Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues in steel pipes, the demand for glass fiber-reinforced plastic (GFRP) pipes is increasing in oil and gas gathering and transmission systems. However, the medium that is transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-, Ca2+, Mg2+, SO42-, CO32-, and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped defect GFRP pipes with depths of 2 mm and 5 mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.

To investigate the extent of damage and seepage characteristics of water-saturated coal samples after subjecting them to microwave cycling. The microwave equipment was used to process the coal samples by microwave cycling. The non-contact digital image processing technology and acoustic emission system were used to carry out the triaxial loading experimental study of the coal samples to obtain the mechanical parameter characteristics, energy evolution pattern, acoustic emission information and permeability characteristics of coal samples under different microwave cycle times. The results of the study show that: With the increase in the number of microwave cycles, dense grid-loaded cracks gradually appeared on the surface of the coal samples, the triaxial partial stresses of the coal samples decreased, and the strains also decreased, and the modulus of elasticity and Poisson\'s ratio also decreased; In the densification stage stage, the dissipated energy is higher than the elastic energy, and as the elastic stage proceeds, the elastic energy gradually reverses to exceed the dissipated energy, and the total energy and elastic energy of the coal samples decrease with the increase in the number of cycles, and the dissipated energy rises; Coal samples produce a large number of fissures due to the increase in the number of microwave cycles, the more frequent the fissure activity during the loading process, the acoustic emission amplitude and ringing count scattering points all become dense with the increase in the number of cycles, and the data increase; Initial permeability, destructive permeability and average permeability were all increased, microwave treatment has a better effect of permeability enhancement, the permeability of the treated coal samples was changed from low permeability to medium permeability, and the permeability enhancement was the largest in 6 cycles, and the permeability was increased by 7.18 times. This article explores the damage condition of water-saturated coal samples under microwave cycling treatment. Then, it explores the effect of microwave cycling on the permeability enhancement of the coal body, which provides a new method for exploring the gas permeability enhancement and extraction of low-permeability coal samples underground.

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

Unveiling the mechanical properties and damage mechanism of the complex composite structure, comprising backfill and surrounding rock, is crucial for ensuring the safe development of the downward-approach backfill mining method. This work conducts biaxial compression tests on backfill-rock under various loading conditions. The damage process is analyzed using DIC and acoustic emission (AE) techniques, while the distribution of AE events at different loading stages is explored. Additionally, the dominant failure forms of specimens are studied through multifractal analysis. The damage evolution law of backfill-rock combinations is elucidated. The results indicate that DIC and AE provide consistent descriptions of specimen damage, and the damage evolution of backfill-rock composite specimens varies notably under different loading conditions, offering valuable insights for engineering site safety protection.

This study proposes a prediction method for residual compressive strength after impact based on the extreme gradient boosting model, focusing on composite laminates as the studied material system. Acoustic emission tests were conducted under controlled temperature and humidity conditions to collect characteristic parameters, establishing a mapping relationship between these parameters and residual compressive strength under small sample conditions. The model accurately predicted the residual compressive strength of the laminates after impact, with the coefficient of determination and root mean square error for the test set being 0.9910 and 2.9174, respectively. A comparison of the performance of the artificial neural network model and the extreme gradient boosting model shows that, in the case of small data volumes, the extreme gradient boosting model exhibits superior accuracy and robustness compared to the artificial neural network. Furthermore, the sensitivity of acoustic emission characteristic parameters is analyzed using the SHAP method, revealing that indicators such as peak amplitude, ring count, energy, and peak frequency significantly impact the prediction results of residual compressive strength. The machine-learning-based method for assessing the damage tolerance of composite laminates proposed in this paper utilizes the global monitoring advantages of acoustic emission technology to rapidly predict the residual compressive strength after the impact of composite laminates, providing a theoretical approach for online structural health monitoring of composite laminates. This method is applicable to various composite laminate structures under different impact conditions, demonstrating its broad applicability and reliability.

The frequency domain characteristics of acoustic emission can reflect issues such as rock structure and stress conditions that are difficult to analyze in time domain parameters. Studying the influence of immersion time on the mechanical properties and acoustic emission frequency domain characteristics of muddy mineral rocks is of great significance for comprehensively analyzing rock changes under water-rock coupling conditions. In this study, uniaxial compression tests and acoustic emission tests were conducted on sandstones containing montmorillonite under dry, saturated, and different immersion time conditions, with a focus on analyzing the effect of immersion time on the dominant frequency of rock acoustic emission. The results indicated that immersion time had varying degrees of influence on compressive strength, the distribution characteristics of dominant acoustic emission frequencies, the frequency range of dominant frequencies, and precursor information of instability failure for sandstones. After initial saturation, the strength of the rock sample decreased from 53.52 MPa in the dry state to 49.51 MPa, and it stabilized after 30 days of immersion. Both dry and initially saturated rock samples exhibited three dominant frequency bands. After different immersion days, a dominant frequency band appeared between 95 kHz and 110 kHz. After 5 days of immersion, the dominant frequency band near 0 kHz gradually disappeared. After 60 days of immersion, the dominant frequency band between 35 kHz and 40 kHz gradually disappeared, and with increasing immersion time, the dominant frequency of the acoustic emission signals increased. During the loading process of dry rock samples, the dominant frequency of acoustic emission signals was mainly concentrated between 0 kHz and 310 kHz, while after saturation, the dominant frequencies were all below 180 kHz. The most significant feature before the rupture of dry rock samples was the frequent occurrence of high frequencies and sudden changes in dominant frequencies. Before rupture, the characteristics of precursor events for initially saturated and immersed samples for 5, 10, and 30 days were the appearance and rapid increase in sudden changes in dominant frequencies, as well as an enlargement of the frequency range of dominant frequencies. After 60 days of immersion, the precursor characteristics of rock sample rupture gradually disappeared, and sudden changes in dominant frequencies frequently occurred at various stages of sample loading, making it difficult to accurately predict the rupture of specimens based on these sudden changes.

The compaction characteristics and bearing capacity of dry filling materials in goaf have a significant influence on stope control and surface stability. Through acoustic emission monitoring and mechanical model analysis, a series of confined compression tests were conducted on crushed waste with varying particle sizes and Talbot coefficients. The deformation, fragmentation, and acoustic emission characteristics under corresponding working conditions were determined. The results indicate that the stress-strain curves of crushed stone with different particle sizes and Talbot coefficients exhibit similar nonlinear behavior during confined compression. However, the strain response varies with changing stress levels. By analyzing the slope change rate of the stress-strain curve, the lateral uniaxial compression process of waste rock can be divided into three deformation stages: rapid compression, stable crushing, and slow compaction. The compressive deformation characteristics of gravel differ based on particle size and Talbot coefficient. Specimens with a higher Talbot coefficient demonstrate stronger compressive resistance and weaker deformation resistance during initial compaction loading. Notably, the internal pressure structure strength is influenced by factors such as maximum particle size D, grading coefficient n, and particle size distribution continuity, rather than solely by the proportion of large particles. The evolution of acoustic emission signals and energy-time curve during waste rock confined axial compression synchronizes with the compaction process. Overall, compaction plays a critical role in maintaining the stability of goaf in dry crushed waste filling.

Acoustic emission (AE) technology plays a crucial role in dynamic nondestructive testing. To investigate material properties, friction characteristics, damage features, and acoustic source localization, AE tests are commonly conducted on metal or composite plates. However, the reflection of AE waves at the boundary often generates strong interference, significantly impacting AE test results. In response to this challenge, this paper introduces an innovative solution: an additional Spiral Acoustic Black Hole (ASABH) affixed to the test plate\'s boundary. The ASABH is designed to mitigate the reflection of AE signals, enhancing the signal-to-noise ratio collected by the sensor. The study begins by establishing a finite element model of the ASABH to validate its efficacy in reducing boundary reflection waves. Subsequently, the paper explores the impact of structural geometric parameters-such as length, residual thickness, power exponent, pitch, and extended length-on the reduction effect. The investigation also delves into the variation of attenuation degree when connecting the ASABH to plates with different relative thickness, relative widths, and materials. Finally, the effectiveness of the ASABH in attenuating structural boundary reflection waves is verified through pencil-lead breaking tests conducted on both metal and composite plates. Results indicate that the proposed ASABH effectively mitigates the reflection of AE waves at the structural boundary, demonstrating adaptability and providing valuable insights for ASABH design.

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed \"acoustic-thermal-stress\" fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.