Non-destructive Testing Methods Research Articles

Impact-Echo for Crack Detection in Concrete with Artificial Intelligence based on Supervised Deep Learning

Aging concrete infrastructure such as bridges and tunnels need to be appropriately maintained in their service life because these structures must be employed with ensured safety level. Inspection and maintenance with reasonable measures are typically operated for the reinforced concrete before severe damage and failure are developed. Especially, the crack propagation in concrete can significantly affect the structural integrity, durability and overall performance of structural elements. Effective maintenance strategies are required for material and structural assessment with detection and repair of cracks to prevent further deterioration. Identification of cracks in concrete is effectively operated to extend the service life of structures for mitigating the potential safety hazards and minimize the repair costs. In order to detect the cracks in the concrete structure member, non-destructive testing (NDT) system can be often introduced to satisfy the technical issues in terms of damage evaluation and estimation of repair condition. Currently, various NDT methods have been utilized for quantitative analysis of internal damage/defects using elastic wave method such as impact-echo method, focusing on reflection of P-wave in concrete as wave propagation behavior. In experimental laboratory tests, concrete specimens are usually prepared with interior damages (cracks) which are simulated as embedded plastic plate or styrofoam. In this study, starch- type polysaccharide sheet is used to form the simulated cracks in concrete specimen. This study aims at applying impact-echo method with AI (Artificial Intelligence) to identify the internal crack information based on the waveform characteristics obtained from the elastic wave propagation behavior. The waveform results are converted to frequency spectrum by FFT analysis. Consequently, it is found in this study that AI, which is employed by supervised deep learning models, successfully evaluates the data and displays the probability of crack existence in concrete. Since FFT is a powerful algorithm that quickly converts a signal from the time domain to the frequency domain, it gives efficient analysis to the impact echo method for crack detection with the application of AI system.

Measurement of Focal Spots of X-ray Tubes Using a CT Reconstruction Approach on Edge Images of Large Holes and Comparison to Pinhole Imaging

The first Non-destructive testing (NDT) method which evolved in the industrial age was radiographic testing (RT) [1]. Among all NDT methods, RT is no exception, so there are still many issues for optimizations even today. One of them is the measurement of the focal spot of X-ray tubes [2]. The size of the focal spot is critical for imaging, because it determines the spatial resolution in the X-ray image. The classical way to evaluate focal spots of X-ray tubes is by pinhole imaging using a camera obscura [1]. But this method has a natural lower limit, which is defined by the diameter of the pinhole used (today min. 10 µm) [2]. Therefore, focal spot sizes lower than 50 µm diameter cannot be imaged and measured correctly. An alternative approach, which permits this, was investigated here using the edge unsharpness of holes much larger than the focal spot size. The results of both methods were compared using 3 different X-ray tubes.

Real-Time Phased Array Ultrasonic Monitoring of Debonding Growth Under Cycling Loading in CFRP

Carbon Fibre Reinforced Polymer (CFRP) composites are widely employed in diverse industries due to their specific stiffness and strength, and corrosion resistance. However, over time, service-induced damage can initiate a failure that can threaten the operation’s safety and reliability, especially in critical industries like aerospace applications. The challenge lies in identifying and monitoring the defects before they escalate into serious issues. Non-Destructive Testing (NDT) methods play a pivotal role in this scenario, aiming to detect and evaluate defects without causing harm to the material. Detecting flaws in composite materials such as CFRP remains a complex task. Traditional NDT methods often struggle to effectively pinpoint and assess defects within these intricate structures. However, recent advancements in ultrasonic testing, especially Phased Array Ultrasonic Testing (PAUT), show promise in examining the internal composition of CFRP composites with higher accuracy and reliability. This study aims to demonstrate the applicability of PAUT for real-time monitoring of debond growth under dynamic loading. The loading was applied to the test specimens up to one million cycles, and 64 elements 2 MHz linear phased array probe was attached to the specimen. The acquired signals were analyzed and could also be recorded for offline processing or archiving. To validate the credibility of the findings, the performance of PAUT was compared to Acoustic Emission (AE), a well-established method for detecting and analysing high-frequency signals emitted by materials undergoing deformation or damage. Encouragingly, both methods exhibited similar trends in capturing the growth of debond. Furthermore, to verify the final defect lengths, post-test ultrasonic C-Scans utilizing the through transmission technique were conducted, complemented by visual inspections. These assessments served to confirm the accuracy and reliability of the results obtained through Phased Array Ultrasonic Testing. Real-time monitoring of composite structures using phased array technology comprises a valuable tool for real- time assessment of debonding in the composite. As industries continue to rely on Carbon Fibre Reinforced Polymer composites for multifaceted applications, the utilization of advanced Non-Destructive Testing methodologies like Phased Array Ultrasonic Testing emerges as a cornerstone in guaranteeing their continued integrity and performance.

Non-Contact Magnetometric Diagnostics of Pipelines Using the Metal Magnetic Memory Method

The non-contact magnetometric diagnostics (NCMD) of buried pipelines (oil and gas pipelines, heat lines, water supply lines, etc.) has been developed by Energodiagnostika Co. Ltd. since 2000 based on the 25-year experience of the metal magnetic memory (MMM) method application (ISO 24497-1:2007(E)). NCMD is based on measurement along the pipeline route of the Earth’s magnetic field intensity (HEarth) distortions caused by variation of the pipe metal magnetization in stress concentration zones (SCZs) and in zones of developing corrosion-fatigue damages. The paper describes the experience of complex diagnostics of buried pipelines based on NCMD, the MMM method and conventional methods of non-destructive testing. Based on NCMD results, the most strained pipeline sections are determined for their opening and additional control by the MMM method, ultrasonic testing and other NDT methods. On opened pipeline sections the MMM method is used to determine the presence of defects and the mechanical properties of the metal by hardness parameters. Inadmissible defects are removed, and the actual mechanical properties (yield strength and ultimate strength) are taken into account in check strength calculations. The inspection should provide the answer to the following question: “Where and when should a damage or an accident be expected?”. If this task is solved, then the possibility of timely replacement or repair of a potentially hazardous section is provided. Application of NCMD in combination with additional inspection of pipelines (UT, eddy current, etc.) in prospect holes determined by NCMD is aimed exactly at solution of this problem. The paper presents the experience of NCMD of buried polyurethane foam insulated pipelines in order to detect girth welded joints with developing damage.

Experimental study on grouting inspection of PC tendons by means of Ultrasonic Pulse Echo Tomography method

Evaluation of grouting condition of post-tensioned tendons in Prestressed Concrete (PC) structures is an important problem in a field of maintenance of civil engineering infrastructure. Existing methods of non-destructive testing (NDT) used for grouting inspection have some limitations. For example, Impact-Echo method has comparatively low accuracy and X-Ray method is expensive and time- and labor-consuming. At the same time, there are promising alternative methods of NDT, which can be used for grouting inspection. The authors carried out an experiment using several large-scale specimens simulating PC girders. Commercially available Ultrasonic Pulse Echo Tomography device with an array of dry-contact sensors was used. Several cases of grouting condition, PC sheath diameter and geometry and rebar placement were studied. It was confirmed that amplitude of reflected ultrasonic pulse in the case of not grouted PC sheath is larger, as compared with a case of a grouted PC sheath. However, depending on diameter of PC sheath, distance from PC sheath to concrete surface and presence of nearby reinforcement, evaluating grouting condition based only on amplitude of reflected ultrasonic pulse can be problematic. To solve this problem authors developed a more accurate method of evaluating grouting condition, which uses multiple parameters, such as amplitude of ultrasonic pulse reflected from PC sheath, backwall and phase of reflected ultrasonic pulse.

Comprehensive Inspection Solution for Boiler Water Wall Tube Corrosion

A substantial number of domestic coal-fired boilers, most of which have been in operation for numerous years, are susceptible to corrosion and tube ruptures within their high-temperature environment, often resulting in unplanned shutdowns. Despite the annual planned maintenance shutdowns, which involve routine inspections of water wall and furnace tubes to detect potential corrosion and erosion, the current non-destructive testing methods are generally localized. This approach, particularly with the considerable height variation of the water wall tubes (ranging from approximately 10 to 25 meters), may overlook segments with severe corrosion during localized ultrasonic thickness gauging. Although the experience of inspection teams reduces the chance of defect oversight, the demand for a comprehensive and effective inspection technique remains a priority for boiler operators. This study utilizes the characteristic of guided waves to propagate over a certain distance along pipelines, initiating an exploration into a comprehensive inspection solution for water wall tubes. Finite element simulation results demonstrate the suitability of utilizing a 120 kHz frequency for shear horizontal mode (SH0) testing on the fire side of water wall tubes. This mode effectively screens for localized corrosion within a 3-meter range. In the field of on-site boiler inspection, electromagnetic coils are employed to generate guided waves within water wall tubes. The gathered signals from each electromagnetic guided wave testing (EMGWT) are consolidated into a distribution map, indicating suspicious signal locations. Pulse eddy current testing (PECT) is employed to identify potential issues with guided waves without requiring surface preparation. If PECT reveals anomalies, the affected surface is polished, and phased array ultrasonic testing (PAUT) is conducted to quantitatively measure the remaining wall thickness due to corrosion. The theoretical exploration and on-site inspection outcomes affirm the feasibility of this approach, aimed at enhancing operational safety for boiler water wall tubes and mitigating the risks of tube ruptures and leaks. This comprehensive inspection solution amalgamates three advanced detection technologies, each capitalizing on its unique inspection characteristics to facilitate corrosion screening, corrosion scanning, and wall thickness sizing. Through the integration of these technologies, the overall inspection strategy ensures the utmost level of reliability.

Analysis of the structural behavior of reinforced concrete components with acoustic emission analysis

The use of non-destructive testing methods can provide insights into processes within the interiors of structures. Acoustic emission testing is one of the non-destructive testing methods that allows the detection of material damage during its occurrence. When structures, such as concrete components, fail under load, elastic waves are generated, propagating within the component. These signals can be detected by acoustic emission sensors placed on the surface of the component, enabling the tracking of the material's condition and the location of failure. This paper summarizes recent applications of acoustic emission measurements on large-scale laboratory specimens made of reinforced concrete. The first example describes measurements during a tensile fatigue test on a large- scale reinforced concrete specimen to investigate the fatigue behaviour of an embedded concrete dowel. The second example involves measurements during a dynamic three-point bending test to examine the fatigue behaviour of a prestressed concrete railway sleeper. The third example describes a full-scale test conducted on a flat slab concrete test specimen with dimensions of 2.8 m × 2.8 m. The objective of these investigations is the development and optimization of new components to enhance punching shear resistance and provide an economical solution. To detect microcracking during loading acoustic emission measurement has been carried out using broadband sensors with a measurement frequency of up to 200 kHz which were especially developed for the detection of microcracks in concrete. For the three-dimensional location of the acoustic emission events, 16 of these acoustic emission sensors were attached to the surface of the specimens. Most of the events were in the centre of the sample in the punching area. These events form a circular structure with a diameter of approximately 1m. A visual inspection of the sample after the experiment shows that macroscopic cracks have formed in this area, which extend up to the surface of the sample.

The Curriculum in NDT Training Have to Include Basics Information Technology to Accomplish Reliability of Automated NDT Result's

Automation in Non-Destructive Testing (NDT) is a reality of modern manufacturing and quality assurance. Information technology applications are simultaneously developed and applied to different fields of NDT methods. The future of doing reliability checks using automated system is inevitable. The results are important to make a variety of decisions regarding the quality of a product and/or service, hence affecting safety and reliability. Depending on artificial intelligence and quantum device development, various NDT training schemes have to adopt a new curriculum to address the issue of the basic knowledge of information technology and automation in NDT. NDT operators are basically dependent on the end results of software, in which the software developer uses a set of algorithms. These are obtained in three categories; 1) Real time database; 2) Synthetic database and 3) Hybrid or combination of them. The developers should know how the NDT systems run and NDT operators in the other hand know the mechanism of the program, so that appropriate action can be taken, in case there is a missed detection, or a detected indication is not very much detrimental to the reliability of the part (Over sizing or wrongly evaluated). For example, a set of algorithms are used to create the focal laws in a phased array ultrasonic test software in space of 1˚ beam steering. Each has a limit of maximum up to 2nd Critical angle. Operators try to copy the same algorithm and set up to 89˚ refraction angle, which will just create creep wave with unreliable results to detect effective size the reflector. Programming knowledge of the operator can effectively eliminate this type of unreliability. In various Schemes of NDT Syllabus which are in common place to employ and train plus certify operators does not have it; actually, the operators do the scope of work which does includes application of Artificial Intelligence (AI) and Software. With the development of Q coupler (Quantum Computing) system, it is obvious to adopt the upcoming technology into the application of NDT industry. Training needs to update the curriculum accordingly. The current topic has intended to focus on the training gap and to address related kind of unreliability.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Evaluating Residual Strength of RCC Tanks Affected by ASR Using NDT Methods

This case study focuses on the groundwater level tanks (GLR) located in Netra Village, 35 km from Jodhpur, on Nagour Road. The investigation revealed that people and livestock in the area were grappling with water scarcity issues. Non-Destructive Testing (NDT) methods were employed to assess the residual strength of three tanks in the region. The results showed that Tank-1 had a residual strength of 35.41%, Tank-2 had 63.08%, and Tank-3 had 53.8%. Alkali-silica reaction (ASR) poses a significant threat to the structural integrity of concrete tanks, necessitating accurate and non-destructive methods for residual strength assessment. This research paper delves into the effectiveness of NDT methods, specifically rebound hammer and Ultrasonic Pulse Velocity (UPV), in evaluating the residual strength of ASR-affected tanks. Through an extensive review of literature, case studies, and experimental data, this study aims to shed light on the practical application of rebound hammer and UPV for ASR assessment in tank structures. The paper discusses the principles, advantages, and limitations of each NDT method, emphasizing their ability to detect ASR-induced damage and predict the remaining structural capacity of tanks. Moreover, the research addresses the challenges associated with implementing rebound hammer and UPV techniques in ASR-affected environments, offering recommendations for enhancing their reliability and accuracy. By harnessing the combined capabilities of rebound hammer and UPV, engineers and asset managers can make well-informed decisions regarding the maintenance, repair, and retrofitting of ASR-affected tanks, ensuring their long-term safety and functionality in critical infrastructure applications.

Modern trends in the application of thermoelectric method in non-destructive testing (review)

The article provides an overview of the main directions of using the thermoelectric testing method in various spheres of the national economy. The thermoelectric method is most widely used in industry. There are publications on the application of the thermoelectric method for quality monitoring of turning and friction stir welding. It is shown that the thermoelectric method makes it possible to increase the reliability of testing and, based on testing data, ensure optimal modes of the technological process of metalworking and welding. A number of articles are devoted to the application of the thermoelectric method to plastic deformation testing, the dependence of thermal EMF on the degree of plastic deformation is revealed. Recently, publications have appeared on the application of the thermoelectric method to testing the thermal resistance of the design “housing of a power semiconductor device-thermal interface-cooling radiator”. This design is very widespread in electronic equipment. In addition, there are articles on the use of the thermoelectric method to testing the transient resistance of contacts in the power supply network. It is shown that with an increase in contact resistance, the value of thermal EMF increases proportionally, which can be used to prevent emergencies in the power supply network. The thermoelectric method has also been successfully applied to diagnose the degree of titanium flooding. The dependence of the thermal EMF value on the degree of hydrogenation has been revealed. The use of the thermoelectric method is not limited to the field of industrial production. The thermoelectric method is successfully used in medicine for the undetectable detection of nucleic acid sequences, for the temperature diagnosis of human teeth, as well as for the diagnosis of inflammatory processes in the human body.

An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction

Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback–Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.

Establishing the Quality of Buckwheat Grain Using Non-destructive Testing Method

The assessment of the quality of buckwheat grain according to regulatory indicators does not allow to determine the state of its core. The paper examines the possibility of using microfocus radiography as a non-destructive testing method. The latent (internal) defects of buckwheat grain (underdeveloped (unfulfilled) grain, hollow grain, damaged core (fracturing), damaged core (destruction), grain with a damaged shell) were identified and classified, obvious distinguishing features on the radiographic image of the grain are described. It is shown that when conducting a comprehensive assessment, the results of the X-ray examination make it possible to increase the objectivity of the conclusion on the quality of buckwheat grain.

Fluorescent Composite Nano‐ and Microparticles Based on Xanthene Dyes and Iron Oxides

AbstractModern industry actively uses metal structural materials to create innovative devices and components. In order to prevent emergency situations in production, nondestructive testing methods are used to detect defects on the surface of the material being developed. Among the methods of nondestructive testing, one can highlight the method of luminescent magnetic flaw detection, which allows identifying defects on the surface and under the surface of the test sample with minimal effort. It is known that in some cases, the magnetic‐powder method cannot achieve the required sensitivity due to the shape and location of defects themselves or due to certain conditions of object operation. To increase the visibility and sensitivity, magnetic nano‐ and microparticles can be conjugated with the dyes. Iron oxide nanoparticles (Fe3O4, γ‐Fe2O3) can be used as such magnetic particles, since they are quite easy to obtain and have low toxicity, high chemical stability, and a wide variety of magnetic properties. To ensure fluorescence of magnetic particles, a wide variety of fluorescent dyes associated with such particles are used. The dyes can include morin, naphthalimide derivatives, as well as semiconductor quantum dots. This article provides a review of fluorescent composite micro‐ and nanoparticles based on xanthene dyes (rhodamine, fluorescein, and pyronine). Such dyes are economically available luminophores, have good binding to the surface of magnetic particles, and therefore can be suitable for luminescent magnetic flaw detection. This review article discusses methods for producing composite nano‐ and microparticles of iron oxides with xanthene dyes and provides prospects of using composite nano‐ and microparticles in nondestructive testing methods.

Barkhausen Noise as A Magnetic Nondestructive Testing Technique

Magnetic Barkhausen Noise (MBN) is a magnetic-based non-destructive electromagnetic testing method. Due to the electromagnetic working principle of MBN, it can be used for ferromagnetic materials, which consist of small magnetic fields discredited by domain walls and oriented in various directions. In an external magnetic field application, the fields turn to the magnetic direction, and the domain walls move and cause magnetic flux jumps. The jumps are named Barkhausen Noise (BN). The domain wall movements are sometimes pushed down by microstructure, composition, and defects. As the magnetic domain walls break away from the pinning sites produce MBN signal. MBN can be used for different material properties such as microstructure, composition, residual stress, and hardness. The paper's purpose is to analyze MBN as an improved NDT, clarify the relationship between material properties and MBN profile, and introduce MBN's applications and test equipment of MBN.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

The Potential for Material Property Characterization Using Physics-Informed Neural Networks and Ultrasonic Wave Data

ABSTRACT There is a need for reliable nondestructive test methods that can collect data from structural members and analyze the results in a rapid and efficient manner. Large amounts of test data are needed to achieve such characterization, which provides additional challenges because of their heterogeneity and complexity. Advances in machine learning, in particular physics-informed neural networks (PINN), offer potential to address these problems. PINN is a particular form of artificial neural networks (ANN) and portends notable advantages over traditional measurand analysis or purely data-driven approaches. Here, we explore the potential of heterogeneous material property characterization using PINN and ultrasonic wave data. First, several types of 1-D ultrasonic wave data are numerically simulated for a spatially heterogeneous material, and then PINN is applied to predict wave velocity, defect location, and energy dissipation. Then, three different types of defects are simulated and all defects are detected using the corresponding 2-D ultrasonic wave data and PINN. The presented results demonstrate the promise of PINN to assist with heterogeneous material characterization methods.

Design of Concrete Mock-Ups with Complex Defect Scenarios Using Numerical Simulations

The major objective when applying non-destructive testing (NDT) in civil engineering is the detection of defects such as honeycombs. Defects can impair the service life of structures and could lead in the worst case to fatal failures. All structures such as bridges, tunnels, locks etc. are unique objects with variation in geometry, dimensions, materials, environmental condition, load scenarios etc. Thus, the interpretation of NDT data is challenging and the validation of the NDT method, the training of inspectors and the reliability analysis of NDT rely on mock-ups with known defect situations mimicking the type of defect as realistic as possible. The considered defect sizes inside the mock-ups should cover the transition zone between detectable and non-detectable to properly evaluate the capability of the inspection system. However, knowledge about the limits of an inspection system is mostly non-existent. To overcome this limitation, this study applies realistic numerical simulations for ultrasonic testing in reinforced concrete performed with the elastodynamic finite integration technique (EFIT) to identify this transition zone. First, the study shows the development and the validation of a numerical representation of concrete considering the implementation of a qualitatively realistic noise level. Additionally, the accurate representation of the sources during ultrasonic testing is confirmed by an investigation of the radiation characteristic for dry point contact ultrasonic transducers. The simulation outcome enabled the design of a mock-up, which explores the detection limits for honeycombs during ultrasonic testing under different boundary conditions. A case study demonstrates the applicability of the design scheme based on numerical simulation. In the real specimen designed with the assistance of numerical simulations, 68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$68\\mathrm{\\%}$$\\end{document} of the implemented defects are detectable, proving the effectiveness of the design methodology.

Bending Strength Evaluation of Japanese Wooden Cultural Heritage Building Columns Based on a Non-Destructive Testing Method

ABSTRACT Column failure assessment is essential for seismic diagnosis of Japanese wooden cultural heritage buildings. Achieving accurate column failure assessment depends on accurately estimating the bending Young’s modulus (E b ) and characteristic bending strength (F b ) of columns. This study extends the application of a non-destructive testing (NDT) method to estimate E b and F b of Japanese cedar columns generally used in wooden cultural heritage buildings. For this purpose, a correlation between the dynamic bending Young’s modulus (E p ) and E b of Japanese cedar columns is established and the variations in the E p –E b relationship across different wood provenances and species are clarified. Using the proposed method, a statistical analysis of the estimated E b of Japanese wooden cultural heritage buildings is provided. Furthermore, estimated E b is used to determine F b of the columns using the mechanical grade classification system. The results show that most Japanese cypress and Japanese cedar columns feature higher F b than those used in conventional seismic diagnosis. Conversely, two-thirds of the Japanese red pine columns feature a lower F b than that used in conventional seismic diagnosis. The proposed F b evaluation method offers an NDT method that can be widely applied to Japanese wooden cultural heritage buildings to assess column failure more accurately.

Improving non-destructive testing methods for detecting cavity damage and internal defects in stone cultural relics: A focus on ultrasonic testing and acoustic tapping technology

This study employs advanced ultrasonic tomography and Grasshopper software to investigate the internal anomalies within stone materials. We aim to enhance the non-destructive testing (NDT) methods with potential applications extending to modern architecture and stone engineering. Cavity damage and internal defects threaten the structural stability of stone cultural relics. Traditional non-destructive testing methods, while practical, have limitations in assessing these relics comprehensively. This paper proposes an enhanced ultrasonic testing method based on mathematical analysis to accurately detect cavity damage in stone cultural relics. We also investigate imaging techniques for internal defects, combining ultrasonic testing with Grasshopper software for efficient and accurate defect characterization. Additionally, we introduce an acoustic tapping method for detecting internal defects in ancient building stones. Verified through field tests and data analysis, this method is non-invasive, accurate, and fast. The process of detecting stone defects using this technology, along with data analysis methods, experimental results, and application effects, are detailed. This research contributes to the improved preservation and restoration of ancient buildings and extends to other fields, such as modern architecture and stone engineering, highlighting its significance in cultural heritage conservation.