Acoustic Emission Technology Research Articles

Study of the Dynamic Splitting Tensile Mechanical Properties and Damage Evolution of Steel Fiber–Reinforced Recycled-Aggregate Concrete Based on Acoustic Emission Technology

Study of the Dynamic Splitting Tensile Mechanical Properties and Damage Evolution of Steel Fiber–Reinforced Recycled-Aggregate Concrete Based on Acoustic Emission Technology

Metal surface crack depth laser acoustic emission detection method based on multivariate feature adaptive extraction and cross-modal interaction fusion

Surface crack depth detection of metal structures is of great significance to ensure the safe of equipment. However, the commonly used detection methods currently suffer from sensitivity and imaging dimension limitations, which make it difficult to ensure accuracy and feature extraction. To this end, a laser acoustic emission crack depth detection method that combines modal decomposition and deep learning is proposed. The new method is more sensitive to elastic wave changes that detect the depth of microscopic cracks and shows higher accuracy than traditional detection methods. At the same time, frequency-adaptive feature mode decomposition of multivariate signals and color multimodal feature fusion are used to solve the difficulty of time–frequency domain feature mining in current research. The crack depth detection network CDDNet is constructed using an attention-based fusion backbone, a novel cross-modal interaction fusion, and a hierarchical information fusion strategy. Its transfer learning achieves a crack detection accuracy level of 0.05 mm and outperforms other models in terms of accuracy, reaching 93.6 %. This paper shows that the proposed transfer learning laser acoustic emission technology and supporting data processing methods can effectively establish a connection between the experimental and simulation datasets, solving the current challenges of crack depth detection in terms of micro-precision and data processing.

An Adaptable Method for Rapid Fatigue Limit Prediction of Composite Materials Based on Acoustic Emission Technology

ABSTRACTEncouraged by the emerging infrared thermography (IT)–based technique for determining fatigue limit of composite materials, this study introduces a novel approach for the quick fatigue limit determination based on acoustic emission (AE) technology. The AE‐based method expands fatigue testing capabilities beyond the IT‐based methods. Fatigue experiments were conducted for the composite laminates and composite sandwich panels subjected to stepwise increasing cyclic loads. For composite laminate specimens subjected to tension–tension and tension–compression fatigue loads, the fatigue limits obtained by the AE‐based method are in good correlation with those acquired from the IT‐based method as well as the conventional up‐and‐down test procedure. In the tests for out‐of‐plane shearing fatigue limits of composite sandwich panel specimens, where the IT‐based method did not work properly, the AE‐based method performed well and obtained the fatigue limit estimation, which is in line with that derived from the S–N curve data, thereby demonstrating its high adaptability.

Damage evolution and failure mechanism of 2.5D woven composite tubes under quasi-static lateral compression

2.5D woven composites are ideal candidate materials for deep-sea pressure tubes owing to their excellent out-of-plane properties. This paper presents the damage evolution and failure mechanism of 2.5D woven composite tubes under quasi-static lateral compression. To conduct this study, 2.5D woven composite tubes with different thickness-to-diameter ratios, 0.04, 0.07 and 0.10, were designed and prepared. The quasi-static lateral compression tests were carried out in order to evaluate the progressive damage analysis, combining high speed photographic image with acoustic emission technologies. The results show that the increase of the ratio of thickness-to-diameter, the deformation and shear failure of the sample can be inhibited obviously. Due to the enhanced interlayer interaction, the lateral stiffness of the sample is obviously improved, so the lateral bearing stability of the sample is improved. When the peak load of samples with the thickness-to-diameter ratio of 0.1 reached 11.51 kN, it exceeded that of samples with thickness-to-diameter ratios of 0.04 and 0.07 by 450% and 82%, respectively. Furthermore, the failure mechanisms of samples with the thickness-to-diameter ratio of 0.1 were controlled by delamination fracture, whereas that of 0.04 and 0.07 were mainly influenced by shear failure and delamination failure, respectively.

Association of frailty and serum neurofilament light chain levels: the mediating role of estimated glomerular filtration rate.

Both frailty and elevated serum neurofilament light chain (sNfL) levels are linked to cognitive impairment. However, evidence of their relationship is lacking, and whether it was mediated by renal function was unknown. This study aimed to investigate the association between frailty and sNfL levels in a representative U.S. population, and to explore the potential mediating role of estimated glomerular filtration rate (eGFR) in this relationship. Data from 1,782 participants aged 20-75 years in the 2013-2014 National Health and Nutrition Examination Survey (NHANES) were analyzed. Frailty was assessed using a 49-item frailty index, and participants were categorized as non-frail, pre-frail, or frail. sNfL levels were measured using acoustic emission technology. Multivariable linear regression models and restricted cubic spline analyses were employed to examine the associations between frailty, eGFR, and sNfL levels. Mediation analysis was conducted to evaluate the role of eGFR in the frailty-sNfL relationship. The prevalence of pre-frailty and frailty was 45.39 and 11.60%, respectively. A significant positive association was observed between frailty score and sNfL levels (adjusted β: 39.97, SE: 11.07, p = 0.003), with a linear relationship confirmed by restricted cubic spline analysis. Frail individuals had significantly higher sNfL levels compared to non-frail participants (adjusted β: 11.86, SE: 5.42, p = 0.04). eGFR was negatively associated with sNfL levels (adjusted β: -0.23, SE: 0.05, p < 0.001). Mediation analysis revealed that eGFR accounted for 12.52% of the total effect of frailty on sNfL levels (p < 0.0001). This study demonstrates a significant association between frailty and elevated sNfL levels in a representative U.S. population, with eGFR partially mediating this relationship. These findings suggest that sNfL may serve as a potential biomarker for frailty-related neuronal damage and highlight the importance of kidney function in this association. Further research is warranted to explore the clinical implications of these findings in frailty assessment and management strategies.

Three-point bending damage detection of SiC coated C/C composites based on acoustic emission

In this study, the three-point bending process of needled C/C composites, pack cementation SiC coated-C/C composites (PC-SiC-C/C) and chemical vapor deposition SiC coated-C/C composites (CVD-SiC-C/C) were monitored by the acoustic emission (AE) technology. To study the bending damage evolution process of three kinds of composite materials, the K-means algorithm was used to analyze the AE characteristic parameters. The damage type was analyzed by combining the electron microscope image of the sample fracture. The findings revealed that C/C composites and CVD-SiC-C/C composites contain three distinct types of damage: matrix cracking, carbon fiber-pyrolytic carbon interface debonding, and fiber fracture under three-point bending loading. PC-SiC-C/C composites has two additional damage: coating cracking and pyrolytic carbon-SiC interface debonding. The various damages could be identified by different AE signals: matrix cracking (175.781–421.875 KHz), carbon fiber-pyrolytic carbon interface debonding (70.312–164.062 KHz), fiber fracture (2.929–46.875 KHz), coating cracking (375–585.937 KHz), pyrolytic carbon-SiC interface debonding (169.921–257.812 KHz). The cumulative AE energy shows that the order of damage types of the three composites during the three-point bending loading is different.

Experimental study on damage of steel fiber reinforced concrete- and wood-filled GFRP tubes under axial compression

In order to reduce the self-weight of concrete structures and improve its corrosion resistance, it is an effective way to fill GFRP tubes with wood columns instead of part of concrete. However, concrete structures are prone to cracks, which will reduce the bearing capacity of the structure and affect the safety and applicability of the structure. Incorporating the steel fibers (SF) in concrete seems to be an excellent solution to limit crack propagation and improve the ductility of concrete structures. Based on the premise, in this paper, a new type of structure composite material of steel fiber reinforced concrete- and wood-filled GFRP tubes was developed. The mechanical properties, damage evolution process and crack fractal characteristics of steel fiber reinforced concrete- and wood-filled GFRP tubes under axial compression with different SF volume fractions (0 %, 1 %, 1.5 % and 2 %) were studied by acoustic emission (AE) technology. The finite element method (FEM) was established to simulate the test work and expand the analysis. The results showed that the bearing capacity and ductility of the specimens increased first and then decrease with the increase of SF content, and the specimens with 1.5 % volume fraction of SF showed the best mechanical properties. According to the characteristics of AE ringing counts, the damage process of the specimen was described by three marker events, which were concrete cracking, wood cracking and GFRP tube fracture. The incorporation of SF increased the intensity of AE signal and the proportion of shear cracks, and reduced the damage scale. The fractal dimension of concrete surface cracks had a good correlation with the degree of local damage and the complexity of crack propagation. The damage evolution model based on AE cumulative events and stress obeyed the two-parameter Weibull distribution. The incorporation of SF reduced the degree of damage, and the parameter m had a negative correlation with brittleness. In addition, the damage process of the specimens revealed by the FEM matched well with the experimental results. Additionally, the incorporation of SF made the stress distribution of each part of the specimen more uniform and avoided the phenomenon of stress concentration.

Experimental study on mechanical and acoustic emission characteristics of red sandstone containing combined flaws of hole-fissure under biaxial compression

To investigate the mechanical behaviour and failure mechanism of flawed rock masses under biaxial stress conditions, a series of biaxial compression experiments are conducted on red sandstone specimens containing combined flaws of the circular hole and perforated symmetric fissure combined with acoustic emission (AE) technology in this work. The results show a close association between the stress-strain curve morphology and confining stress, both the biaxial compression strength and elastic modulus exhibit an upward trend with increasing fissure angle and confining stress. The maximum AE energy and cumulative AE energy both increase with higher confining stress. The crack types in the specimens are identified by analyzing the AF/RA value distribution, revealing a gradual decrease in the percentage of tensile cracks with increasing fissure angle. An AE localization algorithm based on the least absolute value method is applied to pinpoint AE events during biaxial compression, and AE events distribution is analyzed through the Kernel density estimation (KDE). The crack extension behaviour is described based on the movement of the maximum kernel density points, which initially exhibits a progression from both ends of the specimen towards the central region and subsequently from the central fissure tip towards the two ends.

Acoustic Emission characteristics and damage evolution of Concrete-Encased CFST columns under compressive load

Concrete-Encased Concrete Filled Steel Tubular (CE-CFST) is usually served as a compression member in engineering structures due to its high performance. It is crucial to reveal its compressive failure mechanism and the damage evolution law. This study used Acoustic Emission (AE) technology to monitor the compressive failure behavior of seven groups of CE-CFST columns with different diameter-width ratio, slenderness ratio and eccentricity, and then the AE signal characteristics and structural damage evolution law were discussed. Results show that the curve of AE characteristics can be used for effectively identifying the damage stages of CE-CFST structures. The failure process can be divided into six stages: initial compaction, stable growth of micro cracks, unstable propagation of macro cracks, collapsing of the outer RC structure, bulging of the core CFST structure and overall failure. The AE characteristic parameters, RA-AF value and b value are closely related to stress and crack of structure, and can be used for early warning of structural failure during the stage of unstable crack propagation. Particularly, the failure of outer concrete can be judged as b value drops to the minimum. The current study is of great significance for understanding the damage evolution process and achieving damage assessment of CE-CFST structures.

Variable frequency selective structure (CFRP/Cu/CFRP) micro blind hole laser high-quality drilling method guided by acoustic emission technology

The laminated structure of carbon fiber reinforced plastic (CFRP)-Cu-CFRP is an important structure for aerospace radome. The blind holes manufacturing is core process, but there are technological challenges in achieving the exposure of a 10 μm thick copper foil at the bottom of the blind hole. The exact position of the 10 μm thick copper foil within the material’s internal space is unknown, and the uneven heat accumulation and conduction can easily cause burning and damage to the ultra-thin copper foil. To date, there is no publicly accessible literature that documents the execution of this specific process. In this paper, novel variable inner diameter spiral scanning guided by Cu-CFRP interface accurately extracted using cosine similarity (CS) algorithm is proposed to obtain a blind hole with near-zero ablation damage. The similarity of acoustic emission signals generated by laser interaction with Cu and CFRP, as well as thermal accumulation and conduction behavior under different scanning radius, are described. The high-quality blind holes have been successfully drilled, with a large area copper foil exposed at the bottom, while the surface of the copper foil remaining intact without any damage or heat affected zone. Compared with traditional drilling method, the average exposed copper foil area at the bottom of the blind holes is increased by 238.4 % (from 0.464 mm2 to 1.570 mm2). And the average taper of blind hole is reduced by 41.7 % (from 0.470 to 0.274). This method can effectively increase the area of exposed copper foil and reduces the taper of blind holes, improve the welding strength and conductivity of wires in aerospace radomes, thereby enhancing the stealth performance and operational reliability of fighter jets.

Study on bonding properties and constitutive model of steel bar and nano-SiO2 reinforced recycled aggregate concrete

Optimizing design and ensuring the structural safety of recycled aggregate concrete (RAC) structures requires a thorough understanding of the bond-slip mechanism between steel bars and RAC. Previous studies have demonstrated the significant enhancement of the microscopic properties of RAC and its bond with steel bars due to nano-SiO2. However, the structural bonding mechanism between the steel bar and nano-SiO2-enhanced RAC remains unclear. Therefore, this study investigates the influence of nano-SiO2 on the bond performance between RAC and steel bars, elucidating the enhancement mechanism through microscopic techniques. Additionally, various factors affecting the bond-slip performance of RAC specimens are analyzed, leading to establishing a multifactorial coupling formula considering thickness-to-diameter ratio, concrete strength, and relative anchorage length. Furthermore, slip index correction formulas and a constitutive model are developed, considering these factors. Acoustic emission (AE) technology monitors the bonding process, clarifying the evolution of AE digital signals and bond damage history. Employing the finite element method, a numerical model of bond-slip behavior is established, revealing specimens' changing bond strength and failure modes. Experimental results indicate that increasing nano-SiO2 content reduces structural porosity, leading to denser and more homogeneous microstructures, thereby enhancing RAC's mechanical properties and bonding performance. Moreover, AE techniques effectively monitor bond damage at different stages. Finally, a bond-slip constitutive model is proposed for steel bar and nano-SiO2 reinforced RAC, offering insights for numerical calculations and engineering designs.

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Microcracking evolution and clustering fractal characteristics in coal failure under multi step and cyclic loading

During the deep mining process, coal mass encounter intricate geo-environmental stress, such as periodic weighting loading and repeatedly excavation unloading–reloading cycles, which weakens coal’s mechanical integrity and predisposing it to severe coalburst accidents. To investigate the microcracking damage mechanisms and predictive indicators in coal failure under in-situ stress analogs, the multistage step and cyclic loading experiments are conducted on cubic coal specimens. Acoustic emission (AE) technology is employed to track the spatiotemporal-energy evolution of stress-induced damages and discern the microcracking nature through AF/RA assessments, and the power-law scaling relation of AE activity near the catastrophic failure of coal is investigated. Then the clustering fractal structures of microcracking events in the stressed coal are quantified across temporal, spatial and energetic domains, utilizing correlation integral methodologies and b-value derivations from magnitude-frequency relation. Findings indicate that irrespective of the loading mode (step or cyclic), escalating stress triggers an intensification of irreversible fatigue deformations. AE characteristic parameters manifest a gradual rise, culminating in a precipitous peak coinciding with the critical failure point. This escalation adheres to a power-law correlation between AE occurrence frequency and time to failure, observable in the immediate pre-failure seconds, reflecting a universal attribute of coal fracture. Prior to ultimate failure, a marked increase in shear microcracks is discernible, despite tensile-dominated cracks (constituting about 80 % of total microcracks) prevailing as inferred from the variation of AF/RA values, aligning with an inferred “X” conjugate wedge splitting pattern from AE event density and energy mapping. The microcracking events in the loaded coal exhibit a clustering fractal structure that spans across temporal, spatial, and energetic (or magnitude) domains. Notably, the temporal fractal dimension, spatial fractal dimension, and b-value (i.e., a parameter characterized the energetic fractal dimension) all follow a parallel decrease pattern as the loading stress escalates, with a pronounced diminution becoming especially evident as the specimen approaches its catastrophic failure threshold. This insight offers fresh perspectives for predicting rock/coal dynamic disasters, emphasizing the necessity of concurrently monitoring the shift from diffuse microcracking to localized failure across time, space and energy domains. These research findings contribute to a deeper understanding of microcracking damage evolution and failure mechanism of loaded coal, and provide a foundational basis for early warning of rock failure such as the coalburst disasters.

Experimental study on acoustic emission damage in precast reinforced concrete interior joints containing disc springs

To address the inferior performance of precast reinforced concrete beam-column joints compared to cast-in-place joints during earthquakes, this study designed a precast concrete frame beam-column interior joint incorporating disc springs. The disc springs, installed based on grouted sleeve connections, aimed to enhance structural ductility and mitigate seismic forces on the joints. Five joints, including one cast-in-place joint, two ordinary precast joints, and two precast joints with disc springs, were tested quasi-statically and monitored using acoustic emission technology. The precast joints had axial compression ratios of 0.2 and 0.4. Parameters such as energy, count, and b-value from acoustic emission monitoring were analyzed. The Gaussian mixture model was employed for the clustering analysis of RA-AF parameters, and a damage model was developed to evaluate performance differences among the joints in the quasi-static tests. The results indicate that precast joints with the same axial compression ratio can achieve performance similar to cast-in-place joints, with more rapid damage development at higher axial compression ratios. Installing disc springs decelerates damage progression in the later stages of loading, maintaining joint integrity for a certain period even after reaching maximum load, thereby demonstrating improved ductility. At higher axial compression ratios, the b-value curve of joints with disc springs more closely resembles that of ordinary joints. Disc springs' positive effect is more pronounced in clustering analysis and damage models, such as their lower proportion of shear fractures.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.

Acoustic emission-based weld crack leakage monitoring via FGI and MCCF-CondenseNet convolutional neural network

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment.

Effect of CO2 Nanobubble Water on the Fracture Properties of Cemented Backfill Materials under Different Aggregate Fractal Dimensions

In order to cope with climate change and achieve the goal of carbon neutrality, the use of carbonization technology to enhance the performance of cement-based materials and achieve the purpose of carbon sequestration has become a very promising research direction. This paper considers the use of CO2NBW as mixing water for cement-based materials, aiming to improve the carbonization efficiency of materials to achieve the goal of carbon neutrality. This time, the effect of CO2NBW on cementitious filling materials under different aggregate fractal dimensions was studied through uniaxial compression tests and acoustic emission technology. The effect of CO2NBW on the mechanical properties and crack evolution of the material was discussed. The results showed that CO2 nanobubbles significantly improved the strength of cemented filling materials under different fractal dimensions, and the uniaxial compressive strength was most significantly improved by 23.04% when the fractal dimension was 2.7824. In addition, the characteristics of acoustic emission ring counts and energy parameters indicate that CO2 nanobubbles help improve the overall pore structure of the sample, affecting the macroscopic strength. However, the addition of CO2 nanobubbles reduces the limit energy storage ratio of elastic strain energy, which indicates that excessive CO2 concentration may affect the hydration reaction of the cementing material.

Effect of nano-metakaolin on the early-age fracture properties of cement mortar

In this paper, the fracture performance of nano-metakaolin (NMK) cement mortar during early hydration period was investigated. Different NMK contents (0 %, 3 %, 5 %, 7 %) were considered, and three-point bending tests were conducted. The digital image correlation technology and acoustic emission technology were utilized to analyze P-CMOD curves, initial fracture toughness, unstable fracture toughness, fracture energy, cumulative AE hits, and cumulative AE energy, AE source locations of cement mortar at early hydration stages. Furthermore, SEM experiments were utilized to analyze the mechanism by which NMK affects fracture performance of cement mortar. The results indicate that the incorporation of NMK enhances the fracture properties of cement mortar, with the most suitable NMK content being 5 %. The addition of NMK increases the cracking load, peak load, and the CMOD at the peak load of the cement mortar at 3 d, 7 d, and 14 d. The cracking load and fracture energy of the cement mortar with 5 % NMK increase by 22.5 % and 35.7 % respectively, compared to ordinary cement mortar at 14 d. NMK enhances the initial fracture toughness and unstable fracture toughness of the cement mortar, improving its crack propagation resistance. The unstable fracture toughness of the cement mortar is the largest when the NMK content is 5 % at 14 d, which is 31.6 % higher than that of ordinary cement mortar. Furthermore, NMK increases the cumulative AE energy and fracture energy of the cement mortar, increasing the cumulative ring counts and cumulative AE hits, causing the crack propagation path to deviate and resulting in improved crack resistance. The cumulative ring counts and AE hits for the cement mortar with 5 % NMK are 42.9 % and 66.2 % higher, respectively, than those for ordinary cement mortar at 14 d. NMK significantly modifies the cement matrix, thereby enhancing the fracture resistance of cement mortar.

Characterization of damage behavior and failure mechanisms of the bonded‐bolted hybrid three‐bolt joint in composite laminates

AbstractIn this paper, the mechanical properties, damage modes, and bolt load distribution of the bonded‐bolted hybrid three‐bolt joint were investigated by a combination of experiments and numerical simulations. Acoustic emission (AE) was used to monitor the tensile process of the connection in real time, and the K‐means clustering algorithm was used to realize the cluster analysis of the damage modes. A numerical simulation model of the tensile process of the joint was established using ABAQUS simulation software. The progressive damage evolution of composite laminates, and adhesive layer was characterized by using the VUMAT subroutine, the B‐K failure criterion, and cohesive element with zero thickness, respectively. The results show that four main damage modes occur in composite laminates during tension: matrix cracking, delamination, fiber pull‐out, and fiber breakage, with corresponding peak frequency ranges of (0–80 kHz), (80–210 kHz), (210–330 kHz), and (330–375 kHz), respectively. The adhesive layer between the laminates mainly exhibits peeling failure because of the secondary bending effect. In the load‐bearing process, the adhesive layer was loaded first and failed, and the bolt loading was delayed. Meanwhile, the results of bolt load distribution showed that the bolts and the adhesive layer at both ends carried greater loads.Highlights Damage modes and damage evolution of the bonded‐bolted hybrid joint in tension were analyzed by combining acoustic emission technology and numerical simulation. The simulation of delamination damage in composite laminates was carried out by inserting zero‐thickness cohesive elements into the laminates, and the delamination damage between the carbon fiber layup and the adjacent glass fiber layup was analyzed. The effect of the inclusion of the adhesive layer on the mechanical properties of the joint and the distribution of the bolt load was analyzed.