Peak Frequency Research Articles

Analysis of the effect of nonlocal factors on the vibration of nanobeams

Abstract Currently, the Eluer-Bernoulli beam nonlocal theory does not fully consider the effects of foundation deformation and axial force on the beams, and cannot accurately reflect the real mechanical properties of nanobeams. The primary objective of this study is to introduce a novel computational method designed for an enhanced characterization of the vibrational behavior of nano beams. Initially, this method incorporates the influence of foundation deformation on beam bending, accounts for the effects of axial forces, integrates Eringen's nonlocal theory, and establishes a modified Euler-Bernoulli beam theory model for the first time, accompanied by a degradation validation of the model. Subsequently, the Laplace transform and Hasselman's complex mode synthesis method are utilized to solve the model, providing the first derivation of the state-space transfer function for the nano beam vibration model based on the modified Euler-Bernoulli beam theory that includes axial force effects. Lastly, the study elucidates the impact of nonlocal factors and various parameters on the vibration characteristics of nanobeams. The results show that the order n increases, and the peak frequency value moves in the direction where the nonlocal factor tends to zero. When the order n is less than 6, the frequency peak is mainly concentrated in the interval where the nonlocal factor is greater than 0 and less than 0.1. At the same order, the beam length increases, and the peak frequency moves in the direction of increasing non-local factor. The modified geometric parameters and the foundation beam stiffness parameters have a greater effect on the peak of the beam's vibration mode in the higher order case and a lesser effect in the lower order case. The larger the nonlocal factor, the larger the peak of the vibration mode. The foundation beam stiffness parameter also has an effect on the direction of the peak vibration mode at the 1st and 2nd orders. The research results will help to predict the vibration behavior of nano beams under different conditions more accurately and provide an important reference for related applications.

Damage monitoring of glass/epoxy-curved laminates with different stacking sequences using acoustic emission

Delamination is a major concern in the curvature area of a composite laminate due to the flexural stresses occurring between the laminates. This research focuses on the respective merits of different stacking sequences obtained by layering the glass fiber reinforcement at various orientations in an epoxy resin to manufacture a curved laminate. In particular, laminates with stacking sequences, such as Unidirectional [0°]24, Cross-ply [0°/90°]6S, Angle ply [±45°]6S, and Quasi-isotropic laminates [0°/±45°/90°]3S, were tested by performing four-point bending tests. Acoustic emission (AE) monitoring and experimental analysis characterized damage modes, including delamination. AE parameters, such as amplitude, energy, duration, cumulative count, and peak frequency, were utilized to characterize the damage mechanisms of the various stacking sequences. Experimental AE data confirmed that fiber orientation influences delamination resistance and failure mechanisms. Furthermore, compared to other layup sequences, the peak load of cross-ply (Layup B) was found to be greater by 48.7%, 32.87%, and 50.14%. Cross-ply curved laminates also have higher interlaminar tensile strength and curved beam strength than other sequences. They also failed less often due to delamination than other sequences. Finally, scanning electron microscope characterization verified and validated all damage processes found in the AE data collection. As a result, these findings play a significant role in predicting the lifetime, delamination failure, and strength of curved composite laminates.

Analysis of vibration signals near ground surface during blasting excavation of a tunnel in fractured rock

This study aims to analyze the vibration signals near the ground surface due to the underneath drilling and blasting activities in a fissured rock tunnel. Blasting induced vibration on the ground surface was continuously monitored in a fissured rock tunnel drilling and blasting excavation project in field. Wavelet packet analysis of the vibration signals using Matlab was carried out for signal denoising, differential blasting delay time interval identification, and three-way time-frequency energy analysis. The results show that within a 30 m range from the palm face, the dominant frequency bands of blasting-induced vibrations on the ground surface were concentrated in the range of 0–130 Hz. Two prominent peak frequency bands were identified at 31.25–39.063 Hz (low-frequency band) and 93.75–101.56 Hz (high-frequency band), accounting for 12% of the total energy. Among the three directions of ground surface vibrations, the energy decay was the most significant in the x-direction (tunnel excavation direction), which amounted to 54.29% of the overall energy decay with increasing distance. The energy decay within the 50–80 Hz range was the most pronounced (more than 90%), when the angle between the vibration propagation direction and the fissure or joint direction was 75°. The conclusions provide the insights in the attenuation of blast-induced vibrations in fissured rock and can potentially assist in the design of blasting vibration control.

High-resolution multicomponent LFM parameter estimation based on deep learning

This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, facilitated by convolutional neural networks. Initially, it analyzes the issues of peak shifts and the masking of weaker components due to spectral overlap in the FrFT domain of multi-component LFM signals. Convolutional neural networks are then employed to train and achieve high-resolution representations of FrFT parameters. Specifically, convolutional modules with residual structures are utilized to learn coarse features, while a weighted attention mechanism refines independent features across both channel and spatial dimensions. This approach effectively addresses the challenges posed by spectral peak overlap and frequency shifts in multi-component LFM signals, thereby enhancing the quality of high-resolution parameter estimation. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in processing multi-component LFM signals. Moreover, it exhibits robust detection capabilities for both weak and compact components, thereby underscoring its potential applicability in the field of complex signal processing.

Non-stationary inversion of seismic data for fracture compliances in azimuthally anisotropic mediag

Fractures and tectonic settings cause azimuthal anisotropy in reservoirs. Recognizing the fracture model from the seismic data is a useful tool for identifying the productive zone in the reservoirs. We applied azimuthal velocity analysis in seismic processing to improve image quality and to estimate anisotropic model parameters. Using azimuthal residual moveout analysis, we predicted the direction of azimuthal anisotropy in the reservoir, and found that the results are consistent with fracture orientations obtained from the image logs in the reservoirs. Bayes’ theorem, and a cascaded procedure in least-squares inversion, matching observed amplitudes to linearized Zoeppritz equations, were used to estimate the elastic moduli in a first step and normal and tangential fracture weaknesses in a second step. We used laboratory experiments on core samples to validate the first step inversion results. We found that the propagation wavelet varied in space and reflection time, and so a library of extracted wavelets in the time-frequency domain was used for seismic inversion. Maps of computed fracture fluid index and the estimated fracture weaknesses help to visualize the role of fractures in reservoir productivity and revealed a consistency with the seismic peak frequency attribute in identifying zones of highly compliant fracture fill. The estimated fracture model demonstrates a good fit with the fractures seen in the available core samples and implies that the fracture fluid index is a useful attribute for determining the productive zones in the reservoir.

Rate‐dependent mechanical properties and acoustic emission characteristic of recycled aggregate concrete under four‐point bending

AbstractThe application of recycled aggregate is crucial for waste concrete recycling, while the loading rate significantly influences the fracture behavior of recycled aggregate concrete. Herein, we investigate the effect of loading rate on the flexural and fracture behaviors of recycled aggregate concrete, along with associated acoustic emission characteristics. Results show that the flexural strength and energy absorption capacity maximum increase by 15.18% and 38.39%, respectively, as the loading rate rises. As the loading rate increases from 0.05 to 1 mm/min, peak frequency is primarily clustered in 0 ~ 75 kHz and 75 ~ 225 kHz and tends to be dense, while the b value decreases by 19.28%, 23.30%, and 30.17%, respectively. Additionally, the proportion of shear failure also decreases, which relates to the high energy release rate. Further, the multifractal spectrum α − f(α) resembles a quadratic distribution, with both multifractal intensity ∆f and spectrum width ∆α showing a downward trend as the loading rate rises.

The onset of parametric decay of lower hybrid waves during lower hybrid current drive experiments on Experimental Advanced Superconducting Tokamak

Abstract An ad hoc calculation is presented to interpret the growth of sideband waves during lower hybrid current drive (LHCD) experiments on Experimental Advanced Superconducting Tokamak (EAST). We adopt the solution of the non-linear dispersion relation for parametric instabilities to describe the growth rate of ion cyclotron quasimodes (ICQM) in the peripheral plasma, and use the WKB solution of the propagation of electrostatic waves in two dimensions to model the variation of the electric field in the scrape-off-layer (SOL). We then calculate the growth rates and amplification factors of the sideband waves associated with ICQM in the parallel and perpendicular coupling limits. The onset and substantial growth of the first down-shifted sideband of lower hybrid waves (LHWs) are associated with elevation of plasma density in the outer SOL where the growth rate of ICQM in the parallel coupling limit increases sharply after the density is elevated. The peak frequencies associated with the first harmonic of ICQM obtained from calculating the amplification factors in the parallel coupling limit (Apara) agree very well with the observed frequencies of the first down-shifted sideband of 2.45GHz LHWs in EAST pulse 43772 and 86521. The consistency between the calculation and the observation indicates that the sideband waves are associated with PDI of ICQM excited in the outer SOL through parallel coupling. The amplitude of Apara obtained from a single pass of LHWs through the SOL of EAST is, however, one order of magnitude smaller than unity, meaning that it remains elusive whether the substantial growth of the sideband waves mainly occurs in front of the antenna or requires multiple passes of LHWs across the SOL region.

Radial dependence of ionization clustering around a gold nanoparticle irradiated by X-rays under charged particle equilibrium

Objective.This work explores the enhancement of ionization clustering and its radial dependence around a gold nanoparticle (NP), indicative of the induction of DNA lesions, a potential trigger for cell-death.Approach.Monte Carlo track structure simulations were performed to determine (a) the spectral fluence of incident photons and electrons in water around a gold NP under charged particle equilibrium conditions and (b) the density of ionization clusters produced on average as well as conditional on the occurrence of at least one interaction in the NP using Associated Volume Clustering. Absorbed dose was determined for comparison with a recent benchmark intercomparison. Reported quantities are normalized to primary fluence, allowing to establish a connection to macroscopic dosimetric quantities.Main results.The modification of the electron spectral fluence by the gold NP is minor and mainly occurs at low energies. The net fluence of electrons emitted from the NP is dominated by electrons resulting from photon interactions. Similar to the known dose enhancement, increased ionization clustering is limited to a distance from the NP surface of up to200nm. The number of clusters per energy imparted is increased at distances of up to150nm, and accordingly the enhancement in clustering notably surpasses that of dose enhancement. Smaller NPs cause noticeable peaks in the conditional frequency of clusters between50nm-100nmfrom the NP surface.Significance.This work shows that low energy electrons emitted by NPs lead to an increase of ionization clustering in their vicinity exceeding that of energy imparted. While the electron component of the radiation field plays an important role in determining the background contribution to ionization clustering and energy imparted, the dosimetric effects of NPs are governed by the interplay of secondary electron production by photon interaction and their ability to leave the NP.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Preoperative biomarkers associated with delayed neurocognitive recovery.

To identify baseline biomarkers of delayed neurocognitive recovery (dNCR) using monitors commonly used in anesthesia. In this sub-study of observational prospective cohorts, we evaluated adult patients submitted to general anesthesia in a tertiary academic center in the United States. Electroencephalographic (EEG) features and cerebral oximetry were assessed in the perioperative period. The primary outcome was dNCR, defined as a decrease of 2 scores in the global Montreal Cognitive Assessment (MoCA) between the baseline and postoperative period. Forty-six adults (median [IQR] age, 65 [15]; 57% females; 65% American Society of Anesthesiologists (ASA) 3 were analyzed. Thirty-one patients developed dNCR (67%). Baseline higher EEG power in the lower alpha band (AUC = 0.73 (95% CI 0.48-0.93)) and lower alpha peak frequency (AUC = 0.83 (95% CI 0.48-1)), as well as lower cerebral oximetry (68 [5] vs 72 [3], p = 0.011) were associated with dNCR. Higher EEG power in the lower alpha band, lower alpha peak frequency, and lower cerebral oximetry values can be surrogates of baseline brain vulnerability.

Phase transition and internal friction behaviors in Mn-doped Co-V-Ga shape memory alloys

Shape memory alloys with a high abundance of mobile interfaces exhibit remarkable internal friction (IF) behaviors, which can be utilized for the reduction of noise and vibration. In this study, we systematically investigate the martensitic transformation and internal friction behaviors in the Mn-doped Co53V35–xGa12Mnx (x = 0 - 10) polycrystalline alloys. Our findings indicate that using Mn to replace V results in decreased transformation temperatures but increased thermal hysteresis. The addition of Mn enhances magnetic exchange interaction, leading to a gradual enhancement in magnetization difference across martensitic transformation, thereby compromising the transformation entropy change and arresting the martensitic transformation. At a frequency of 0.4 Hz, the intensity of transformation IF peak is enhanced from 0.124 to 0.178 with increasing the Mn content from 0 to 4.5 %, as a result of deteriorated geometrical compatibility between austenite and martensite due to the addition of Mn. Furthermore, hydrogenation treatments substantially improve both the peak intensity and frequency dependence of peak position for the relaxation-type IF, demonstrating the great contribution from hydrogen-twin boundary interaction.

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

Investigating the simulation test and acoustic emission characteristics of structural-control type rockbursts in deep underground environments

Deep underground projects are in a complex in-situ stress environment, and rock burst disasters induced by the hard and brittle failure of the rock mass are prone to occur around the tunnel openings. In this study, we aim to investigate the impacts of geological weak surfaces in rock masses on rockbursts and the spatiotemporal evolution of microcracks during the development of rockbursts. To achieve this objective, two sets of cubic specimens containing a circular through-hole were designed. One set of specimens had prefabricated unfilled cracks around the openings. Subsequently, the rockburst development process within the deep tunnel was replicated by subjecting both groups of specimens to identical gradient loading using a true triaxial test system. During the test, a micro camera was used to record the failure of the borehole wall in real-time, and an acoustic emission monitoring system was utilized to capture the stress waves released during structural damage. Finally, a multi-level synergistic analysis of the rockburst damage mechanism was carried out based on the macro-imaging data and micro-acoustic emission data. The results show that failure in high-stress environments within tunnels mainly includes microcrack initiation, particle ejection, crack propagation, local cracking, slabbing spalling, damage penetration, and rockburst damage. The time–frequency domain information of acoustic emission signals is highly perceptive and representative of the structural damage state of the specimens. The mutation characteristics observed in time-domain parameters, such as acoustic emission amplitude, event rate, ringing, and cumulative energy, correspond to drastic alterations in the internal structure of the rock. The distribution characteristics of frequency-domain parameters, such as acoustic emission peak frequency, dominant frequency energy, and frequency centroid, reflect different crack scales and damage modes. The diffusion of frequency centroid indicates that the damage and failure patterns within the rock are evolving towards a more complex direction. The energy magnitude of acoustic emissions represents the damage intensity during the development of rockburst. The root causes of induced structural-control type rockbursts in deep hard brittle rock masses are the combined effects of localized high-stress concentrations and large-scale discontinuous geological structures, such as natural joints, fissures, and structural planes. The naturally occurring large-scale through-type geological weak surfaces within the rock mass reduce the strength of the surrounding rock, alter the location of stress concentration, and change the initial damage characteristics. Moreover, they promote the evolution of rockbursts, thereby increasing their destructive intensity.

Specific EEG resting state biomarkers in FXS and ASD

BackgroundFragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments.MethodsResting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5–20), 49 participants with ASD (aged between 6–17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations.ResultsOur results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated.ConclusionsThese findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

Development of a Wind–Wave Coherence Function Based on Numerical Studies

The synchronization and intensity of fluctuating wind speeds and wave surfaces in wind–wave joint propagation processes are affected by the coherence of the marine ambient factors of fluctuating wind and random waves. This coherence further affects the precise calculations of wind–wave joint actions on marine structures. Therefore, a wind–wave joint propagation numerical flume was established based on the numerical simulation of random waves and fluctuating wind fields. A series of numerical simulations of wind–wave joint propagations were carried out. Based on the numerical results, the influences and influence laws of factors such as wind speed position height, significant wave height and wave spectrum peak frequency on the wind–wave coherence values were studied. According to the influence characteristics of these factors, a function of wind–wave coherence values for random wind–wave joint propagation was calculated. The coherence function takes frequency as the variable, while parameters include significant wave height, wind speed position height and wave spectrum peak frequency. Through a series of numerical simulation results, data fitting was used to calculate the parameter coefficients of the coherence function. The established random wind–wave coherence function can be described using the wind–wave joint fields of marine structures and the computational analyses of structural wind–wave joint actions.

Effect of Atmospheric Stability on Meandering and Wake Characteristics in Wind Turbine Fluid Dynamics

This study investigates the impact of atmospheric stability on wind turbine flow dynamics, focusing on wake deflection and meandering. Using the high-fidelity large-eddy simulation coupled with the Actuator Line model, we explore three stability conditions for the Vestas V80 turbine, both with and without yaw. The results indicate that wake meandering occurs predominantly along the deflected wake axis. Despite varying wake deficits and meandering behaviors, neutral and stable conditions exhibit similar wake deflection trajectories during yawed turbine operations. Spectral analysis of meandering reveals comparable cutoff and peak frequencies between neutral and stable cases, with a consistent Strouhal number (St=0.16). The unstable condition shows significant deviations, albeit with associated uncertainties. Overall, increased stability decreases both oscillation amplitude and frequency, highlighting the complex interplay between atmospheric stability and wind turbine wake dynamics.

A Comprehensive Study on the Mid-Infrared Variability of Blazars

We present a comprehensive investigation of mid-infrared (MIR) flux variability at 3.4 μm (W1 band) for a large sample of 3816 blazars, using Wide-field Infrared Survey Explorer (WISE) data through December 2022. The sample consists of 1740 flat-spectrum radio quasars (FSRQs), 1281 BL Lac objects (BL Lacs), and 795 blazars of uncertain type (BCUs). Considering Fermi Large Area Telescope detection, we classify 2331 as Fermi blazars and 1485 as non-Fermi blazars. Additionally, based on synchrotron peak frequency, the sample includes 2264 low-synchrotron peaked (LSP), 512 intermediate-synchrotron peaked (ISP), and 655 high-synchrotron peaked (HSP) sources. We conduct a comparative analysis of short- and long-term intrinsic variability amplitude (σm), duty cycle (DC), and ensemble structure function (ESF) across blazar subclasses. The median short-term σm values were 0.181−0.106+0.153, 0.104−0.054+0.101, 0.135−0.076+0.154, 0.173−0.097+0.158, 0.177−0.100+0.156, 0.096−0.050+0.109, and 0.106−0.058+0.100 mag for FSRQs, BL Lacs, Fermi blazars, non-Fermi blazars, LSPs, ISPs, and HSPs, respectively. The median DC values were 71.03−22.48+14.17, 64.02−22.86+16.97, 68.96−25.52+15.66, 69.40−22.17+14.42, 71.24−21.36+14.25, 63.03−33.19+16.93, and 64.63−24.26+15.88 percent for the same subclasses. The median long-term σm values were 0.137−0.105+0.408, 0.171−0.132+0.206, 0.282−0.184+0.332, 0.071−0.062+0.143, 0.218−0.174+0.386, 0.173−0.132+0.208, and 0.101−0.077+0.161 mag for the same subclasses, respectively. Our results reveal significant differences in 3.4 μm flux variability among these subclasses. FSRQs (LSPs) exhibit larger σm and DC values compared to BL Lacs (ISPs and HSPs). Fermi blazars display higher long-term σm but lower short-term σm relative to non-Fermi blazars, while DC distributions between the two groups are similar. ESF analysis further confirms the greater variability of FSRQs, LSPs, and Fermi blazars across a wide range of time scales compared to BL Lacs, ISPs/HSPs, and non-Fermi blazars. These findings highlight a close correlation between MIR variability and blazar properties, providing valuable insights into the underlying physical mechanisms responsible for their emission.

Method for evaluating healing state of self-healing ceramics using acoustic emission

Previous studies have evaluated the volume expansion rate of oxidation products and healing temperature and time. However, the bonding strength between the product and the matrix is yet to be appropriately evaluated. To develop a self-healing agent selection method for ceramics, this study proposed a method for examining the healing state and fracture behavior of self-healing ceramics through acoustic emissions (AE) generated during three-point bending tests. This study fabricated completely and incompletely healed SiC30 vol%/Al2O3 composite specimens by adjusting the healing time, and the crack length was assessed employing linear fracture mechanics, and effect of healing state on cumulative AE energy and AE frequency. The crack lengths were compared with those of the kinetic model of crack healing. The crack lengths of the 5-h healed specimens were estimated by linear fracture mechanics to be 12.5 μm for incomplete and 10.4 μm for complete healing, while that estimated by the kinetic model were 13.5 μm, indicating the usefulness of the kinetic model in the initial stages of crack healing. Furthermore, while the AE frequency at the initial stage of fracture was dominated by peaks below 100 kHz in damaged or incompletely healed specimen, there was a relatively high frequency peak of over 200 kHz in smooth and completely healed specimen, The average cumulative AE energies of smooth, damaged, 5 h-healed (incompletely), 5 h-healed (completely), and 24 h-healed specimens were 395, 0.89, 26.8, 189, and 318 V・μs, respectively. Thus, the AE energy that accumulated until rupture tended to increase as healing progressed, these findings suggest that the AE method can be used to determine healing conditions. These results will help establish a method for selecting self-healing agents according to the environment in which they are used.

Rockburst early-warning method based on time series prediction of multiple acoustic emission parameters

Rockburst early-warning is crucial for ensuring safety in deep underground engineering. Existing methods primarily focus on classifying rockburst grades, making it challenging to provide timely warnings. This paper proposes a novel rockburst early-warning framework based on time series prediction of acoustic emission (AE) parameters. Six AE parameters (rise time, count, duration, amplitude, absolute energy, and peak frequency) were identified as potential indicators for rockburst early-warning based on rockburst tests. A sliding window method was applied to process normalized AE data, calculating statistical parameters of the local duration. An LSTM-based time series prediction model was developed to forecast the future evolution of these AE parameters. This, in turn, enabled the establishment of a comprehensive multi-indicator early-warning system. The Isolation Forest (IF) algorithm, an outlier detection method, was used to determine the warning thresholds for each indicator. The CRITIC weighting method was employed to integrate the six rockburst indicators into a single early-warning coefficient (EC), with EC=100 signifying the warning trigger condition. The results demonstrate that the proposed framework effectively captures the evolution trends of AE parameters, enabling proactive early warnings. This approach addresses the limitations of existing methods, such as reliance on experience for threshold determination, lack of a clear basis for multi-indicator weights, and difficulty in quantifying early-warning trigger conditions. The framework provides a new perspective for rockburst early-warning systems.

A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water-Rock Interaction.

The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.