Shear band evolution and acoustic emission characteristics of sandstone containing non-persistent flaws

Direct shear tests were conducted on sandstone specimens under different constant normal stresses to study the coalescence of cracks between non-persistent flaws and the shear sliding characteristics of the shear-formed fault.Digital image correlation and acoustic emission(AE)techniques were used to monitor the evolution of shear bands at the rock bridge area and microcracking behaviors.The experimental results revealed that the shear stresses corresponding to the peak and sub-peak in the stressdisplacement curve are significantly affected by the normal stress.Strain localization bands emerged at both the tip of joints and the rock bridge,and their extension and interaction near the peak stress caused a surge in the AE hit rate and a significant decrease in the AE b value.Short and curvilinear strain bands were detected at low normal stress,while high normal stress generally led to more microcracking events and longer coplanar cracks at the rock bridge area.Furthermore,an increase in normal stress resulted in a higher AE count rate and more energetic AE events during friction sliding along the shearformed fault.It was observed that the elastic energy released during the crack coalescence at the prepeak stage was much greater than that released during friction sliding at the post-peak stage.More than 75%of AE events were located in the low-frequency band(0e100 kHz),and this proportion continued to rise with increasing normal stress.Moreover,more AE events of low AF value and high RA value were observed in specimens subjected to high normal stress,indicating that greater normal stress led to more microcracks of shear nature.

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Acoustic emission signal identification of different rocks based on SE-1DCNN-BLSTM network model

In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al content(BMC),and then determine the content of brittle matter in rock.To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture,a 1DCNN-BLSTM network model with SE module is constructed in this study.The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals,the non-correlated features of the local space and the weak periodicity law.Furthermore,the processed signals data is input into the fully connected layers.Finally,softmax function is used to accurately identify the acoustic emission signals released by different rocks,and then determine the content of brittle minerals contained in rocks.Through experimental comparison and analysis,1DCNN-BLSTM model embedded with SE module has good anti-noise performance,and the recognition accuracy can reach more than 90 percent,which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission re-search.

Study on Acoustic Emission Characteristics of Deformation Damage Process of Zirconia Ceramics

Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited research on the deformation and damage process of zirconia ceramics. This article analyzes the acoustic emission characteristics of each stage of ceramic damage from the perspective of acoustic emission, and explores its deformation process characteristics from multiple perspectives such as time domain, frequency, and EWT modal analysis. It is concluded that zirconia ceramics exhibit higher brittleness and acoustic emission strength than alumina ceramics, and when approaching the fracture, it tends to generate lower frequency acoustic emission signals.

Implications for identification of principal stress directions from acoustic emission characteristics of granite under biaxial compression experiments

The rock fracture characteristics and principal stress directions are crucial for prevention of geological disasters.In this study,we carried out biaxial compression tests on cubic granite samples of 100 mm in side length with different intermediate principal stress gradients in combination with acoustic emission(AE)technique.Results show that the fracture characteristics of granite samples change from‘sudden and aggregated’to‘continuous and dispersed’with the increase of the intermediate principal stress.The effect of increasing intermediate principal stress on AE amplitude is not significant,but it increases the proportions of high-frequency AE signals and shear cracks,which in turn increases the possibility of unstable rock failure.The difference of stress in different directions causes the anisotropy of rock fracture and thus leads to the obvious anisotropic characteristics of wave velocity variations.The anisotropy of wave velocity variations with stress difference is probable to identify the principal stress directions.The AE characteristics and the anisotropy of wave velocity variations of granite under two-dimensional stress are not only beneficial complements for rock fracture characteristic and principal stress direction identification,but also can provide a new analysis method for stability monitoring in practical rock engineering.

Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention

Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning.Considering that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection tasks.Based on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral respectively.Experimental results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.

Mechanical responses and acoustic emission behaviors of coal under compressive differential cyclic loading(DCL):a numerical study via 3D heterogeneous particle model

The stability of coal walls(pillars)can be seriously undermined by diverse in-situ dynamic disturbances.Based on a 3D par-ticle model,this work strives to numerically replicate the major mechanical responses and acoustic emission(AE)behaviors of coal samples under multi-stage compressive cyclic loading with different loading and unloading rates,which is termed differential cyclic loading(DCL).A Weibull-distribution-based model with heterogeneous bond strengths is constructed by both considering the stress-strain relations and AE parameters.Six previously loaded samples were respectively grouped to indicate two DCL regimes,the damage mechanisms for the two groups are explicitly characterized via the time-stress-dependent variation of bond size multiplier,and it is found the two regimes correlate with distinct damage patterns,which involves the competition between stiffness hardening and softening.The numerical b-value is calculated based on the mag-nitudes of AE energy,the results show that both stress level and bond radius multiplier can impact the numerical b-value.The proposed numerical model succeeds in replicating the stress-strain relations of lab data as well as the elastic-after effect in DCL tests.The effect of damping on energy dissipation and phase shift in numerical model is summarized.

Moment tensor and stress inversion solutions of acoustic emissions during compression and tensile fracturing in crystalline rocks

We investigate the accuracy and robustness of moment tensor(MT)and stress inversion solutions derived from acoustic emissions(AEs)during the laboratory fracturing of prismatic Barre granite specimens.Pre-cut flaws in the specimens introduce a complex stress field,resulting in a spatial and temporal variation of focal mechanisms.Specifically,we consider two experimental setups:(1)where the rock is loaded in compression to generate primarily shear-type fractures and(2)where the material is loaded in indirect tension to generate predominantly tensile-type fractures.In each test,we first decompose AE moment tensors into double-couple(DC)and non-DC terms and then derive unambiguous normal and slip vectors using k-means clustering and an unstructured damped stress inversion algorithm.We explore temporal and spatial distributions of DC and non-DC events at different loading levels.The majority of the DC and the tensile non-DC events cluster around the pre-cut flaws,where macro-cracks later develop.Results of stress inversion are verified against the stress field from finite element(FE)modeling.A good agreement is found between the experimentally derived and numerically simulated stress orientations.To the best of the authors’knowledge,this work presents the first case where stress inversion methodologies are validated by numerical simulations at laboratory scale and under highly heterogeneous stress distributions.

Experiment on acoustic emission response and damage evolution characteristics of polymer-modified cemented paste backfill under uniaxial compression

The mechanical properties of cemented paste backfill(CPB)determine its control effect on the goaf roof.In this study,the mechanical strength of polymer-modified cemented paste backfill(PCPB)samples was tested by uniaxial compression tests,and the failure characteristics of PCPB under the compression were analyzed.Besides,acoustic emission(AE)technology was used to monitor and record the cracking process of the PCPB sample with a curing age of 28 d,and two AE indexes(rise angle and average frequency)were used to classify the failure modes of samples under different loading processes.The results show that waterborne epoxy resin can significantly enhance the mechanical strength of PCPB samples(when the mass ratio of polymer to powder material is 0.30,the strength of PCPB samples with a curing age of 28 d is increased by 102.6%);with the increase of polymer content,the mechanical strength of PCPB samples is improved significantly in the early and middle period of curing.Under uniaxial load,the macro cracks of PCPB samples are mostly generated along the axial direction,the main crack runs through the sample,and a large number of small cracks are distributed around the main crack.The AE response of PCPB samples during the whole loading process can be divided into four periods:quiet period,slow growth period,rapid growth period,and remission period,corresponding to the micro-pore compaction stage,elastic deformation stage,plastic deformation stage,and failure instability stage of the stress-strain curve.The AE events are mainly concentrated in the plastic deformation stage;both shear failure and tensile failure occur in the above four stages,while tensile failure is dominant for PCPB samples.This study provides a reference for the safety of coal pillar recovery in pillar goaf.

Master crack types and typical acoustic emission characteristics during rock failure

Acoustic emission(AE)signals contain substantial information about the internal fracture characteristics of rocks and are useful for revealing the laws governing the release of energy stored therein.Reported here is the evolution of rock failure with diferent master crack types as investigated using Brazilian splitting tests(BSTs),direct shear tests(DSTs),and uniaxial compression tests(UCTs).The AE parameters and typical modes of each fracture type were obtained,and the energy release characteristics of each fracture mechanism were discussed.From the observed changes in the AE parameters,the rock fracture process exhibits characteristics of staged intensifcation.The scale and energy level of crack activity in the BSTs were signifcantly lower than those in the DSTs and UCTs.The proportion of tensile cracks in the BSTs was 65%–75%,while the proportions of shear cracks in the DSTs and UCTs were 75%–85%and 70%–75%,respectively.During the rock loading process under diferent conditions,failure was accompanied by an increased number of shear cracks.The amplitude,duration,and rise time of the AE signal from rock failure were larger when the failure was dominated by shear cracks rather than tensile ones,and most of the medium-and high-energy signals had medium to low frequencies.After calculating the proposed energy amplitude ratio,the energy release of shear cracks was found to exceed that of tensile cracks at the same fracture scale.

Evaluation of Self-Healing Efficiency of Microcapsule-Based Self-Healing Cementitious Composites Based on Acoustic Emission

Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cementbased composites.The evaluation method of the self-healing efficiency of microcapsule self-healing cement-based composites is one of the difficulties that limits the self-healing technology.This paper attempts to characterize the self-healing efficiency of microcapsule self-healing cement-based composites by acoustic emission(AE)parameters,which provides a reference for the evaluation of microcapsule self-healing technology.Firstly,a kind of self-healing microcapsules were prepared,and the microcapsules were added into the cement-based composites to prepare the compression samples.Then,the specimen with certain pre damage was obtained by compression test.Secondly,the damaged samples were divided into two groups.One group was directly used for compression tests to obtain the damage failure process.The other group was put into water for healing for 30 days,and then compression tests were carried out to study the influence of self-healing on the compression failure process.During the experiments,the AE signals were collected and the AE characteristics were extracted for the evaluation of self-healing efficiency.The results show that the compression pre damage test can trigger the microcapsule,and the compression strength of the self-healing sample is improved.The failure mechanism of microcapsule selfhealing cement-based composites can be revealed by the AE parameters during compression,and the self-healing efficiency can be quantitatively characterized by AE hits.The research results of this paper provide experimental reference and technical support for the mechanical property test and healing efficiency evaluation of microcapsule self-healing cement-based composites.

Microscopic damage evolution of anisotropic rocks under indirect tensile conditions: Insights from acoustic emission and digital image correlation techniques

The anisotropy induced by rock bedding structures is usually manifested in the mechanical behaviors and failure modes of rocks.Brazilian tests are conducted for seven groups of shale specimens featuring different bedding angles. Acoustic emission (AE) and digital image correlation (DIC) technologies are used to monitor the in-situ failure of the specimens. Furthermore, the crack morphology of damaged samples is observed through scanning electron microscopy (SEM). Results reveal the structural dependence on the tensile mechanical behavior of shales. The shale disk exhibits compression in the early stage of the experiment with varying locations and durations. The location of the compression area moves downward and gradually disappears when the bedding angle increases. The macroscopic failure is well characterized by AE event location results, and the dominant frequency distribution is related to the bedding angle. The b-value is found to be stress-dependent.The crack turning angle between layers and the number of cracks crossing the bedding both increase with the bedding angle, indicating competition between crack propagations. SEM results revealed that the failure modes of the samples can be classified into three types:tensile failure along beddings with shear failure of the matrix, ladder shear failure along beddings with tensile failure of the matrix, and shear failure along multiple beddings with tensile failure of the matrix.

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.

Characterization,identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.

The study of acoustic emission (AE) forecasting coal and rock disaster technique

Introduced the coal and rock AE propagation rule,wave guide fixing technics onAE sensors,and AE forecasting coal and rock disaster on the scene and so on,The coaland rock AE propagation rule that follows the exponent attenuation function on different AEfrequencies,different quality factors and different propagation distances were analyzedand deduced by theory,numerical simulation,and by actual experiment.Consequently,itwas deduced that the coal and rock AE propagation rule follows the exponent attenuationfunction.Based on the correlative theory of wave dynamics and AE sensor,the AE waveguide propagation mechanical model on the sensor fixing manner is found,and the relationsof displacement and speed and acceleration between the AE signal source and theAE signal receiving terminal are presented.The effect of the AE sensor fixing manners oncoal and rock surfaces,coal and rock bottoms and wave guides were studied by actualexperiment.For the results,the effect of the AE sensor fixing manner on wave guides isbetter than on coal and rock surfaces,and was equivalent to the fixing manner on coal androck bottoms.Based on the above study results,actual coal and rock dynamistic disasterswere successfully forecasted.

Application of quantum neural networks in localization of acoustic emission

Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.

Fracture propagation in sandstone and slateeLaboratory experiments, acoustic emissions and fracture mechanics

Fracturing of highly anisotropic rocks is a problem often encountered in the stimulation of unconventional hydrocarbon or geothermal reservoirs by hydraulic fracturing. Fracture propagation in isotropic material is well understood but strictly isotropic rocks are rarely found in nature. This study aims at the examination of fracture initiation and propagation processes in a highly anisotropic rock, specifically slate. We performed a series of tensile fracturing laboratory experiments under uniaxial as well as triaxial loading. Cubic specimens with edge lengths of 150 mm and a central borehole with a diameter of13 mm were prepared from Fredeburg slate. An experiment using the rather isotropic Bebertal sandstone as a rather isotropic rock was also performed for comparison. Tensile fractures were generated using the sleeve fracturing technique, in which a polymer tube placed inside the borehole is pressurized to generate tensile fractures emanating from the borehole. In the uniaxial test series, the loading was varied in order to observe the transition from strength-dominated fracture propagation at low loading magnitudes to stress-dominated fracture propagation at high loading magnitudes.

Effect of Deliberate Ingestion of Organophosphate Pesticide on Distortion Product Oto-Acoustic Emissions (DPOAE)

Background: Oto-Acoustic Emissions (OAE) are low intensity sounds produced by the cochlea in response to an acoustic stimulus. Deliberate self-poisoning has become an increasingly common response to emotional distress in young adults (NHS, 1998). Organophosphate (OP) compounds are most commonly involved in 76% of pesticide poisoning especially in the developing countries like India. Several pesticides are neurotoxin which could potentially affect hearing and, animal studies showed that OP treatment leads to change in hearing threshold, outer and inner hair cell loss but human studies on the same are very few. Aim: The present study was aimed at exploring the cochlear changes after self-poisoning with OP pesticides by measuring the Distortion Product Oto-Acoustic Emissions (DPOAE). Method: 114 subjects were participated in the study, with an age range of 18-30 years having pure tone audiometric thresholds within the normal limits and no history of middle ear pathology. They were further divided into two groups, I and II which includes self-poisoned and healthy subjects respectively. Results: The “pass” and “fail” criteria of DPOAE measures in group I were 30% and 70% respectively whereas in group II, it was found 91% and 9% respectively. Discussion: There was a significant failure in DPOAE in Group I (subjects who were self-poisoned) compared to group II (healthy subjects). The two important pathologies behind OP toxicity are the generation of Reactive Oxygen Species and the depletion of NADPH which is necessary for normal function. Conclusion: The results of the present study highlight that;the minute cochlear changes caused due to pesticide poisoning can be effectively measured through DPOAE. Further it can suggest in other clinical targets like Ototoxicity, Noise-induced hearing loss, Meniere’s disease etc., which has an effect on cochlear hair cells can be monitored through DPOAE.

Numerical modeling techniques for noise emission of free railway wheels

In this article,we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions.We focus on the fluid-structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources.These are a discretized baffled piston using the discrete calculation method(DCM),a closed cylindrical volume using the boundary element method(BEM)and radiating elastic disks in a cubic enclosure solved with the finite element method(FEM).We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models.Selected space-resolved waveforms are compared with experimental results as well as with a fluid-structure interaction finite element model.The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted,and the baffled pistons limitations caused by a lack of edge radiation effects are investigated.