Nondestructive Testing Methodology Research Articles

Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model

In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Improved acoustic source localization method for crack identification in structures

Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.

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.

Utilizing Optimized Machine Learning Techniques to Predict the Compressive Strength of Concrete through Non-Destructive Testing Methodologies

Background:: Examining the concrete quality in its original location and optimizing machine learning models for precise forecasting of concrete compressive strength(fc) is crucial. Current research advocates the fine tuning of hyperparameters within machine learning methodologies in tandem with non-destructive testing techniques to forecast the compressive strength of concrete. Objective:: This study aimsto incorporate age as a crucial factor by utilizing data spanning from 3 days to 365 days. This approach enhances the study’s applicability for real-time forecasting purposes. Methods:: In the methodology of this current research, three machine learning (ML) models— specifically, Multi-Linear Regression (MLR), Decision Tree Regressor (DTR), and Random Forest Regressor (RFR)—are introduced within the context of age as a significant factor influencing measurements obtained from the Rebound Hammer (RN) and Ultra Sonic Pulse Velocity (UPV). These ML models were sequentially applied, followed by a meticulous process of hyperparameter finetuning conducted through grid search Cross-Validation (CV). To gain insights into the predictive results, the study also employed SHapley Additive exPlanations (SHAP) for interpretation purposes. Results:: The results of this study reveal the development of an empirical relationship using Multi- Linear Regression, which yielded an R2 value of 0.88. Furthermore, the evaluation showed that Random Forest Regression outperformed other models with an R2 value of 0.95 in the training and 0.92 in the testing datasets. These models hold promise for facilitating decisions about qualitative analyses based on UPV and Rebound Hammer measurements relative to the age of the concrete. Rigorous validation of the models was conducted through standard cross-validation techniques. Conclusion:: The research has created and validated hyper tunned machine learning models with the help of grid search cross-validation function, with Random Forest Regression being the most effective. These models can potentially guide decisions regarding qualitative analyses using UPV and Rebound Hammer measurements concerning concrete age. They provide a valuable tool for on-site assessments in construction and structural evaluations. The primary objective of the research is to introduce age as a significant feature. To achieve this, data ranging from 3 days to 365 days was integrated. This inclusion aims to enhance real-time decision-making in construction processes, facilitating actions like the prompt removal of formwork in high-speed construction projects.

Consideration of electronic mean heat transport via a low dimension system

The object of research is the complex realm of energy localization and coherent ballistic electronic transport within low-dimensional silicon quantum wires, specifically those doped with germanium atoms. Unlike their three-dimensional counterparts, low-dimensional systems exhibit unique electronic transport behaviors, necessitating novel analytical approaches for a comprehensive understanding. The core of this investigation leverages the Phase Field Matching Theory (PMFT) and the tight-binding (TB) approximation, sophisticated methodologies that enable a deep dive into the quantum mechanical nuances of these systems. Through this lens, we examine the intricate dynamics of dispersion relationships, phase factors, group velocities, and notably, the impact of defects introduced by the germanium doping. This research meticulously analyzes how these defects affect electronic and thermal conductivities, along with densities of states, offering new insights into the role of Fano resonances in the fluctuation of transmission and reflection spectra. These resonances, we find, are crucially dependent on the nature of the defects, their configuration, and the electronic parameters in their vicinity, underscoring the nuanced interplay between material composition and electronic properties in low-dimensional systems. The implications of our findings extend far beyond the theoretical. They pave the way for significant advancements in nanotechnology and the design of electronic devices, highlighting the potential for creating more efficient, high-performance components. Furthermore, this work proposes a framework for developing non-destructive testing methodologies that could revolutionize material science by enabling the precise analysis of defects in low-dimensional systems without causing damage. This is particularly critical for the ongoing development of materials with optimized properties for various applications, from electronics to energy storage. In essence, this research not only enriches our understanding of the physics governing low-dimensional systems but also offers practical insights into leveraging these properties for technological innovation. By bridging the gap between theoretical physics and material science, our study sets the stage for the next generation of electronic components and non-destructive evaluation techniques, marking a significant step forward in the application of quantum mechanics to real-world challenges.

Comparative study of pile quality control techniques

This article delves into the critical realm of quality control in pile foundation construction, presenting a comprehensive exploration of both destructive and non-destructive testing methodologies. Focused on enhancing structural integrity and reliability, the study evaluates techniques such as concrete strength testing, core sampling, Cross-Hole Sonic Logging (CSL), and Pile Integrity Testing (PIT). Results from field observations conducted during construction of the Light Rail Transit (LRT) system in Astana showcase the effectiveness of non-destructive methods, with 1,896 bored piles subjected to rapid testing. Significant findings reveal that approximately 75% of tested shafts exhibited anomalies, emphasizing the necessity for meticulous quality control. The article concludes by advocating for the adoption of advanced quality assurance protocols to mitigate risks and ensure the robustness of pile foundations in construction projects.

Gradual error detection technique for non-destructive assessment of density and tensile strength in fused filament fabrication processes

Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products.

Evaluating the strength properties of standing trees through fractometry

In recent years, significant advancements in non-destructive testing (NDT) methodologies have emerged, with applications spanning various domains, including structural wood quality assessment and planted tree characteristic evaluation. Within the context of planted trees, a range of non-destructive and semi-destructive techniques have been developed to assess the extent of degradation in tree trunks. In this study, various mechanical characteristics of brutian pine (Pinus brutia Ten.) trees near the Ertokuş Madrasah in the Atabey district of the province of Isparta are examined. Beside their historical significance, these trees are notable for the potential risk they present in terms of leaning towards the madrasah facade and the risk of falling over. To achieve the goals of research, the resistance characteristics of incremental cores were systematically determined by using a thermal imaging camera in conjunction with a portable, non-destructive testing device called a Fractometer. Totally 15 incremental auger specimens were obtained by extracting three increment core samples, each with a thickness of 5 mm, from the trunks of five distinct trees, all at a consistent height of 1.3 meters above ground level. Bending and compressive strength measurements were recorded at intervals of 6 mm from the core to the outermost layer. Furthermore, the moisture content of the incremental cores was assessed using thermal imaging technology. Following an analysis of the collected data, it was concluded that the mechanical properties of the investigated brutian pine trees within an acceptable range.

Automated woven background removal for enhanced infrared thermographic inspection of fabric composites

Infrared thermography is a promising non-destructive testing methodology for the inspection of composite materials. While there are many accounts in literature that report an excellent performance on unidirectional composite laminates, this is not the case for woven fabric composites. Their inspection is typically characterised by the presence of an imprint of the weave pattern in the thermographic recordings, which is most-often found to be disturbing for reliable anomaly detection. In this contribution, it is proposed to evaluate the thermographic data in ????-space in order to separate the weave pattern from the defect information. To this end, an algorithm is introduced to automatically perform this decomposition. The efficiency of the proposed methodology is demonstrated for several weave patterns with different defect cases.

FFRC and SASW nondestructive evaluation of concrete strength from early ages

Free-Free Resonant Column and Spectral Analysis of Surface Waves are two nondestructive testing techniques which are becoming increasingly relevant in construction engineering, as they offer the possibility of assessing the mechanical properties of the building materials without damaging the sample. In the present study, these two testing methods are applied on concrete samples with increasing curing times, from 9 h to 60 days, estimating the dynamic elastic modulus and proposing relationships to obtain the compressive strength from these values. The nondestructive results are compared with the actual strength of the material obtained via uniaxial compression tests, proving the accuracy of the proposed nondestructive testing methodologies, even for early ages. SASW tests are also carried out on a reinforced concrete sample, concluding that the presence of the reinforcement does not alter the results of the test.

Analysis of prestressing in old full-scale concrete members

The effective prestressing force is a key parameter that affects the performance of prestressed concrete members (PCM). In fact, the accurate determination of the residual prestressing force is crucial to assess existing PCM since it influences load-carrying capacities, in-service structural properties and remaining service life. The aging of structures, with limited functional capacity, implies a major problem that must be addressed without delay. Progress in the promotion of innovative solutions to extend the service life of structures depends on: (a) the availability of reliable indirect methods, since old PCM were usually no instrumented during casting; (b) the formulation of adequate diagnostics of the residual stress–strain state of structures; and (c) the availability of highly refined predictive tools. The implementation of these elements in the management and maintenance systems of PCM will serve to support decision making associated with structural assessment and to gather information to establish criteria for the design of more resilient PCM.Among the destructive methods available to empirically assess the actual condition of PCM, it can be found: crack initiation, crack re-opening and tendon cutting. As destructive methods inevitably cause structural damage and are not suitable for application to in-service structures, non-destructive methods or with conditions that only require aesthetic restitution (e.g. exposed tendon deformation, hole-drilling and saw-cuts) appear as an alternative of increasing interest.As a first step to obtain feed-back from several experimental tests carried out on unmonitored full-scale PCM over the years, this paper examines different techniques and methods used to evaluate the residual prestressing force, and also presents the deviations and uncertainties regarding predictions of short- and long-term prestress losses from main code models used in design. It is concluded that it is interesting to follow a non-destructive indirect testing methodology to be able to keep in service and monitor the current structures. This would make it possible to draw up a maintenance plan based on the condition of the PCM.

A novel ultrasonic non-destructive testing methodology to monitor fatigue crack growth in compact tension specimens.

Detection and evaluation of fatigue cracks in structural materials are extremely important for life prediction analysis as part of the structural integrity procedure. In this article, we present a novel ultrasonic measurement methodology, based on the diffraction of elastic waves at the crack tips, to monitor the fatigue crack growth near the threshold regime in compact tension specimens at different load ratios. The diffraction phenomenon of ultrasonic waves from the crack tip is demonstrated using a finite element 2D wave propagation simulation. The applicability of this methodology has also been contrasted with that of the conventional direct current potential drop method. In addition, the crack morphology obtained in the ultrasonic c-scan imaging showed a variation in the crack propagation plane as a function of the cyclic loading parameters. The results suggest that this novel methodology is sensitive to fatigue cracks and can form the basis of in situ ultrasonic-based crack measurements in metallic and non-metallic materials.

A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates

The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints.

Analysis of the GPR signal for moisture detection: application to heritage buildings

ABSTRACT Moisture is one of the main causes of degradation of heritage buildings. Early detection of zones affected by moisture is crucial information for preservation and maintenance of those structures. Ground-penetrating radar (GPR) is an effective survey method to assess damage in civil engineering and structures. Several methodologies involving this technique have been applied to determine the extension of damping in concrete, stone, and brick structures. This article summarizes the different signal analysis techniques generally used to detect moisture from GPR data. Four different case studies are included, covering different types of buildings. The case studies include the description of the problem, the results, the methodology (data acquisition, survey parameters, and processing), and the limits and advantages. Comparing the different studies, the limitations and advantages are associated with each type of problem and a final discussion describes other non-destructive testing techniques, numerical simulations and methodologies that could provide more reliable understanding when combined with GPR data.

Self-healing assessment of cementitious mortars through ultrasonic monitoring

Self-healing cementitious composites have gained increased attention over the past decades, encouraged by the excessive costs related to manual concrete repair. Thorough investigations on these materials have shown their potential to be applied for real life construction elements. However, the existing methods used to assess the self-healing effectiveness possess several limitations, making an in-situ evaluation of the regained properties practically impossible. This study therefore investigates a non-destructive testing methodology based on ultrasonic wave propagation that can eventually be practically implemented. The sensitivity of ultrasound to the elastic properties of the material under study allows to assess the self-healing effectiveness and discriminate between actual healing, in the sense of mechanical properties restoration, and simple filling of the crack. Moreover, the method is validated and complemented by numerical wave simulations that allow to connect the macroscopically measured wave velocity to the stiffness of the healing products deposited within the crack.

Evaluation of self-healing in cementitious materials with superabsorbent polymers through ultrasonic mapping

The maintenance and repair of concrete infrastructure covers an increasing portion of the annual construction budget. For this reason, self-healing cementitious materials have been investigated thoroughly in literature. One way to achieve an effective self-healing composite is by promoting their inherent repair ability through the inclusion of superabsorbent polymers (SAPs). Thanks to their large absorption capacity upon contact with water, SAPs allow to preserve the humidity inside the cracks for a longer period of time, which is essential to promote the autogenous healing process. In order to encourage the application of SAPs for self-healing purposes, adequate assessment techniques should be at hand in order to evaluate a SAP’s effectiveness for self-repair. Whereas various methods, such as water permeability tests and microscopic analysis, are used to assess a material’s self-healing potential, mechanical tests are needed to determine the regain in mechanical performance. Due to the destructive nature of these methods, the monitoring of the self-healing mechanism over time is practically impossible. In the present research, a non-destructive testing methodology based on ultrasonic wave through transmission was investigated, due to its sensitivity to the elastic properties of the material under study. By means of ultrasonic mapping, the interior was visualized and allowed to determine the degree and uniformity of healing along the depth for different healing times for the first time in literature.

Influence of Elastic–Plastic Deformation on the Structure and Magnetic Characteristics of 13Cr-V Alloyed Steel Pipe

The principle of symmetry is one of the general methodological principles of science. The effects of any external influences, such as deformation, stresses, temperature, etc., could lead to the anisotropy (asymmetry) of properties in constructional materials. During operation, metal structures and machine parts are exposed to time-varying external mechanical loads, which can cause changes in the metal structure, the initiation of cracks, and, as a result, the destruction of the product. The application of nondestructive testing methods prevents changes in the stress–strain state and, consequently, the destruction of the object. This article contains the results of studying the effects of elastic–plastic deformation by uniaxial tension and torsion on the change in the structure and magnetic parameters of low-alloy 13Cr-V pipe steel. Modern methods of metallography and magnetic nondestructive testing methods were used as part of this study. The results of the EBSD analysis showed that deformation during torsion, in contrast to uniaxial tension, is unevenly distributed over the sample cross section. In the cross section of the sample, the most severely deformed grains with a change in their geometry are observed near the surface; in the center, there is no change in geometry. During tension, the deformation over the cross section of the sample is uniformly distributed. Correlations between the applied normal and tangential stresses and magnetic characteristics of the 13Cr-V structural steel were determined. Informative parameters that could be used for the development of nondestructive testing methodologies for solving concrete tasks were determined. Different methods of deformation lead to diverse structural changes in grain structure.

An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing

PurposeThe major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion.Design/methodology/approachThis paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects.FindingsThermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process.Originality/valueIn this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

Estimating the Concrete Compressive Strength by Using the Concrete Ultrasonic Pulse Velocity and Moduli of Elasticity

Developing a non-destructive method which delivers fast, accurate and non-invasive results regarding the concrete compressive strength, is an important issue, currently investigated by many researchers all over the world. Different methodologies, like using the simple non-destructive testing (NDT) or the fusion of different techniques approach, were taken into consideration in order to find the optimal, most suitable method. The purpose of this paper is to present a new approach in this direction. The methodology consists in predicting the concrete compressive strength through ultrasonic testing, for non-destructive determination of the dynamic and static moduli of elasticity. One important, basic assumption of the proposed methodology considers values provided by technical literature for concrete dynamic Poisson’s coefficient. The air-dry density was experimentally determined on concrete cores. The dynamic modulus of elasticity was also experimentally determined by using the ultrasonic pulse velocity (UPV) method on concrete cores. Further on, the static modulus of elasticity and the concrete compressive strength can be mathematically calculated, by using the previously mentioned parameters. The experimental procedures were performed on concrete specimens, namely concrete cores extracted from the raft foundation of a multistorey building; initially they were subjected to the specific NDT, namely ultrasonic testing, and the validation of the results and the proposed methodology derives from the destructive testing of the specimens. The destructive testing is generally recognized as the most trustable method. The precision of the proposed method, established with respect to the destructive testing, revealed a high level of confidence, exceeding 90% (as mean value). It was noticed that even the cores with compressive strength outside of mean range interval (minimum and maximum values) presented high rate of precision, not influencing the overall result. The high rate of accuracy makes this method a suitable research background for further investigations, in order to establish a reliable NDT methodology which could substitute the very invasive and less convenient, destructive method.