, a number of optimal updated FE models are produced that permit both the natural frequency errors and mode shape errors to be simultaneously minimised. The effectiveness of the proposed method is illustrated by an example using an existing cable-stayed bridge. The findings show that error-minimised, well-distributed FE models can be obtained in terms of modal frequency errors and mode shape errors. The high quality of the candidate model sets is also verified by observations showing that the distributions of the structural parameters are consistent through each updated FE models, and the characteristic features of the target structure such as non-symmetric mode shapes are relatively well captured.

In this paper, a finite element model updating (FEMU) method is proposed based on the response surface model (RSM) and genetic algorithm (GA) to establish a high-precision finite element (FE) model of space station scientific experiment racks. First, the fine solid and mixed FE models are established, respectively, and a comparison of the modal test results is conducted. Then, an orthogonal experimental design is used to analyze the significance of the parameters, and the variables to be modified are determined. The design parameters are sampled via the Latin hyperbolic method and are substituted into the FE model to obtain the modal parameters of the scientific experiment rack. The mapping relationship between the design and modal parameters is fitted by constructing the Kriging function, and the RSM is established. The design parameters of the scientific experiment rack are optimized via GA, and the initial FE model is updated, which has the advantage of improving the computing efficiency. Finally, the updated FE model of the experiment rack is verified by frequency sweep and random vibration tests. The experimental results show that the proposed approach has high precision and computing efficiency, and compared with the test results, the modal frequency errors of the updated model are within 5%, and the vibration response errors under random excitation of the updated model are within 7%.

ABSTRACTThe electro-mechanical impedance (EMI) method has been accepted as an effective technique for detecting damages in the Structural health monitoring (SHM). EMI at any point of the structure depends on material properties, geometry and boundary conditions that all appears in dynamic stiffness of the structure. In spite of the expensive experimental methods for measuring the mechanical impedance, or the cheaper one electromechanical impedance, of structures, various analytical methods could be substitutions for them. In this paper, an analytical method is developed to obtain the EMI response of L-shaped beams through calculating the dynamic stiffness of the structure. To verify the model, an experimental setup with an embedded piezoelectric wafer active sensor (PWAS) is carried out. The results have shown that EMI and its real part, extracted by the current analytical method, are with good agreement of the experimental results. Also, the dynamic stiffness of the structure directly depends on the mode shapes of the structure and its natural frequencies in terms of the excitation frequency. The peaks of the real part of the EMI results related to the coincidence between agitation frequency value and natural frequencies of the structure.