Results Of Non-destructive Tests Research Articles

Numerical simulation of strengthening/deterioration and damage development of cementitious materials under sulfate attack environment

Cementitious materials exposed to sulfate environments often suffer from the combined effects of external sulfate attack and dry wet cycling. In order to predict the long-term performance of cement-based materials in sulfur rich environments, this paper aims to establish a numerical model that can reasonably characterize the degradation process of cementitious materials in sulfate environments. Using this model, we can address durability problems common to cementitious materials in real sulphate environments, such as determining the degree of corrosion, predicting the durability life of materials, and calculating the time to failure. The model consists of three parts: transportation, chemical reactions, and material damage. We establish the degradation model for cement-based materials in sulfate environments by considering ion diffusion, water convection, chemical reactions, accumulation of corrosion products, and the development of material strengthening/degradation. Compared with existing sulfate corrosion models, this model not only considers ettringite, but also takes into account the volume changes of gypsum formation, calcium leaching, and salt crystallization precipitation, which can more reasonably simulate the sulfate corrosion process in real environments. The model can reasonably simulate the distribution pattern of ion concentration, hydration products and corrosion products in the cementitious material, and on this basis calculate the porosity of the cementitious material. In order to make the simulation results more applicable to experimental and non-destructive testing results, the model uses easily obtainable relative dynamic elastic modulus to evaluate the degree of material strengthening/degradation and predict the durability life of the material.

Influence of geometric factors on cyclic durability of rotor main parts of the hot section of aircraft engines. Validation using lumen control results

A study was conducted of the influence of some geometric factors on such parameters as the position of dangerous points determining the cyclic durability of a part, and the values of cyclic durability at these points. Based on the results of the validation carried out using the results of non-destructive testing and fractographic studies of opened cracks in the main part with operational defects, the adequacy - the degree of compliance with the research results - of the calculation models used to determine the cyclic durability was established. The work was carried out as part of the task of determining the resource indicators of the main part, taking into account the presence of an initial defect.

AI optimization and mathematical simulation validated by nondestructive testing for resonance frequency in advanced composite structures for bridge applications

This paper presents a novel approach integrating artificial intelligence (AI) optimization and mathematical simulation to predict the resonance frequency of nanoclay-reinforced concrete cylindrical shell structures intended for bridge applications. These composite structures, known for their enhanced mechanical properties, require precise evaluation of their vibrational behavior to ensure structural stability and longevity. Traditional methods for predicting resonance frequencies are often time-consuming and prone to inaccuracies, especially in complex materials like nanoclay composites. To address this, an AI-based optimization algorithm was developed, incorporating Particle Swarm Optimization (PSO) and a mathematical modeling to simulate resonance characteristics under varying material and geometric parameters. The mathematical modeling is validated using nondestructive testing (NDT) techniques, such as modal analysis, which provided real-world resonance data without damaging the structure. The nondestructive testing results is compared against the mathematical model to ensure accuracy and reliability. The integration of nanoclay into the concrete matrix significantly altered the vibrational properties, enhancing the stiffness and reducing damping losses, which is crucial for bridge applications where dynamic loads are prevalent. The optimized model not only predicted resonance frequencies with high accuracy but also demonstrated its potential for large-scale bridge infrastructure. This methodology offers a streamlined and robust tool for engineers, reducing the need for physical prototyping and providing enhanced design capabilities. Future research will focus on expanding the model to incorporate additional material behaviors and load conditions, furthering its application in civil engineering.

The Impact of Dynamic Effects on the Results of Non-Destructive Falling Weight Deflectometer Testing.

The article investigates the impact of applying a dynamic computational model that considers inertia forces on pavement deflections under rapidly changing loads over time. This study is particularly relevant to the modelling of falling weight deflectometer (FWD) testing. Initially, the article examines the deflection values obtained from computational models under loads with varying frequencies. In this context, considering inertia forces was significant for load durations shorter than 0.04 s. In such cases, the results of static and dynamic analyses differed considerably. One application of FWD measurement results is determining the stiffness moduli of pavement layers using backcalculation. The study explored the impact of incorporating inertia forces into the pavement model on the estimated values of stiffness moduli obtained via backcalculation. The results revealed differences of several percent between the stiffness moduli calculated using dynamic and static numerical models. Subsequently, the key pavement deformations and fatigue life were determined using the obtained moduli. Again, significantly different results were observed between dynamic and static cases. Based on these findings, it can be concluded that dynamic effects should not be ignored when using FWD testing for backcalculation. Additionally, the article addresses the sensitivity of backcalculation results, which is crucial for the accurate interpretation of the obtained data.

Experimental investigation of heat treatments on the mechanical performance of grade 300 maraging steel strut-based lattices

Lightweight and strong components are essential for reducing energy consumption and enhancing efficiency. Lattice structures are one such geometry utilized to achieve weight reduction. This study investigates the mechanical properties of various lattice structures fabricated from Maraging Steel (EOS MS1) using the Direct Metal Laser Sintering (DMLS) method. The samples include three distinct cellular geometries: body-centered cubic (BCC), face-centered cubic (FCC), and octet truss configurations, which are subjected to tensile and compressive tests. The primary goal of this research is to evaluate the impact of heat treatment on the mechanical properties of cellular architecture under tensile and compressive loading conditions. Destructive, nondestructive testing, and simulation results were also obtained from different heat treatment processes. It was found that the age-hardened specimens performed the best overall in terms of ultimate tensile/compressive strength and elongation. The top-performing topologies in compression and tension were found to be the octet structure, as they were able to withstand the most loading and straining when compared to the other specimens.

New Approach for Conditional Coring in RC Structures Using Bivariate Distributions of Nondestructive Test Results

New Approach for Conditional Coring in RC Structures Using Bivariate Distributions of Nondestructive Test Results

Use of Machine Learning Techniques to Predict the In-situ Concrete Compressive Strength using Non-Destructive Testing

Recently, machine learning-based prediction models have gained considerable popularity in predicting the compressive strength of concrete using results from Non-Destructive Testing (NDT) methods. This research considers NDT results from Rebound Hammer (RH) and Ultrasonic Pulse Velocity (UPV) tests for concrete samples with different concrete grades. 270 test samples were considered for the analysis using different machine learning techniques such as Multi-Layer Perceptron (MLP), Long Short-Term Memory network (LSTM), and Convolutional Neural Network (CNN). Concrete compressive strength results are obtained from Destructive Test (DT) results of the same for the purpose of labelling. Each model's performance is evaluated through the computation of the Coefficient of Determination (R²), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). The findings of this study underscore the potential of machine learning techniques in predicting concrete compressive strength based on Non-Destructive Test (NDT) results. The analysis findings suggested that employing UPV and RH data in conjunction with a trained MLP model to be an effective approach for accurately estimating the compressive strength of concrete specimens. The outcomes of the research serve as a valuable reference for researchers and industry practitioners alike when assessing in-situ concrete compressive strength through Non-Destructive Testing (NDT) methods.

A Methodology to Manage and Correlate Results of Non-Destructive and Destructive Tests on Ancient Timber Beams: The Case of Montorio Tower

Intending to safeguard architectural heritage, the assessment of the health of timber structures is crucial, though challenging, due to the organic nature of wood and to the uncertainties of its preservation state. To this end, useful support is provided by the ICOMOS guidelines, which provide conservation strategies based on thorough diagnosis and safety evaluations. In this context, the study summarized in this paper focuses on the medieval Tower of Montorio, which suffered considerable damage due to the strong earthquake that occurred in those area in September 2003. Its subsequent process of rehabilitation and restoration involved a widespread experimental campaign and the substitution of some timber beams. This circumstance has offered a rare opportunity to study these ancient elements in detail, although they are limited in number. Six beams made of oak and removed from an intermediate floor of the tower were evaluated through a comprehensive approach that included both non-destructive tests (NDT) and destructive tests (DT). Particularly, they were subjected to visual inspections, ultrasonic, sclerometric, and resistographic methods, and destructive four-point bending tests. Overall, the study presented here provides a useful qualitative comparison between them. Results highlighted that relying only on NDT might lead to an overestimation of mechanical properties and that combining NDT with DT is crucial for a more accurate assessment. Therefore, the need to deepen the research on correlations between NDT and DT to obtain reliable values of mechanical properties while respecting the conservation aim was confirmed.

Применение машинного обучения и интеллектуального анализа данных в автоматизированной системе неразрушающего вихретокового контроля поверхностного слоя деталей подшипников

Changing the properties of the material during physical and mechanical processing can significantly reduce the working life of the manufactured product, therefore it is important to control the quality of the surface layer of the parts. To solve this problem, non-destructive testing techniques such as etching, visual, capillary, magnetic powder, ultrasonic, vibration, eddy current methods are used at bearing enterprises. The article discusses the physical foundations of the presented techniques and provides their comparative analysis. Machine vision and digital signal processing approaches can be used to automate the processing of the results of non-destructive testing of the surface layer of bearing parts within the framework of the Industry 4.0 concept. From the point of view of productivity and the possibility of integration into the production system, the eddy current method is the most promising, the result of surface control in this way is an array of digital values. The development of modern methods of information analysis makes it possible to efficiently process a large amount of data, and machine learning makes it possible to solve problems of classification, regression, etc. This article provides methodological support for the development and application of an automated eddy current control system using machine learning and data mining methods. The works of scientists devoted to the processing of the results of the shock control of various objects, including bearing parts, are considered, it is noted that previously attention had not been paid to the issue of a reasonable choice of a machine learning model for recognizing defects on the surface of parts. The possibility of using the median polishing method to transform the eddy current signal is shown. The development and implementation of a bearing defect recognition system based on the methodological support presented in this paper can significantly improve the efficiency of product quality control and optimize the technological process.

Thermal nondestructive testing of cracks in turbine blades by using ultrasonic stimulation

The results of nondestructive testing (NDT) of a turbine blade made of a heat resistant alloy with a ceramic coating were obtained by using ultrasonic infrared (IR) thermography. The purpose of the study was to determine possibilities of this NDT technique in detecting cracks in the blade and the coating. Image processing was performed by using principal component analysis, which allows to underline defect indications in IR images. The obtained test results were in a good accordance with the results obtained by means of penetrants with a considerably shorter inspection time. Potentials of IR ultrasonic thermography in the detection of “kissing: cracks and cracks located in difficult to reach sites was demonstrated.

Non-destructive characterization of resistance projection welded joints by ultrasonic and passive magnetic flux density testing

The weld of resistance projection welded joints is not visible from outside. Therefore, visual evaluation is restricted, and visual inspection is not possible at all. So far, the parameters of the welding process are monitored and controlled. In addition, the welds are periodically tested destructively to determine the quality of the welds. No industrial standard has yet been established in the field of non-destructive testing (NDT) for projection welded joints. This study focuses on NDT of projection welds using two different ultrasonic imaging inspection systems and the passive magnetic ux density testing (pMFT) method. The ultrasonic inspection systems commercially available and established in the field of NDT of spot welds. Both systems are originally designed for the NDT of spot welds and not for projection welds. Unlike the ultrasonic systems, the pMFT is still in laboratory status. The method has originally been developed to evaluate spot welds and is also used in this study to evaluate projection welds. The applicability of the investigated systems to projection welding is investigated in order to derive mandatory development steps to achieve reliable results. The pMFT method shows also good results for NDT of spot welds In this contribution, the measurement and evaluation concept of the three NDT systems for projection welded joints is presented. The NDT results are discussed in the context of the corresponding destructive results in terms of tensile forces and fracture areas. Advises for further development of all investigated systems are given.

Estimation of concrete compressive strength from non-destructive tests using a customized neural network and genetic algorithm

This paper introduces a novel approach for deriving an equation aimed at predicting concrete compressive strength, leveraging two non-destructive test results: ultrasonic pulse velocity and rebound hammer value. Traditional regression methods often fail to capture the complex non-linear relationships crucial for accurate concrete CS estimation, leading to limited effectiveness. In contrast, while machine learning models excel at mapping these non-linear relationships from non-destructive test results, their intricate internal mechanisms can be opaque, posing challenges for practical application and comprehension by engineers. This study aims to bridge these gaps by developing an estimation equation derived from the decoded weights of a neural network specifically designed to capture the intricate nonlinear mapping relationship. Initially, the equation is intricate and lengthy, comprising multiple terms. To streamline and refine it, a genetic algorithm is employed, focusing solely on the most pivotal terms. Consequently, the refined prediction equation demonstrates superior estimation performance with an RMSE of 3.40 MPa, an MAE of 2.70 MPa, an R2 of 0.92, and an R of 0.97 compared to several existing formulations. Furthermore, a comparative analysis is undertaken to assess the impact of the degree of equation simplification on its predictive accuracy. The findings offer insights into the pivotal roles of exponential function terms in enhancing prediction performance, as well as elucidating trigonometric function terms contribute the model’s complex non-linear mapping capability.

Methods and means of ensuring the accuracy of bending deformation measurements of connecting pipe between the VVER reactor and the steam generator in the conditions of a fuel campaign

The welded joint zone (WJ) No. 111 is a welding unit for the "hot" collector of the primary coolant to the DN1200 steam generator nozzle. The experience of operation of steam generators PGV-1000M of NPP power units with VVER-1000 shows that the WJ zone No. 111 is prone to the formation and development of operational defects. According to the results of periodic non-destructive testing of the WJ No. 111 zone performed at the PG of various power units of the VVER-1000 NPP, cases of detection of extended planar-type discontinuities, including through ones, were repeatedly recorded. Thus, this zone is one of the most critical zones of the reactor plant (RU) with VVER -1000. The root cause of the formation and development of operational defects has not yet been identified. Solving the problem of cracking of WJ No. 111 steam generators of nuclear power plants with VVER-1000 is one of the priority areas for improving the safety of operation of the power unit during the over-design service life. This problem is determined by a complex combination of temperature conditions, mechanical and corrosive effects, is relevant and has not yet been definitively solved. Therefore, it is very important to ensure the collection of reliable data on the actual stress-strain state of the WJ No. 111 zone in various operating modes (pressure loading of steam generators, heating/cooling of the power unit, hydraulic testing of the 1st and 2nd circuits). The article reveals the approaches and stages of creating and applying a strain gauge installation to ensure metrological suitability in the conditions of a campaign fuel company at an NPP power unit by using an adequate physical model to obtain additive and multiplicative correction coefficients.

Application of neural fatigue cohesive element to R-DCB model with in-situ training strategy

In this work, a recently developed fatigue delamination growth (FDG) simulation tool driven by neural network (Neural Fatigue Cohesive element, NFCOH) is validated using a benchmark model that offers complex crack front known as the R-DCB model. The NFCOH is tested using two different training strategy for the driving neural network, i.e., a regular training strategy and an in-situ training strategy. For the regular training strategy, virtual samples based on material fatigue properties in the form of Paris parameters are generated to train the neural network. Whilst for the in-situ training strategy, samples are generated using non-destructive testing (NDT) results of the R-DCB model. The results for both strategies are presented and compared to demonstrate the advantages as well as disadvantages of each strategy and NFCOH in general.

Correlation between thermal and density properties of chestnuts: preliminary results of experimental non-destructive testing

This study presents the preliminary outcomes of a methodology for the physical and mechanical characterization of various chestnut elements in different states of preservation. Strategizing conservation and retrofit interventions for timber is necessary, and to do this, it is necessary to establish an estimation of physical (transmissivity, thermal conductivity, humidity level, etc.) and mechanical properties (density, compressive or bending strength, etc.). This essential information is typically associated with timber defects, but there are lack of correlations. The primary objective is to establish correlations between thermal and density properties with the aim of preserving original assets. The investigation delves into the relationship between timber density and thermal properties through experimental non-destructive testing (NDT). Two NDTs were employed with the aim of correlating: penetrometric testing and active thermography investigations. The parametric study on the excitation period yielded valuable insights into the temporal dynamics of heat transfer within the timber, underscoring the significance of selecting appropriate excitation periods to capture precise thermal properties. Tabular data on relative humidity for salified, dried, and new samples provided a quantitative backdrop to these observations, unveiling the nuanced effects of humidity on the timber’s thermal response. The results of this study are positioned to inform future conservation efforts by laying the groundwork for a comprehensive understanding of timber’s mechanical properties. Particularly, the challenge lies in accurately estimating density, where surface tests are often less reliable than in-depth ones. Therefore, it is crucial to seek validation through other NDT tests, such as thermographic analysis and visual inspection, and hygrometric tests recognizing their importance in enhancing the reliability of density assessments. This approach will contribute to the development of more discerning preservation strategies.

Ultrasonic non-destructive inspection method for glass fibre reinforcement polymer (GFRP) hull plates considering design and construction characteristics

The design and construction characteristics, including glass fibre weight fraction ( Gc), number of single-ply layers, fabric combination, and fabrication quality, of glass fibre reinforcement polymer (GFRP) hull plates affect ultrasound propagation characteristics, such as ultrasonic velocity and attenuation, thereby influencing the accuracy of ultrasonic non-destructive test results. Therefore, this study is to propose a method to decrease the ultrasonic test errors of GFRP hull plate by using statistical method. The GFRP specimens with Gc of approximately 30–50 wt%, thicknesses of approximately 5–20 mm, and different fabric combinations were prepared using the hand lay-up method, considering the general design–construction characteristics. Further, an ultrasonic velocity decision method for ultrasonic inspection was proposed considering the GFRP hull design-construction characteristics, such as Gc and number of single-ply layers of hull plate by multiple linear regression method. The results show that the proposed method can reduce the thickness measurement error from approximately 20%–30% to 1%–2%, compared to an existing ultrasonic inspection method, only considering the Gc effect on ultrasonic velocity, which indicates that the proposed test method is suitable for practical applications.

Novel flat-top laser-aided cold metal transfer additive manufacturing for titanium alloy: Arc characteristics, microstructure, and tensile properties

In this paper, a novel flat-top laser-aided cold metal transfer additive manufacturing (F-LCAM) method is proposed for titanium alloys to restrict internal defects and anisotropy of mechanical properties in large-scale additive-manufactured titanium structures. The proposed method uses a flat-top laser to stabilise the drifting cathode spot and suppress the irregular shape of the weld deposit by promoting thermionic emission on the molten pool surface. Compared to the cold metal transfer additive manufacturing (CMT-AM) process, the auxiliary flat-top laser promotes the wettability of the molten pool and increases the contact angle of deposition from 95° to 142° in an F-LCAM sample. The non-destructive X-ray testing (NDT) results of multi-bead multilayer deposition showed that the defects caused by the pool weld bead morphology were effectively suppressed. Attributed to fluid flow in the melt pool enhanced by the flat-top laser, the dendritic arms were broken, columnar β grains in the F-LCAM sample were finer than in CMT-AM sample, and the aspect ratios of prior-β grains decreased from approximately 6 to 1. The F-LCAM sample exhibited a higher elongation and lower anisotropy than the CMT-AM sample. Defects were observed on the fracture surfaces of the CMT-AM vertical samples, which reduced the elongation of the CMT-AM samples to lower than that of the F-LCAM sample. The ultimate tensile strength, yield strength, and elongation of the multi-bead multilayer deposition met the standards of as-forged Ti6321 titanium alloy, indicating that the Ti6321 titanium alloy structure fabricated by the F-LCAM process has acceptable tensile properties for engineering applications.

Enhancing load prediction for structures with concrete overlay using transfer learning of time–frequency feature-based deep models

This study proposes a method to predict applied loads on bonded structures via non-destructive testing results. Various types of concrete, such as high-performance alkali-activated slag concrete (HPAASC) and regular Portland cement concrete (OPCC) are simultaneously tested via ultrasound testing and bi-surface shear for extracting features and target values, respectively. Variational Mode Decomposition is employed to extract useful features from ultrasound signals. In the first part, selected features using a PCA-based method are utilized to train a set of deep-learning models to estimate the imposed mechanical load on the tested specimens. The most effective models from the first step, i.e., a CNN-LSTM model and LSTM models, are then fine-tuned in the second step to estimate loading conditions in another set of specimens. Results indicate the superior performance of the CNN-LSTM model. The proposed algorithm highlights the effectiveness of ultrasound features in accurately evaluating structural loading conditions for efficient monitoring of various structures.

Damage of a post-tensioned concrete bridge – Unwanted cracks of the girders

The cracking of a post-tensioned T-beam superstructure, which was built using the incremental launching method, is analyzed in the paper. The problem is studied in detail, as specific damage was observed in the form of longitudinal cracks, especially in the mid-height zone of the girder at the interface of two assembly sections. The paper is a case study. A detailed inspection is done and non-destructive testing results of the girders are briefly discussed. The attention is especially focused on advanced and comprehensive numerical simulations of the bridge mechanical behavior. Linear and nonlinear static calculations are performed employing the Finite Element Method at global and local levels of precision, enabling deep insight into the bridge response during all the stages of bridge construction and after it is opened to traffic. The crack propagation process in local analyses is described by the application of the Concrete Damage Plasticity law, the parameters of which were carefully chosen. The predicted damage patterns closely resemble those observed at the site. The results reveal, that the girder damage process was initiated when centric prestressing was applied, because vertical reinforcement of the assembly section front-end surface was not designed. However, the damage did not compromise the safety of the bridge. Finally, the repair methods employed are described and also a discussion is presented on how to prevent the occurrence of such cracking.

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation.

Amidst escalating environmental concerns, short natural-fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load, and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% with regard to the impact load, absorbed energy, and tensile strength, respectively.