Search Results (817)

Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing damage mechanisms in these materials is challenging. Several scientific studies have examined the root causes of GFRPC failure using various methods, including non-destructive techniques and learning algorithms. Despite this, ongoing investigations aim to accurately detect mechanical defects in GFRPCs. This study explores the use of non-destructive testing (NDT) combined with unsupervised learning algorithms to identify and classify damage mechanisms in GFRPCs. The NDT method employed in this study is acoustic emission (AE), which identifies waveforms associated with various failure mechanisms during testing. These waveforms are categorized using unsupervised learning methods such as principal component analysis (PCA) and self-organizing maps. PCA selects the most appropriate AE descriptors for distinguishing between different damage mechanisms, while the self-organizing maps algorithm performs clustering analysis and classifies failure mechanisms. Scanning electron microscope images of the observed failures are provided to sup-port the findings derived from AE data. Full article

As coal mining progresses to greater depths, the complex geological conditions significantly increase the risk of compound disasters. With increasing mining depth, elevated ground stress and gas pressure exacerbate the coupling effects of rockburst and gas outburst. This study employs laboratory tests and theoretical analysis to investigate gas disasters under varying gas and confining pressures. The experimental results are analyzed in terms of mechanical parameters, crack propagation, and acoustic emission (AE) time–frequency evolution. Under conventional compression, coal failure exhibits shear damage with axial splitting or debris ejection. The peak strength demonstrates a clear confining pressure strengthening effect and gas pressure weakening effect. At constant gas pressure, the elastic modulus increases with confining pressure, whereas at constant confining pressure, it decreases with rising gas pressure. Full article

Existing studies have shown that placing 3D-printed lattices in cement matrices can effectively improve the ductility of cement-based composites. However, the influence of thermal fatigue effect on the mechanical properties of 3D-printed lattice-reinforced cement-based composites during service remains to be studied. In this paper, cement-based materials without lattices were used as the control group, and the uniaxial compressive mechanical properties of 3D-printed lattice-reinforced cement-based composites after thermal fatigue treatment under a temperature difference of 60 °C were tested. The number of thermal fatigue cycles was set to 45, 90, and 145 times, respectively. During the test, two non-destructive testing technologies, AE and DIC, were used to analyze the strength degradation and deformation law of 3D-printed lattice-reinforced cement-based composites with the increase in cycles. AE adopted the threshold triggering mode, and the channel threshold was 100 mv. The experiment showed that the compressive strength of the control group after 45, 90, and 145 thermal cycles decreased to 72.47% and 49.44% of that of the specimen after 45 thermal cycles, respectively. The strength of RO lattices decreased to 91.07% and 82.14% of that of the specimen after 45 thermal cycles, respectively, while the strength of SO lattices decreased to 83.27% and 77.96% of that of the specimen after 45 thermal cycles, respectively. The compressive strengths of the two types of lattices were higher than that of the control group after three cycles, indicating that 3D-printed lattices can effectively mitigate the influence of environmental thermal fatigue on the mechanical properties of cement-based materials. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

To reveal the meso-mechanical essence of deep rock mass failure and capture precursor information, this study focuses on soft rock failure mechanisms. Based on the discontinuous medium discrete element method (DEM), we employed digital image correlation (DIC) technology, acoustic emission (AE) monitoring, and particle flow code (PFC) numerical simulation to investigate the failure evolution characteristics and AE quantitative representation of soft rocks. Key findings include the following: Localized high-strain zones emerge on specimen surfaces before macroscopic crack visualization, with crack tip positions guiding both high-strain zones and crack propagation directions. Strong force chain evolution exhibits high consistency with the macroscopic stress response—as stress increases and damage progresses, force chains concentrate near macroscopic fracture surfaces, aligning with crack propagation directions, while numerous short force chains coalesce into longer chains. The spatial and temporal distribution characteristics of acoustic emissions were explored, and the damage types were quantitatively characterized, with ring-down counts demonstrating four distinct stages: sporadic, gradual increase, stepwise growth, and surge. Shear failures predominantly occurred along macroscopic fracture surfaces. At the same time, there is a phenomenon of acoustic emission silence in front of the stress peak in the surrounding rock of deep soft rock roadway, as a potential precursor indicator for engineering disaster early warning. These findings provide critical theoretical support for deep engineering disaster prediction. Full article

Acoustic emission (AE) technology has been extensively applied in the damage assessment of steel strands; however, it remains inadequate in identifying and quantifying the number of strand fractures, which limits the accuracy and reliability of prestressed structure monitoring. In this study, a test platform based on practical engineering was built. The AE monitoring method using a waveguide rod was applied to identify signals from different numbers of strand fractures, and their acoustic characteristics were analyzed using Fourier transform and multi-bandwidth wavelet transform. The propagation attenuation behavior of the AE signals in the waveguide rod was then analyzed, and the optimal parameters for field monitoring as well as the maximum number of plates suitable for series beam plates were determined. The results show that AE signals decrease exponentially with an increasing propagation distance, and attenuation models for various AE parameters were established. As the number of strand fractures increases, the amplitude of the dominant frequency increases significantly, and the energy distribution shifts towards higher-frequency bands. This finding introduces a novel approach for quantifying fractures in steel strands, enhancing the effectiveness of AE technology in monitoring and laying a foundation for the development of related technologies. Full article

This study investigates the cracking behavior of high-performance calcium oxide-activated concrete incorporating basalt and synthetic macro fibers under compressive and flexural loading. Acoustic emission (AE) monitoring was employed to capture real-time crack initiation and propagation, offering insights into damage evolution mechanisms. A comprehensive series of uniaxial compression and four-point bending tests were conducted on fiber-reinforced and plain specimens. AE parameters, including count, duration, risetime, amplitude, and signal energy, were analyzed to quantify crack intensity and classify fracture modes. The results showed that tensile cracking dominated even under compressive loading due to lateral stresses, while fiber inclusion significantly enhanced toughness by promoting distributed microcracking and reducing abrupt energy release. Basalt fibers were particularly effective under flexural loading, increasing the post-peak load-bearing capacity, whereas synthetic macro fibers excelled in minimizing tensile crack occurrence under compression. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

This study conducted uniaxial creep tests on coal samples under both natural and water-saturated conditions for durations of about 180 days per sample to study the stability of coal pillar dams of the Daliuta Coal Mine underground reservoir. Combined with synchronized acoustic emission (AE) monitoring, the research systematically revealed the time-dependent deformation mechanisms and damage evolution laws of coal under prolonged water immersion and natural conditions. The results indicate that water-immersed coal exhibits a unique negative creep phenomenon at the initial stage, with the strain rate down to −0.00086%/d, attributed to non-uniform pore compaction and elastic rebound effects. During the steady-state creep phase, the creep rates under water-immersed and natural conditions were comparable. However, water immersion led to an 11.4% attenuation in elastic modulus, decreasing from 2300 MPa to 2037 MPa. Water immersion would also suppress AE activity, leading to the average daily AE events of 128, which is only 25% of that under natural conditions. In the accelerating creep stage, the AE event rate surged abruptly, validating its potential as an early warning indicator for coal pillar instability. Based on the identified long-term strength of the coal sample, it is recommended to maintain operational loads below the threshold of 9 MPa. This research provides crucial theoretical foundations and experimental data for optimizing the design and safety monitoring of coal pillar dams in CMURs. Full article

This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that resonant VS150-RIC sensors outperform broadband sensors in defect detection, showing greater energy response at characteristic frequencies for inner race defects. The RMS parameter emerges as a robust diagnostic indicator, with defective bearings exhibiting periodic peaks and higher mean RMS values. Field tests reveal progressive RMS escalation preceding visible damage, enabling predictive maintenance. Furthermore, we develop a novel Paligemma LLM model for automated wear detection using AE time-domain images. The research validates the AE technology’s superiority over conventional vibration methods for low-speed bearing monitoring, providing a scientifically grounded approach for safety-critical ropeway maintenance. Full article

With the extensive application of 3D-printed composites across multiple industries, the investigation into their structural reliability under complex loading conditions has become a critical research focus. This study comprehensively employs acoustic emission (AE) monitoring, digital image correlation (DIC) measurement, and micro-computed tomography (Micro-CT) visualization techniques to explore the progressive damage behavior of 3D-printed sandwich-structured composites reinforced with continuous carbon fiber sheets under three-point bending. Mechanical tests show that increasing the fiber content of face sheets from 10% to 20% enhances average bending strength by 56%, while low fiber content compromises stiffness and load-bearing capacity. AE analysis categorizes damage modes into matrix cracking (<50 kHz), debonding/delamination (50–150 kHz), and fiber breakage (>150 kHz) using k-means clustering algorithms. DIC measurement reveals significant structural deformation processes during damage progression. The AE-DIC-Micro-CT combination demonstrates an initial undamaged state, followed by damage initiation and propagation in the subsequent stages. This integrated approach provides an effective method for damage assessment, guiding the design and reliability improvement of 3D-printed composites. Full article

Ultra-high-performance concrete (UHPC) reinforced with recycled tire steel fibers (RTSFs) was studied to evaluate its mechanical properties and cracking behavior. Using acoustic emission (AE) monitoring, researchers tested various RTSF replacement rates in compression and flexural tests. Results revealed a clear trend: mechanical properties initially improved then declined with increasing RTSF content, peaking at 25% replacement. AE analysis showed distinct patterns in energy release and crack propagation. Signal timing for energy and ringing count followed a delayed-to-advanced sequence, while b-value and information entropy changes indicated optimal flexural performance at specific replacement rates. RA-AF classification demonstrated that shear failure reached its minimum (25% replacement), with shear cracks increasing at higher ratios. These findings demonstrate RTSFs’ dual benefits: enhancing UHPC performance while promoting sustainability. The 25% replacement ratio emerged as the optimal balance, improving strength while delaying crack formation. This study provides insights into the mechanism by which waste tire steel fibers enhance the performance of UHPC. This research provides valuable insights for developing eco-friendly UHPC formulations using recycled materials, offering both environmental and economic advantages for construction applications. Full article

It is vital to stabilize pillar dams in underground reservoirs in coal mine goafs to protect groundwater resources and quarry safety, practice green mining, and protect the ecological environment. Considering the actual occurrence of coal pillar dams in underground reservoirs, acoustic emission (AE) mechanical tests were performed on dry, naturally absorbed, and soaked coal samples. According to the mechanical analysis, Quantitative analysis revealed that dry samples exhibited the highest mechanical parameters (peak strength: 12.3 ± 0.8 MPa; elastic modulus: 1.45 ± 0.12 GPa), followed by natural absorption (peak strength: 9.7 ± 0.6 MPa; elastic modulus: 1.02 ± 0.09 GPa), and soaked absorption showed the lowest values (peak strength: 7.2 ± 0.5 MPa; elastic modulus: 0.78 ± 0.07 GPa). The rate of mechanical deterioration increased by ~25% per 1% increase in moisture content. It was identified that the internal crack development presented a macrofracture surface initiating at the sample center and expanding radially outward, and gradually expanding to the edges by adopting AE seismic source localization and the K-means clustering algorithm. Soaked absorption was easier to produce shear cracks than natural absorption, and a higher water content increased the likelihood. The b-value of the AE damage evaluation index based on crack development was negatively correlated with the rock damage state, and the S-value was positively correlated, and both effectively characterized it. The research results can offer reference and guidance for the support design, monitoring, and warning of coal pillar dams in underground reservoirs. (The samples were tested under two moisture conditions: (1) ‘Soaked absorption’—samples fully saturated by immersion in water for 24 h, and (2) ‘Natural absorption’—samples equilibrated at 50% relative humidity and 25 °C for 7 days). Full article

This study analyzed the effects of curing and maturation on the formation of shrinkage strain and destructive processes in concrete. Experimental tests were performed on commonly used concrete, class C30/37, with basalt aggregate and blast furnace cement tested: at constant temperature after water curing, at constant temperature without water curing, and under cyclically changing temperature without prior curing. Shrinkage strain was measured for 46 days with an extensometer on 150 × 150 × 600 mm specimens, and the acoustic emission (AE) method was used to monitor microcracks and processes in concrete in real time. The results were compared with the model according to EN 1992-1-1:2023. It was found that this model correctly estimates shrinkage strain for wet-curing concrete, but there are discrepancies for air-dried concrete, regardless of temperature and moisture conditions (constant/variable). Correlation coefficients between shrinkage strain increments and process increments in early-age concrete are proposed. Correlations between shrinkage strain and destructive processes occurring in concrete were confirmed. It was found that by using correlation coefficients, it is possible to estimate internal damage in relation to shrinkage strain. The results indicate the need to develop guidelines for estimating shrinkage strain in non-model environmental conditions and demonstrate the usefulness of the nondestructive AE method in diagnosing early damage, especially in concrete structures exposed to adverse service conditions. Full article