Search Results (69)

In response to the high cost and environmental impact of backfill materials in Xinjiang mines, an eco-friendly, large-volume composite was developed by bonding desert-sand mortar to waste concrete. A rock-filled concrete process produced a highly flowable mortar from desert sand, cement, and fly ash. Waste concrete blocks served as coarse aggregate. Specimens were cured for 28 days, then subjected to uniaxial compression tests on a mining rock-mechanics system using water-to-binder ratios of 0.30, 0.35, and 0.40 and aggregate sizes of 30–40 mm, 40–50 mm, and 50–60 mm. Mechanical performance—failure modes, stress–strain response, and related properties—was systematically evaluated. Crack propagation was tracked via digital image correlation (DIC) and acoustic emission (AE) techniques. Failure patterns indicated that the pure-mortar specimens exhibited classic brittle fractures with through-going cracks. Aggregate-containing specimens showed mixed-mode failure, with cracks flowing around aggregates and secondary branches forming non-through-going damage networks. Optimization identified a 0.30 water-to-binder ratio (Groups 3 and 6) as optimal, yielding an average strength of 25 MPa. Among the aggregate sizes, 40–50 mm (Group 7) performed best, with 22.58 MPa. The AE data revealed a three-stage evolution—linear-elastic, nonlinear crack growth, and critical failure—with signal density positively correlating to fracture energy. DIC maps showed unidirectional energy release in pure-mortar specimens, whereas aggregate-containing specimens displayed chaotic energy patterns. This confirms that aggregates alter stress fields at crack tips and redirect energy-dissipation paths, shifting failure from single-crack propagation to a multi-scale damage network. These results provide a theoretical basis and technical support for the resource-efficient use of mining waste and advance green backfill technology, thereby contributing to the sustainable development of mining operations. Full article

In this study, acoustic emission (AE) analysis was carried out to evaluate and quantify the cracking behavior of large-scale multi-layered reinforced concrete beams under flexural tests. Four normal concrete beams were repaired by adding a layer of crumb rubberized engineered cementitious composites (CRECCs) or powder rubberized engineered cementitious composites (PRECCs), in either the tension or compression zone of the beam. Additional three unrepaired control beams, fully cast with either normal concrete, CRECCs, or PRECCs, were tested for comparison. Flexural tests were performed on all the tested beams in conjunction with AE monitoring until failure. AE raw data obtained from the flexural testing was filtered and then analyzed to detect and assess the cracking behavior of all the tested beams. A variety of AE parameters, including number of hits and cumulative signal strength, were utilized to study the crack propagation throughout the testing. Furthermore, b-value and intensity analyses were implemented and yielded additional parameters called b-value, historic index [H (t)], and severity (S_r)_. The analysis of the changes in the AE parameters allowed the identification of the first crack in all tested beams. Moreover, varying the rubber particle size (crumb rubber or powder rubber), repair layer location, or AE sensor location showed a significant impact on the number of hits and signal amplitude. Finally, by using the results of the study, it was possible to develop a damage quantification chart that can identify different damage stages (first crack and ultimate load) related to the intensity analysis parameters (H (t) and S_r). Full article

During deep mining engineering, coal bodies are subjected to complex geological stresses such as periodic roof pressure and blasting impacts, which may induce mechanical property deterioration and trigger severe rock burst accidents. This study systematically investigated the mechanical characteristics and failure mechanisms of coal under strain rates on two orders of magnitude through quasi-static cyclic loading–unloading experiments and split Hopkinson pressure bar (SHPB) tests, combined with acoustic emission (AE) localization and crack characteristic stress analysis. The research focused on the differential mechanical responses of coal-rock masses under distinct stress environments in deep mining. The results demonstrated that under quasi-static loading, the stress–strain curve exhibited four characteristic stages: compaction (I), linear elasticity (II), nonlinear crack propagation (III), and post-peak softening (IV). The peak strain displayed linear growth with increasing cycle, accompanied by a failure mode characterized by oblique shear failure that induced a transition from gradual to abrupt increases in the AE counts. In contrast, under the dynamic loading conditions, there was a bifurcated post-peak phase consisting of two unloading stages due to elastic rebound effects, with nonlinear growth of the peak strain and an interlaced failure pattern combining lateral tensile cracks and axial compressive fractures. The two loading conditions exhibited similar evolutionary trends in crack damage stress, though a slight reduction in stress occurred during the final dynamic loading phase due to accumulated damage. Notably, the crack closure stress under quasi-static loading followed a decrease–increase pattern with cycle progression, whereas the dynamic loading conditions presented the inverse increase–decrease tendency. These findings provide theoretical foundations for stability control in underground engineering and prevention of dynamic hazards. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

An accurate understanding of the tensile mechanical behavior of shale rock is essential for optimizing shale gas drilling and hydraulic fracturing operations. However, the mechanical behavior of shale is significantly influenced by its anisotropy. Therefore, this study investigated the tensile mechanical behavior of layered shale by combining acoustic emission (AE) monitoring with two testing methods: the Brazilian splitting test (BST) and a novel direct tensile test (DTT). The impact of anisotropy on the tensile mechanical behavior and failure modes of layered shale under different test methods was evaluated. Additionally, seven anisotropic tensile strength criteria were compared and validated using the experimental results. The results show that: (1) As the loading angle (β) increased, the tensile strength measured by both BST and DTT increased. Both methods exhibited maximum tensile strength at β = 90° and minimum tensile strength at β = 0°. The anisotropy ratios for BST and DTT were 1.52 and 2.36, respectively, indicating the significant influence of the loading angle on tensile strength. (2) The AE results indicated that both DTT and BST specimens exhibited brittle failure characteristics. However, the DTT specimens demonstrated more pronounced progressive failure behavior, with failure modes categorized into four types: tensile failure across the bedding plane, shear failure along the bedding plane, and two types of tensile–shear mixed failure. In contrast, the BST specimens primarily exhibited tensile–shear mixed failure, except for tensile failure along the bedding plane at β = 0° and tensile failure across the bedding plane at β = 90°. (3) Neither of the two test methods could fully eliminate the influence of anisotropy, but three anisotropic tensile criteria, the Lee–Pietruszczak criterion, the critical plane approach criterion, and the anisotropic mode I fracture toughness criterion based on the stress–strain transformation rule demonstrated high accuracy in predicting tensile strength. Furthermore, in alignment with previous studies, the indirect tensile strength of various rock types was found to range between one and three times the direct tensile strength, and a linear correlation between the two variables was established, with a coefficient of approximately 1.11. Full article

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters. Full article

Tilting pad thrust bearings are widely utilized in large rotating machinery such as steam turbines and hydraulic turbines. Defects in their shaft tiles directly impact lubrication characteristics, thereby influencing the overall safety performance of the entire unit. To address this issue, this paper presents a fault diagnosis method for tilting pad thrust bearings using a modified multi-feature fused convolutional neural network (MMFCNN). Initially, an experimental bench for diagnosing faults in tilting pad thrust bearings was developed to collect multi-channel acoustic emission (AE) signals from both normal and faulty pads. Subsequently, the squeeze-and-excitation (SE) module was employed to reallocate the weights of each channel and fuse the features of multi-channel signals. Learning was then conducted on the signal fused with multiple features using the inverse-add module and spanning convolution. Next, a comparative analysis was carried out among the CNN1D, ResNet, and DFCNN models, and the MMFCNN model proposed in this study. The results show that under consistent operating conditions, the MMFCNN model achieves an average fault diagnosis accuracy of 99.58% when utilizing AE signal data from tilting pad thrust bearings in four states as inputs. Furthermore, when different operational conditions are introduced, the MMFCNN model also outperforms other models in terms of accuracy. Full article

Biscuits are characterized by their popular sweet taste, but they have a poor nutritional profile due to their high sugar and saturated fat content, along with low fibre levels. Their sweetness primarily comes from sucrose, which not only determines the flavour but also performs several technological functions, making it difficult to replace in pastry products. Commercial sweeteners and soluble fibres designed for pastry products are available. Therefore, it is necessary to test the feasibility of using these ingredients in biscuit formulations and assess their impact on biscuit quality. Concurrently, the correlation analysis of dough rheological parameters, structure, and instrumental texture parameters with sensory characteristics will help identify which parameters are strongly correlated and can be used to predict biscuit quality. The purpose of this study was to investigate the dough rheological properties, structure, texture, and sensory characteristics of biscuits in which sucrose was replaced by the commercial sweeteners Tagatesse, maltitol, and erythritol–stevia, with the addition of soluble fibres Nutriose^® FB (wheat fibre) and PromOat 35 (oat fibre). At the same time, a correlation analysis was conducted between dough rheological parameters (stickiness, work of adhesion, dough strength) and biscuit quality parameters, such as water activity, water content, colour, texture (pore area, pore shape, pore elongation), and instrumental texture properties (hardness, brittleness, number of acoustic emission (AE) events, AE event energy), with sensory discrimination evaluated through a consumer test. The use of wheat and oat fibres in combination with sucrose resulted in biscuits with lower apparent density, increased porosity, and weaker texture (fracturability, hardness, number of AE events), yet they had better sensory properties compared to biscuits containing sucrose alone. Replacing sucrose with sweeteners combined with fibres led to a deterioration in the sensory quality of the biscuits and a significant change in the dough’s rheological properties. Regardless of the type of sweetener, biscuits with wheat fibre were rated better than those with oat fibre. Of the tested sweeteners, only maltitol combined with wheat fibre resulted in a sensory quality similar to that of sucrose biscuits. Correlation analysis of all measured biscuit quality parameters showed that only the number of AE events had a strong positive correlation with all tested sensory attributes. Porosity was only correlated with sensory crispness, and fracturability was correlated with sweetness, taste, and overall acceptability. Therefore, it appears that the number of AE events recorded at the time of breaking may be a reliable parameter for predicting biscuit quality. Full article

The deterioration of the interlayer interface of a double-block ballastless track is affected by the environmental temperature and moisture conditions, which will have a negative effect on its service life. Composite specimens with interlayer interfaces of double-block ballastless track were fabricated and deteriorated by an accelerated method, i.e., immersed in saturated ammonium chloride solution with various temperatures for different times. Then, the deterioration condition and mechanical properties of the composite specimens were investigated experimentally by a universal material testing machine and acoustic emission technique. The automatic sensor test (AST) method is capable of assessing the deterioration condition of the interlayer interface based on the relative wave velocity. The deterioration depth of the interlayer interface tends to increase with increasing solution temperature and immersion time. Both the solution temperature and immersion time have a negative impact on the splitting tensile strength and direct shear strength. A linear relation is found between the splitting tensile strength (direct shear strength) and the cumulative AE energy released at the fracture moment. The damage factor defined by the cumulative AE energy for most composite specimens is no greater than 0.2 before they are going to be fractured but increases sharply to 1.0 at the fracture moment. Full article

Damage in composite laminates evolves through complex interactions of different failure modes, influenced by load type, environment, and initial damage, such as from transverse impact. This paper investigates damage growth in cross-ply polymeric matrix laminates under tensile load, focusing on three primary failure modes: transverse matrix cracks, delaminations, and fiber breaks in the primary loadbearing 0-degree laminae. Acoustic emission (AE) techniques can monitor and quantify damage in real time, provided the signals from these failure modes can be distinguished. However, directly observing crack growth and related AE signals is challenging, making numerical simulations a useful alternative. AE signals generated by the three failure modes were simulated using modified step impulses of appropriate durations based on incremental crack growth. Linear elastic finite element analysis (FEA) was applied to model the AE signal propagating as Lamb waves. Experimental attenuation data were used to modify the simulated AE waveforms by designing arbitrary magnitude response filters. The propagating waves can be detected as surface displacements or surface strains depending upon the type of sensor employed. This paper presents the signals corresponding to surface strains measured by surface-bonded piezoelectric sensors. Fiber break events showed higher-order Lamb wave modes with frequencies over 2 MHz, while matrix cracks primarily exhibited the fundamental S₀ and A₀ modes with frequencies ranging up to 650 kHz, with delaminations having a dominant A₀ mode and frequency content less than 250 kHz. The amplitude and frequency content of signals from these failure modes are seen to change significantly with source–sensor distance, hence requiring an array of dense sensors to acquire the signals effectively. Furthermore, the reasonable correlation between the simulated waveforms and experimental acoustic emission signals obtained during quasi-static tensile test highlights the effectiveness of FEA in accurately modeling these failure modes in composite materials. Full article

This study aimed to investigate the effect of acidic media storage (gastric acid and Coca-Cola) on the mechanical properties of CAD/CAM materials. Three types of materials were tested: a polymer-infiltrated ceramic network (PICN) (Vita Enamic (En), VITA Zahnfabrik, Germany), a resin composite block (RCB) (Cerasmart (Cs), GC Corp, Japan), and a conventional resin-based composite (Gradia direct (Gr), GC Corp, Japan), which was used as a control. Beam-shaped specimens of each material, with dimensions of 16 mm × 4 mm × 1.5 mm, were prepared (90 in total). The specimens were divided into subgroups (10 each) and stored for 96 h in either gastric acid, Coca-Cola, or distilled water. Flexural strength and elastic modulus were evaluated using a three-point flexural strength test with acoustic emission (AE) monitoring. Vickers microhardness was measured before and after storage in gastric acid and Coca-Cola. Data were statistically analysed using two-way and one-way ANOVA, the Tukey’s post hoc, and independent t-test at a significance level of 0.05. The results showed that Cs and En maintained their flexural strength and elastic modulus after acidic media exposure, while Gr experienced a significant decrease in flexural strength following gastric acid storage (p < 0.01). Initial crack detection was not possible using the AE system, impacting the determination of flexural strength. Exposure to acidic media decreased all materials’ microhardness, with Gr showing the most notable reduction (p < 0.0001). Gastric acid had a greater impact on the microhardness of all tested materials compared to Coca-Cola (p < 0.0001). In conclusion, storage in erosive media did not notably affect the flexural strength or elastic modulus of CAD/CAM composites but it did affect hardness. CAD/CAM composite blocks demonstrated superior mechanical properties compared to the conventional composite. Full article

Different coal failure impact types exhibit different damage statuses and failure modes, resulting in distinct signal characteristics of infrared radiation (IR) and acoustic emission (AE). This paper combines IR and AE monitoring methods to innovatively establish coal damage and failure precursor warning models and obtains the IR and AE precursor characteristics for different coal failure impact types. This research shows that there is a good correspondence between IR and AE timing and spatial distribution of different coal impact types. As the impact tendency increases, the intensity of IR and AE signals increases with coal failure, and the AE positioning points and IR high-temperature areas tend to concentrate. The coal body gradually changes from tensile failure to shear failure. The shear cracks in the failure stage of coal with no, weak, and strong impact are 39.9%, 50.9%, and 53.7%, respectively. The IR and AE instability precursor point of coal with no, weak, and strong impact occurred at 55.2%, 66.3%, and 93.4% of coal failure, respectively. After the IR and AE combined instability precursor point, the dissipated energy and combined damage variable increase rapidly, and the coal body will undergo instability and failure. The research results provide a theoretical basis for comprehensive monitoring of coal body failure and rock burst. Full article

Fire incidents pose significant threats to the structural integrity of reinforced concrete buildings, often necessitating comprehensive rehabilitation to restore safety and functionality. Effective rehabilitation of fire-damaged structures relies heavily on accurate damage assessment, which can be challenging with traditional invasive methods. This paper explores the impact of severe damage due to fire exposure on the mechanical behavior of steel-fiber-reinforced concrete (SFRC) using nondestructive evaluation (NDE) techniques. After being exposed to direct fire, the SFRC specimens are subjected to fracture testing to assess their mechanical properties. NDE techniques, specifically acoustic emission (AE) and ultrasonic pulse velocity (UPV), are employed to assess fire-induced damage. The primary aim of this study is to reveal that AE parameters—such as amplitude, cumulative hits, and energy—are strongly correlated with mechanical properties and damage of SFRC due to fire. Additionally, AE monitoring is employed to assess structural integrity throughout the loading application. The distribution of AE hits and the changes in specific AE parameters throughout the loading can serve as valuable indicators for differentiating between healthy and thermally damaged concrete. Compared to the well-established relationship between UPV and strength in bending and compression, the sensitivity of AE to fracture events shows its potential for in situ application, providing new characterization capabilities for evaluating the post-fire mechanical performance of SFRC. The test results of this study reveal the ability of the examined NDE methods to establish the optimum rehabilitation procedure to restore the capacity of the fire-damaged SFRC structural members. Full article

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. Full article