Non-destructive Testing Methods Research Articles

Bricks non-destructive simulation testing method utilizing neutron radiography facility based on a 7Li(p,n)7Be reaction

The evaluation of non-destructive neutron radiography (NR) for examining the internal composition of various structural materials, has been the focus of extensive research. This manuscript uses non-destructive testing to generate three-dimensional radiographs of three different brick structural materials: glass block, magnesia-chrome, and lead to evaluate their capability to withstand fast neutrons and gammas emitted from a source. When neutrons with thermal or epithermal spectrum are required, the optimum combination for an accelerator was simulated using a 2.8 MeV proton beam on a lithium target. The presented facility tested both thermal and fast neutron radiography. This study examined various aperture diameters and collimator lengths. It found that implementing a special fast neutron filter significantly increased the thermal neutron content (TNC) with minimal impact on the thermal neutron flux. For fast neutron radiography, the study evaluated parameters such as geometric unsharpness, fast neutron flux, and the percentage of the uncollided fast neutron reaching the object. Both neutrons and photons from the source were used to inspect faults in a glass brick.

Strain Measurement for Composite Materials During Mechanical Testing Based on Fiber Bragg Grating Monitoring

Composite materials are widely used as repair materials in the oil and gas sector, particularly for vessels, piping, and pipelines. Inspection and monitoring of these composite repairs is a critical aspect of ensuring their integrity and long-term performance. The reason being that failure of composite repairs may lead to loss of production and incur additional costs for re-repair or equipment replacement. Conventionally, a visual inspection is generally performed to observe any irregularities on the external surface of the composite repairs. This will usually be followed by a series of Non-destructive Tests (NDT) to identify the extent of the damage. However, these NDT methods, merely allowing off-line testing in a local manner with complicated and heavy equipment, require extensive and time-consuming labour. Structural Health Monitoring (SHM) based on Fibre Bragg Grating (FBG) that utilizes strain-based signals is one of the best options for monitoring the damage mechanism in composite due to its unique advantages of light weight, high stability and reliability, a long-life cycle, and the ability to capture the online reading. In this paper, the application of the FBG monitoring approach was used to examine the strain characteristics of composite materials based on mechanical tests (tensile and flexural). Three types of samples were prepared to simulate the type of damage mechanism: healthy, delamination, and disbondment. Based on the results, it was shown that the FBG sensor has potential for detecting and distinguishing the types of failure mechanisms in composites, including cracks, delamination, and disbondment. Hence, it was suggested that the FBG sensor technology could be used for site deployment in industry.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model

BackgroundSince traditional germination test methods have drawbacks such as slow efficiency, proneness to error, and damage to seeds, a non-destructive testing method is proposed for full-process germination of radish seeds, which improves the monitoring efficiency of seed quality.ResultsBased on YOLOv8n, a lightweight test model YOLOv8-R is proposed, where the number of parameters, the amount of calculation, and size of weights are significantly reduced by replacing the backbone network with PP-LCNet, the neck part with CCFM, the C2f of the neck part with OREPA, the SPPF with FocalModulation, and the Detect of the head part with LADH. The ablation test and comparative test prove the performance of the model. With adoption of germination rate, germination index, and germination potential as the three vitality indicators, the seed germination phenotype collection system and YOLOv8-R model are used to analyze the full time-series sequence effects of different ZnO NPs concentrations on germination of radish seeds under varying degrees of salt stress.ConclusionsThe results show that salt stress inhibits the germination of radish seeds and that the inhibition effect is more obvious with the increased concentration of NaCl solution; in cultivation with deionized water, the germination rate of radish seeds does not change significantly with increased concentration of ZnO NPs, but the germination index and germination potential increase initially and then decline; in cultivation with NaCl solution, the germination rate, germination potential and germination index of radish seeds first increase and then decline with increased concentration of ZnO NPs.

Development of a High-Sensitivity Millimeter-Wave Radar Imaging System for Non-Destructive Testing.

There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye.

Detection of near-surface defects in viscoelastic material based on focused ultrasound thermal effects

Viscoelastic materials will absorb and dissipate energy under stress, resulting in energy loss and heat generation. The conventional non-destructive testing methods have certain limitations when it comes to detecting near-surface defects in viscoelastic materials. In this paper, a detection method of near-surface defects based on focused ultrasonic thermal effect is proposed. Firstly, the difference in thermal effects caused by defective and non-defective regions of the material under ultrasound is analyzed according to the stress response equation of viscoelastic materials, and the detection principle is elucidated. Secondly, the feasibility of this method is verified through finite element simulation with an example of plexiglass material Subsequently, the variations in the surface temperature distribution of defective specimens with varying diameters and depths are analyzed. Finally, experimental validation reveals that ultrasonic waves operating at 1.12 MHz successfully detect artificial defects with a diameter of 1 mm. With the increase of the equivalent diameter of the defect, the width of the low-temperature depression area in the surface temperature field exhibits a linear increase relationship. With the increase of the defect depth, the surface temperature difference between the corresponding position of the defective and the surrounding non-defective area gradually decreases. This method effectively overcomes the half-wavelength limitation and introduces a novel detection approach for near-surface defect identification in viscoelastic materials such as plexiglass.

Surface defect visualisation using scanning electromagnetic induction thermography

ABSTRACT Scanning electromagnetic induction thermography (SEIT) has great potential for industrial applications, such as rapid visualising of surface cracks. This dynamic non-destructive testing method has improved detection efficiency but has led to blurred or discontinuous defect boundaries at varying scanning speeds. To alleviate this problem, we explore the use of the patch-based sparse decomposition (PSD) technique. This technique improves dynamic imaging by retaining only the information associated with sparse regression and introducing a locally adaptive threshold. Besides, this method offers a potential thermal image preconditioned scheme for follow-up artificial intelligence detection. Experimental results show that PSD can correct blurred images caused by relative motion, thus improving defect detection accuracy. Moreover, it is suitable for both translational and rotational detection systems. Images reconstructed through the deblurring process demonstrate the ability to visualise holes with a radius of 0.5 mm at velocities up to 150 mm/s, while cracks with a defect width of 0.2 mm on a railroad wheel can be detected at speeds ranging from 25 mm/s to 75 mm/s.

Inspection of aluminum sheets using a multi-element eddy current sensor: 2d and 3d imaging of surface defects of various sizes and internal defects at various depths

In the industrial sector, ensuring reliability and durability is of paramount importance. Our research aims to advance beyond conventional non-destructive testing methods by focusing on thorough defect detection and imaging. We utilize advanced, sensor-enhanced eddy current testing, featuring multiple elements arranged in a cutting-edge serial array. This innovative configuration addresses the issue of magnetic repulsion between sensor elements, thereby speeding up the testing process and ensuring precise results through both 3D and 2D imaging. This sophisticated approach allows us to more effectively characterize defects of varying sizes and depths in aluminum sheets. By meticulously collecting and analyzing data from the sensors, we can identify the appearance and nature of these defects with greater clarity. Our findings introduce a pioneering method for defect detection, highlighting the efficacy of our advanced testing technique. Our research underscores the potential of multi-element eddy current sensors in revolutionizing the inspection process. The ability to produce detailed 3D and 2D images of surface and internal defects represents a significant leap forward in non-destructive testing. This comprehensive imaging capability not only accelerates the detection process but also enhances the accuracy and reliability of defect characterization. By employing this state-of-the-art technology, we can detect even the smallest and most deeply embedded defects that traditional methods might miss. The precise imaging provided by our approach ensures that defects of various sizes and depths are accurately identified and characterized. This level of detail is crucial for maintaining the structural integrity and performance of aluminum sheets used in industrial applications. Our research demonstrates a groundbreaking approach to defect detection in aluminum sheets, leveraging the advanced capabilities of multi-element eddy current sensors. The innovative use of a serial array of sensors, combined with sophisticated data analysis techniques, allows for rapid, accurate, and detailed imaging of defects. These findings pave the way for improved reliability and durability in industrial applications, setting a new standard for non-destructive testing.

Numerical study of micro-dimple depth and stress distribution induced by laser shock waves in visco-elasto-plastic materials

Laser shock peening (LSP) is an advanced surface strengthening technology that uses laser shock waves (LSWs) to induce severe plastic deformation, considerable compressive residual stress, and grain refinement, and thereby improve the fatigue performance of metallic materials. Understanding the spatiotemporal distribution of the stress wave is important for precisely managing the strengthening effect of LSP. In this paper, the stress distribution of LSWs and the equation for LSW-induced residual strain in visco-elasto-plastic materials are presented. The formation of LSW-induced micro dimples on the surface is noteworthy. We derived an approximate equation for the maximum micro-dimple depth induced by LSWs. Finally, we measured the micro-dimple depths induced by LSWs at different peak pressures and verified the reliability of the theoretical calculation by comparing the calculated data with the experimental data. The micro-dimple depth can serve as an indicator of the effectiveness of LSP and improvement in fatigue performance. This characteristic can be utilized as a non-destructive testing method. This study has demonstrated the potential for promoting and applying of LSP in different industries.

Tree internal defects detection method based on ResNet improved subspace optimization algorithm

The erosion behavior of trunk borers leads to the destruction of trunk structure and the formation of internal defects, which significantly impacts the ecological and economic value of trees. Traditional non-destructive testing (NDT) methods are costly and have low resolution, whereas electromagnetic NDT methods are more suitable for high-resolution detection and imaging. However, solving the highly nonlinear electromagnetic inverse scattering problems (ISPs) for small-sized defects with high contrast is challenging. Therefore, this paper proposes an improved subspace optimization algorithm based on a ResNet network called SOM-ResNet. SOM-ResNet incorporates physical principles into deep learning networks by simulating the iterative process of induced current and contrast, thereby enhancing its ability to accurately detect small objects with high contrast. Experimental results demonstrate that SOM-ResNet outperforms single inversion algorithms in detecting complex scatterers with small to medium-sized targets, validating its excellent performance.

Digital twin-based non-destructive testing method for ultimate load-carrying capacity prediction

Load-carrying experiments are essential for validating the mechanical performance of structural designs. Unpredictable early structure collapses during experiments would lead to severe safety accidents, and thus the real-time prediction of the ultimate load-carrying capacity (ULC) becomes highly necessary. However, due to multi-source experiment deviations, ULC prediction methods based on finite element (FE) analysis struggle to meet the high-accuracy and real-time requirements. To address this issue, this paper proposes a digital twin-based non-destructive testing (DT-NDT) method for ULC prediction. The method employs a deep neural network (DNN) to construct an experiment digital twin (DT) model in the offline phase. In the online phase, the experiment DT model utilizes the current measured strains to predict the ULC at future time steps in real-time, thus achieving non-destructive testing (NDT). First, a series of virtual experiments considering multi-source experiment deviations are carried out by FE analysis to obtain the virtual experiment dataset. The dataset includes strains and collapse loads corresponding to different experiment deviations and is augmented using the data augmentation approach based on structural symmetry. Subsequently, DNN is trained by the virtual experiment dataset to establish the mapping of strain values to collapse load. Finally, the strains are measured in real-time using strain gauges and the strain values are inputted into the DNN, i.e., the experiment DT model, to predict the collapse load. In this paper, the principle and effectiveness of the proposed method are illustrated through an analytical example and two experiment examples. In the experiment examples, the average relative errors of the DT-NDT method are 0.9 % for an open-hole plate and -1.45 % for a grid-stiffened cylindrical shell. The results illustrate the high prediction accuracy and effectiveness of the DT-NDT method, demonstrating its immense potential in NDT.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Towards 3D Pore Structure of Porous Gypsum Cement Pozzolan Ternary Binder by Micro-Computed Tomography

A sophisticated characterisation of a porous material structure has been challenging in material science. Three-dimensional (3D) structure analysis allows the evaluation of a material’s homogeneity, pore size distribution and pore wall properties. Micro-computed tomography (micro-CT) offers a non-destructive test method for material evaluation. This paper characterises a novel ternary binder’s porous structure using micro-CT. Gypsum–cement–pozzolan (GCP) ternary binders are low-carbon footprint binders. Both natural and industrial gypsum were evaluated as a major components of GCP binders. Porous GCP binder was obtained by a foaming admixture, and the bulk density of the material characterised ranged from 387 to 700 kg/m3. Micro-CT results indicate that pores in the range from 0.017 to 3.0 mm can be effectively detected and described for porous GCP binders. The GCP binder structure proved to be dominant by 0.1 to 0.2 mm micropores. For GCP binders produced with natural gypsum, macropores from 2.2 to 2.9 mm are formed, while GCP binders with phosphogypsum possess pores from 0.2 to 0.6 mm. Micro-CT proved to be an effective instrument for characterising the homogeneity and hierarchical pore structure of porous ternary binders.

Identification of the quality for rice with different storage humidity: An electronic nose combined with multiblock feature integration method

The ability of traditional attention mechanisms (AMs) to extract useful features from electronic nose (e-nose) data is limited, which affects the performance of the e-nose system for the identification of rice quality. Motivated by this, a nondestructive testing method incorporating an e-nose and multiblock feature integration (MBFI) is proposed to effectively discriminate the quality of rice at different storage humidity. First, gas information for two brands of rice at five storage humidity is acquired using the e-nose system. Second, the feature-mining ability of quality classification models is enhanced by the MBFI module. Finally, compared with the recognition results of multiple AMs, multiple classification models, and ablation analysis, the best identification performance and stability for rice quality are obtained by the MBFI and a residual network18 model. In conclusion, effective identification of rice quality is achieved by the e-nose and MBFI.

Enhancing corrosion detection in pulsed eddy current testing systems through autoencoder-based unsupervised learning

Pulsed Eddy Current Testing (PECT) stands out as an advanced method in Non-Destructive Testing due to its extensive spectrum characteristics in comparison to traditional ECT techniques, making it exceptionally suitable for identifying corrosion. Nonetheless, the analysis of PECT signals for corrosion detection poses a challenge due to the transient nature of these signals and the impact of sensor lift-off effects. As a result, conventional methods are facing hurdles in dealing with corrosion signals of poor quality. In this study, the challenge is addressed by employing unsupervised learning methods utilizing an autoencoder neural network. This autoencoder integrates Long Short-Term Memory and 1D convolutional layers, acquiring the underlying features of normal PECT signals from non-corrosive regions. Significantly, the model is trained exclusively on this normal data, thereby obviating the necessity for pre-existing corrosion information. Through learning the inherent structure of normal signals, the model can detect anomalies in unseen data, potentially indicating corrosion. The unsupervised framework presents several advantages, such as reducing reliance on prior corrosion knowledge, mitigating inherent noise, and addressing sensor lift-off effects. Experimental results were conducted to compare with traditional methods like the lift-off of intersection and lift-off compensation methods. This approach resulted in a significant improvement in SNR, ranging from 100 % to 200 %, thus facilitating more robust NDT applications employing smart PECT sensors empowered by unsupervised learning techniques.

Evaluation of in-place strength and stiffness development in concrete structures using non-destructive test methods

ABSTRACT This study investigates the effectiveness of two widely used non-destructive assessment techniques – rebound hardness and ultrasonic pulse velocity – in evaluating the in-place compressive strength and modulus of elasticity of reinforced concrete structures. Large-scale reinforced concrete structures, composed of slab and wall elements, were prepared in the field for these measurements to estimate the mechanical properties during the curing process. The study found that environmental conditions significantly affected the ultrasonic pulse velocity results more than rebound hardness measurements. Standardized cylindrical specimens and cored samples from the concrete wall were prepared for comparison, revealing that cored samples exhibited slightly lower mechanical properties than standardized specimens. Notably, the modulus of elasticity demonstrated a strong correlation with rebound hardness. The experimental results indicate that non-destructive methods are more efficient for estimating the modulus of elasticity than compressive strength, and they have a potential as a quality inspection method for early-age concrete structures during the curing process.

Effect of recycled concrete aggregate size on physical and mechanical properties of eco-friendly-self-compacting concrete

In recent years, the rapid increase in population and building demand across the world leads to significant deterioration or reduction of natural resources. Meanwhile the huge amounts of waste from the demolition of buildings and concrete structures in the landfill space become a serious ecological and environmental problem. The recovery of aggregates from recycled concrete for the formulation of new concrete would contribute to reducing the depletion of natural resources and therefore integrate into the context of sustainable development, because there are several socio-economic advantages. The objective of this possibility of using aggregates from recycled concrete mixes. We have established a comparison between the physical and mechanical behavior of SCC based on RCA (Recycled Sand: SR 0/3 and Recycled Gravel RG: 3/8 and 8/15) and that of SCC based on natural aggregates (NA) on fresh support and hardened state. Additionally, Non-Destructive Testing (NDT) methods were conducted in this research to obtain more information about the properties of the studied SCC. The results of the tests on fresh SCC meet the recommendations of the French Association of Civil Engineering (AFGC). The physical and mechanical behavior of SCC-RS is relatively weak compared to SCC-NA/RG. This is directly related to the high absorption capacity of the aggregates (depending on the size of the aggregates) and the poor quality of the mortar attached to the aggregates, which can create areas of weakness in the concrete structure. From the regression results, it was found that there is a direct correlation between the physical and mechanical properties of different concretes indicated by the higher values of R-squared (R2 = 0.92).

The Influence of Concrete Age on the Accuracy of Pile Integrity Testing on Bored Piles Foundation

Abstract Bored pile foundations are defined as deep foundations in the form of an elongated tube with a specific diameter made of reinforced concrete installed using the drilling method and in-situ casting concrete. The cast in-situ process in bored holes with a deep elevation below the ground surface can potentially cause integrity problems, such as voids, segregation, fracture, necking, bulging, and other concrete defects. Currently, many non-destructive test methods are available to ensure the quality of the bored pile; one is the Low Strain Integrity Testing method, also known as Pile Integrity Testing (PIT). ASTM recommends that PIT should be carried out no sooner than 7 days after casting or the strength of the concrete has reached a minimum of 75,0% of its design strength. In fact, the PIT test is carried out before the concrete’s age and/or its strength is achieved, considering the limited construction time. This could lead to misinterpretation of the PIT test results and inaccurate identification of the bored pile defect locations. The study aims to determine the effect of concrete age on the accuracy of bored pile defect locations on a laboratory scale. The test model consisted of four bored piles with a diameter of 0,3 meters and a depth of 2,0 meters, which were cast using reinforced concrete with a design strength of fc’ 30 MPa. The PIT was performed at <7, 14, and 28 days of concrete age, and then the confusion matrix method was used to create a binary classification model to determine the accuracy of the defect location. The defect locations were visually observed and validated by dismantling the test model after 28 days of casting. The results showed that the accuracy of identifying the location of concrete defects was not affected by the age of the concrete but was determined by the compressive strength of the concrete during testing. Referring to the F-score, bored piles with concrete strength >75,0% of the design accurately identify defect locations above 84,21%. On the other hand, bored piles with concrete strength <75,0% design resulted in accuracy below 66,67%.

Theoretical and simulation study on the vibration modes of shear walls in prefabricated modular construction

Abstract As prefabricated construction advances, the enhanced informatization and the industrialized modular construction method of MIC (Modular integrated construction) shear walls have been widely applied in real projects. MIC shear walls are generally made up of prefabricated wall modules with varying strength grades, using concrete of various ages, and are combined with cast-in-place concrete. Due to the unique construction requirements of these walls, defects such as interfacial delamination and internal voids might occur, arising from factors like concrete shrinkage and creep, complex interfacial structures, inadequate compaction during vibration, and construction process issues. Since these interfacial defects cannot be visually inspected from the exterior, developing non-destructive testing (NDT) methods specifically for detecting such defects in shear walls becomes an urgent necessity. This paper utilizes a combination of theoretical analysis and numerical simulation to explore the vibration modes of MIC shear walls with hidden defects. First, a theoretical model is proposed for the local vibration modes of MIC shear wall shells with consideration of interfacial delamination defects. Then, a numerical simulation model is established based on MIC shear wall construction and defect features to simulate the vibration behavior accurately. Finally, after examining the theoretical and simulated vibration characteristics of MIC shear walls, a significant theoretical foundation is established for identifying interfacial defects in MIC shear walls.

Impedance Spectroscopy – comparison of experimental ceramic results with model parameters

Abstract Impedance spectroscopy is a non-destructive test method belonging to the group of electrical engineering measurements. The method is suitable for monitoring the quality of building materials even with low conductivity. Although the method focuses on the intrinsic conductivity of systems, most IS techniques, including data analysis methods, conceive of the material in terms of a lossy dielectric. In selecting the material for testing with the IS method, an attempt was made to bring the IS capabilities as close as possible to practical, real-world materials. This paper is devoted to a comparison of the impedance characteristics of ceramics with three types of dielectric models that are described in theory or referenced in the literature. The ceramic samples were fabricated using different material additions, such as bentonite, phosphoric acid, or sodium water glass. Experimental curves of the loss factor versus frequency for all samples are described and then approximations of selected samples are shown successively for all three models considered. Experimental results were obtained from more than 50 points in each characterization. Measurements were performed for frequencies from 40 Hz to 1 MHz.