Search Results (978)

Relatively low-cost titanium carbide (TiC) materials and metal matrix composites (MMC) are proposed for waste-to-hydrogen conversion. Two types of steels are used as bases prepared from EN 10088 flat products, namely X2CrTi12 (1.4512, AISI 409) and X5CrNi18-10 (1.4301, AISI 304). TiC is mixed with TRIBALOY^® T-800 alloy in powder form and applied via laser-directed energy deposition (DED-LB) over the substrates. For the powder mixture, Fourier transform infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC) are performed. The raw materials are investigated for the processes that occur in them under heating. After the solidification of the molten mixture, grinding and polishing are performed to achieve a thin layer. The studies of the obtained MMC include interface zone assessment, hardness and Young’s modulus distribution, microstructural analysis, and visual defect evaluation. Advanced sensors for acoustic emission (AE) and Electrical Contact Resistance (ECR) provided characterization together with micro-scratch testing. The use of photoluminescence spectroscopy is proposed for the new composite materials. The electron transfer pathway can be studied with time-resolved spectroscopy. Renewable energy production by breaking down waste into hydrogen-rich syngas can be achieved through pyrolysis, followed by steam reforming and purification. The obtained novel materials show promising application solutions with increased durability, corrosion, and wear resistance. Full article

During operation, several parts of the thermal reactor burners sustain heavy damage and need to be replaced. Different solutions for parts manufacturing are investigated: thermal spraying, Selective Laser Melting (SLM), and hardfacing by Directed Energy Deposition plasma arc (DED-arc). Based on the comparison to original material and the duration of usage, application of those three methods for replacement is studied in order to determine the most suitable one, with Additive Manufacturing (AM) being proposed for targeting the problem. Thermal-sprayed items have a zirconium-oxide-based outer layer. SLM produces a monolithic item, while with the help of DED-arc, a composite structure with a sound metallurgical bond between the base and the added material is produced. The microstructures with the interface zones are observed. Samples are machined and ground, and their friction characteristics are taken with the help of acoustic emission (AE) and Electrical Contact Resistances (ECR) sensors during scratching. As a result, overlaying of the base stainless steel by DED-arc is proposed due to the better metallurgical stability of the added mixture in a hot environment above 800 °C and its hardness characteristics. Full article

The larvae of wood-boring insects often cause damage to wooden buildings, furniture, and objects of cultural heritage. A fundamental practical challenge in assessment and conservation is reliably distinguishing between active and inactive infestations—a distinction that conventional visual inspection methods cannot always resolve with confidence. We present a selection of experiments for the implementation of acoustic emission (AE) measurement methods for these inspections, spanning from highly controlled laboratory settings to practical field tests. An overview explains the physical principles of structure-borne sound emission by feeding larvae and the AE sensors and other instrumentation used (AMSY-6 stationary system and the single-channel Insect Activity Detection System, IADS). Laboratory experiments with standardized small specimens containing single larvae of Hylotrupes bajulus are described in detail, examining the influence of temperature, sensor coupling method, and sensor-to-larva distance on detection reliability. Verification experiments on large structural timber with unknown infestation levels demonstrated the practical applicability of the method and enabled qualitative localization of larval activity. Field applications in historic buildings, churches, and cultural heritage objects are presented and discussed. The results confirm that AE sensors can reliably detect larval activity of H. bajulus in standardized specimens at signal-to-noise ratios clearly distinguishable from negative controls. Sensor sensitivity decreases significantly with distance, with reliable detection up to approximately 30 cm under the tested conditions. Although interpretive expertise in wood biology, infestation assessment, and conservation expertise remains essential, the results indicate that AE sensors can indeed be used to detect larval activity inside wood in a non-destructive way in a broad range of field and laboratory applications and across a range of conditions. Full article

Conventional push-out tests detect bone–implant failure only at the point of macroscopic instability, leaving earlier damage stages unresolved. Here we present a proof-of-concept for a push-out test stand combined with acoustic emission (AE) monitoring, aimed at capturing crack initiation before the macroscopic load drop. To provide a controlled failure process, samples were fabricated from SLA resin with defined breaking points, serving as mechanical surrogates rather than biological models. Four sample types with varying strut number and thickness were tested while recording AE, and post-processing was applied to remove friction and noise signals. A four-stage fracture model—initial, pre-fracture, fracture, and post-fracture—was defined, with the pre-fracture stage showing AE activity prior to any macroscopic load response. Increasing strut thickness and contact area raised maximum load resistance and AE activity, and Principal Component Analysis confirmed a progressive, intensity-driven separation of stages. The results demonstrate that AE monitoring resolves a pre-fracture regime inaccessible to conventional load measurement, establishing a methodological basis for future application to bone–implant samples. Full article

This study developed an eco-friendly mine backfill material using coal gangue, fly ash, desert sand, and natural rice husk. The mechanical properties and damage evolution characteristics were investigated through uniaxial compression, Brazilian splitting, acoustic emission (AE), digital image correlation (DIC), and scanning electron microscopy (SEM) tests. Polyvinyl alcohol (PVA) fibers provided the highest reinforcement efficiency, followed by polypropylene (PP) fibers. At a natural rice husk dosage of 5%, the backfill reached a compressive strength of 5.6 MPa and a splitting tensile strength of 0.376 MPa, representing an 83.4% increase in tensile strength compared with the fiber-free group and approaching the performance of the PP fiber group. Increasing the cement replacement ratio from 50% to 70% reduced strength, whereas longer curing improved strength development. The coupled AE–DIC observations indicate that natural rice husk incorporation delayed crack localization and changed the failure process from sudden brittle cracking to more progressive damage. SEM observations show that natural rice husk formed local contact and partial embedding with the cementitious matrix, with hydration products around the rice husk and visible pull-out voids. Overall, natural rice husk can be used as a low-cost and sustainable reinforcing material for coal gangue–desert sand–fly ash-based mine backfill. Full article

Cemented coal gangue backfill (CCGB) is an important material for the resource utilization of mining solid waste, and its long-term stability is strongly affected by aggregate gradation. In this study, aggregate mass fractal dimension was used to characterize the particle size distribution of coal gangue, and four gradation schemes with different fractal dimensions were designed. Uniaxial compressive strength (UCS) tests, stepwise accelerated creep tests, acoustic emission (AE) monitoring, and scanning electron microscopy (SEM) observations were conducted to investigate strength, creep behavior, crack evolution, and damage mechanisms. The results show that P-wave velocity and UCS exhibit generally non-monotonic variations with fractal dimension, with relatively high values in the intermediate fractal-dimension range. The empirical long-term strengths for $D=2.20$, 2.41, 2.59, and 2.79 are 6.62, 8.83, 7.34, and 7.07 MPa, respectively. AE results indicate that shear cracking first decreases and then increases with fractal dimension, reaching the lowest proportion of 41.8% at $D=2.41$. SEM observations show that an intermediate fractal dimension improves skeleton continuity and interfacial integrity, thereby suppressing shear-related damage and delaying creep instability. These findings demonstrate that aggregate mass fractal dimension is an effective structural parameter for linking gradation characteristics, creep resistance, and damage evolution of CCGB. Full article

Fiber-reinforced self-compacting concrete (FR-SCC) incorporating granite powder (GP), an expansive agent (EA), steel fibers (SFs), and polypropylene fibers (PPFs) was investigated for potential pre-cast tunnel-segment applications. Sixteen mixtures, covering GP replacement ratios of 0–18%, EA dosages of 0–8% by binder mass, and SF and PPF volume fractions of 0–0.75% and 0–0.15%, were tested in uniaxial compression on 100 mm × 100 mm × 300 mm prisms with acoustic emission (AE) monitoring. Within the tested range, 12% GP and 8% EA gave the most favorable binder composition. XRD and SEM analyses indicated that GP acted predominantly as an inert filler with no detectable portlandite consumption, while the expansive agent was associated with additional ettringite formation. At this composition, hybrid SF/PPFs increased the post-peak energy by a factor of 7.66 relative to the fiber-free mixture, mainly improving the post-peak rather than the pre-peak behavior. Among the Carreira–Chu, GB 50010, and modified Weibull formulations, the GB 50010 piecewise model best reproduced the full stress–strain curves and was used as the primary constitutive model. Two-variable regressions were established to separate the apparent effects of the SF and PPF volume fractions on the ascending- and descending-branch shape parameters, and a ductility-calibrated expression was developed for the descending-branch parameter. The Pearson coefficient between the descending-branch parameter and the AE characteristic strain was −0.904, while that between the AE characteristic strain and the macroscopic residual strain was +0.983. These results link constitutive modeling, AE damage evolution, and macroscopic post-peak ductility for FR-SCC within the tested range of mix proportions. Full article

Tectonic coal, highly fragmented by geological stresses, cannot be sampled as intact specimens, making remolded (briquette) samples essential for mechanical testing. However, remolding conditions, particle grading, molding pressure, and moisture content, are typically selected empirically, lacking objective evaluation criteria. This study proposes an acoustic emission (AE)-based multi-parameter framework to optimize these conditions. Using an orthogonal design (three factors at three levels), we prepared remolded tectonic coal samples with varying gradings (1:1:1, 1:4:1, 1:8:1), pressures (15 MPa, 20 MPa, 25 MPa), and moisture contents (8%, 10%, 12%). Uniaxial compression tests were conducted, and five evaluation parameters were extracted: compressive strength, cumulative AE count, cumulative AE energy, energy conversion ratio k, and shear crack proportion r. Results show that AE parameters effectively reflect remolding quality in terms of signal activity, damage mode, and energy efficiency. Grading and pressure dominate cumulative AE metrics, while moisture content and pressure control k and r. Increasing medium-particle proportion or pressure enhances AE activity, k, and r; increasing moisture content suppresses them. The optimal remolding conditions for the studied coal are 1:8:1 grading, 25 MPa pressure, and 8% moisture content, with different condition priority sequences depending on optimization goals. This AE-based approach provides an objective, quantitative tool for tectonic coal remolding optimization, benefiting subsequent mechanical and permeability studies. Full article

Valve leakage in marine oil and gas pipelines is a critical failure mode that threatens operational safety, ecological integrity and production economic benefits, creating an urgent demand for accurate, real-time and robust fault diagnosis systems. Acoustic Emission (AE) technology captures transient acoustic signatures generated by leakage to enable non-intrusive online monitoring, while deep learning supports intelligent analysis through automatic signal feature extraction. Nevertheless, traditional AE-based leakage diagnosis methods rely heavily on manual feature engineering and fixed signal processing rules. Existing AE-driven deep learning methods fail to simultaneously deliver high detection accuracy, low inference latency and strong noise immunity, hindering their practical deployment on offshore platforms. To address these limitations, this paper proposes a Parameter-free Star-shaped Attention Fusion Network (SAFNet) for lightweight valve leakage localization using AE signals. Centered on the Temporal Pyramid Encoder (TPE) and Progressive Lightweight Star-shaped Attention (PLSA) module, SAFNet integrates Dual Bilinear Star Mapping (DBSM), Energy-Driven Feature Refiner (EDFR) and Multi-Scale Gated Attention Fusion (MS-GAF) modules. This architecture achieves efficient multi-scale temporal feature extraction, parameter-free nonlinear enhancement, noise-resistant refined feature processing and adaptive hierarchical feature fusion. The proposed method is applicable to valve leakage diagnosis of marine oil and gas pipelines under variable pressure and complex marine noise conditions. Comprehensive experiments are conducted on a dataset constructed by combining laboratory controlled leakage signals with real marine background noise recorded from the Liwan 3-1 offshore platform. The experimental results reveal that SAFNet balances high detection accuracy, compact model size and low inference latency simultaneously. Specifically, the network maintains a stable detection accuracy above 95% under pipeline pressures ranging from 2 MPa to 5 MPa, and exhibits excellent stability under extreme heavy noise environments. Ablation experiments further validate the synergistic performance gain brought by all core modules. The presented network delivers an efficient lightweight solution for valve leakage localization under simulated marine acoustic conditions, promotes the development of intelligent monitoring technologies for marine pipeline systems, and comprehensively improves offshore operational safety and marine ecological protection capacity. Full article

This study investigates the acoustic emission (AE) characteristics associated with the shear failure mode based on the principles of active waveguide monitoring for the bedding rock slopes. Physical simulation experiments were conducted to assess the AE response during the shear-induced failure process of active waveguide structures. The findings indicate that during the initial loading phase, the scatter points of the signals are concentrated within a relatively narrow range. As the shear stress exceeds 90% of the peak stress and approaches the failure stage, there is a significant increase in the AE count and a rise in the high-frequency signals. Additionally, the distribution range of signals in the parameter correlation plot expands progressively. With increasing shear stress, the AE count, amplitude, and energy also rise gradually. And the emergence of continuous high-frequency signals is noted. During the failure stage, numerous microcracks initiate and propagate within the specimen, with signal amplitudes ranging between 40 and 90 dB. The peak frequency range of the AE signals broadens, with high-frequency components mainly concentrated between 350 and 450 kHz. Loading tests conducted at shear displacement rates of 0.25–1.5 mm/min reveal a strong correlation between the AE count and the shear displacement rate. Furthermore, prior to the shear failure of the active waveguide structures, the AE count shows a positive correlation with shear displacement. After the shear failure of the waveguide structure specimens, the AE count gradually decreases from a higher level to a lower level, demonstrating a negative correlation with shear displacement. The active waveguide structure can monitor the internal deformation conditions of the bedding rock slope so as to provide some reference for the early warning research. In addition, quantitative statistical analysis and curve fitting are conducted on the relationship between AE statistical count and shear displacement under different loading rates. The measured data show good agreement with the fitted curves, and a distinct two-stage evolutionary pattern (positive correlation before peak and negative correlation after peak) is quantitatively identified. These results further enhance the reliability of using AE parameters for quantitative evaluation of shear failure characteristics and displacement rate effects in bedding rock slopes. Full article

Sandstone–marble waste powder concrete composite structures serve as common load-bearing systems in tunnels, underground caverns, and similar engineering projects, where the interface roughness characteristics directly govern their overall stability and service safety. To investigate the influence of interface roughness on the failure behavior of the composite, four groups of sandstone–concrete composite specimens made with marble waste powder concrete were prefabricated with different joint roughness coefficients (JRC = 0, 7.84, 17.99, 20.79). The concrete matrix was prepared with marble waste powder incorporated at 25 wt% of the total binder, corresponding to 20.45 wt% of the total mixture, and the water-to-binder ratio was 0.20. Uniaxial compression tests were conducted with synchronous acoustic emission (AE) and digital image correlation (DIC) monitoring to examine the roughness-dependent mechanical response, energy evolution, damage activity, and strain localization of the composites. The results show that the peak stress and elastic modulus of the composite increase continuously with increasing JRC. When JRC increases from 0 to 20.79, the peak stress increases by 170.3% and the elastic modulus increases by 201.1%. The energy evolution mechanism transitions from progressive damage with gradual energy dissipation at low roughness to a three-stage mode at high roughness, characterized by initial frictional energy dissipation, intermediate energy storage, and rapid elastic energy release and dissipated energy increase near failure. DIC results further reveal that increasing interface roughness suppresses interfacial shear slip and promotes tensile-dominated strain localization, whereas excessive roughness may induce local stress concentration around asperities and increase the tendency toward abrupt post-peak instability, the failure mode changes from mixed tensile–shear failure with obvious interfacial slip to tensile-dominated failure. Full article

Oil shale often exhibits well-developed internal bedding planes, microcracks and organic-rich weak interfaces, while a mixed failure mode of tensile fracture and shear slip along weak bedding planes can be observed under bending loads. In this study, three-point bending tests were performed on the oil shale, and with the combination of acoustic emission (AE) monitoring to analyze the crack propagation paths, the crack path selection and mixed-mode I-II fracture behavior controlled by bedding were symmetrically investigated. The experimental results demonstrate that crack propagation does not always steadily proceed along the initial direction of pre-existing crack, and instead, the occurrence of pronounced deflection can be observed near the weak bedding planes, indicating a trend of transition from tensile crack to shear slip along bedding, while the obvious mixed-mode I-II fracture characteristics are presented. Meanwhile, this process is also accompanied by the enhanced AE activity and the occurrence of a localized high-energy event. Furthermore, based on theoretical fracture mechanics analysis, it is interpreted that the localized driving force conditions at the crack tip can be altered by the mechanical differences between the bedding weak planes and the matrix, which provides a theoretical explanation for why the crack deflection along the structural weak planes is promoted. These research findings correlate the crack propagation path evolution, AE response and mixed-mode fracture characteristics, which can provide the experimental evidence for understanding the controlling role of crack path selection in brittle shale under bending conditions. Full article

Lithium-ion batteries (LIBs) are widely used; however, they have safety hazards because of their susceptibility to thermal runaway (TR). Current early warning methods rely on the external monitoring of parameters such as temperature and strain. These methods have an inherent lag, as the signals can only be detected after internal heat and gas accumulation. Internal sensors are difficult to implement due to the harsh environment and high cost, leaving the ultra-early incubation stage of TR poorly addressed. To overcome these limitations, this study introduces acoustic emission (AE) technology for the real-time external detection of internal TR reactions. An experimental platform induced TR through overcharging, integrating multi-source AE and temperature signal acquisition. Continuous AE signals were collected from the onset of overcharging until the valve opened. Time–frequency analysis revealed anomalous waveform features in the early stage of TR; a two-dimensional method enhanced frequency-domain recognition. Combining the processed AE signals with a convolutional neural network achieved high-accuracy phase segmentation. Cross-validation and comparisons with temperature-based methods demonstrate the effectiveness and precision of AE monitoring for ultra-early TR warning. The results highlight the potential of AE-based monitoring as a proactive risk-management strategy, supporting dynamic assessment and safety responses in energy-storage applications. Full article

This study investigated the mechanical response and instability precursors of highly porous coral blocks from the South China Sea under complex stress paths through conventional triaxial compression tests, two types of triaxial unloading tests, and synchronous acoustic emission (AE) monitoring. The effects of confining pressure, unloading path, and unloading stage on strength, deformation, dilatancy, and failure behavior were examined. The coefficients of variation of dry density, saturated density, porosity, and P-wave velocity were 5.07%, 3.56%, 3.72%, and 5.77%, respectively, indicating relatively limited variability in the measured physical properties, although the influence of specimen heterogeneity cannot be fully excluded. Within the 0–2 MPa confining-pressure range, peak strength increased from 8.81 to 16.85 MPa, whereas axial strain at peak strength changed from 0.33% at 0 MPa to 0.63% at 1 MPa and then decreased to 0.40% at 2 MPa, indicating strong strength sensitivity but a nonmonotonic deformation response. During unloading, all specimens exhibited a transition from compaction to dilatancy. At unloading rates of 0.2 and 0.5 MPa/min, the absolute value of the volumetric strain evolution slope was higher under the increasing-axial-pressure unloading path than under the constant-axial-pressure unloading path, indicating that the path-related difference in dilatancy appears more pronounced under the present test conditions. AE activity increased progressively near peak stress during conventional compression, whereas unloading-induced AE events concentrated near macroscopic failure. Lateral strain anomalies generally preceded AE bursts, suggesting that lateral deformation appears to provide a more sensitive early-warning indicator under the present test conditions. Full article

Acoustic emission (AE) signals associated with different damage processes in reinforced concrete (RC) beams often show overlapping feature distributions, making unsupervised identification difficult. In this study, three RC beams were tested under loading-induced damage, freeze–thaw damage and reinforcement corrosion conditions, and 490 valid AE samples were obtained. Seven AE parameters were selected to construct the clustering feature set. To enhance the separation of different damage-related AE signals, a kernel K-means clustering framework was adopted, and the coyote optimization algorithm was used to optimize the kernel function type and key parameters based on the Gap Statistic. Comparative analysis with K-means, FCM and GMM was also conducted. The results show that the COA-optimized kernel K-means method achieved the best overall clustering performance, increasing the mean ACC by 10.35 percentage points compared with K-means and by 2.49 percentage points compared with GMM. Its mean ARI was also higher than that of GMM by 0.0330, while the standard deviation of ACC decreased from 7.73% to 4.19%. Class-level results indicated that loading-induced AE signals were more readily identified, whereas freeze–thaw and corrosion signals were more affected by feature overlap. Feature interpretation further showed that the main misclassified samples were located in transitional feature regions. The results suggest that COA-based kernel optimization can improve the clustering separation and stability of AE signal identification for different damage types in RC beams. Full article