Acoustic Emission Signals Research Articles

Tension and Shear Behaviour of Basalt Fiber Bio-Composites with Digital Image Correlation and Acoustic Emission Monitoring.

This research investigates the mechanical behavior and damage evolution in cross-ply basalt fiber composites subjected to different loading modes. A modified Arcan rig for simultaneous acoustic emission (AE) monitoring was designed and manufactured to apply quasi-isotropic shear, combined tensile and shear loading, and pure tensile loading on specimens with a central notch. Digital image correlation (DIC) was applied for high-resolution strain measurements. The measured failure strengths of the bio-composite specimens under different loading angles are presented. The different competing failure mechanisms that contribute to the local reduction in stress concentration are described. Different damage mechanisms trigger elastic waves in the composite, with distinct AE signatures that closely follow the sequence of fracture mechanisms. AE monitoring is employed to capture signals associated with structural damage initiation and progression. The characteristic parameters of AE signals are correlated with crack modes and damage mechanisms. The evolution of AE parameters during the peak load transition is presented, which enables the timely AE detection of the maximum load transition. The combination of DIC and AE monitoring improves understanding of the mechanical response and failure mechanisms in cross-ply basalt fiber composites, offering valuable insights for possible performance monitoring and structural reliability in diverse engineering applications.

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

The localization results of acoustic emission (AE) events can reflect the location and pattern of burst-prone rock failures. However, event localization heavily depends on the quality of the original waveform of the sensor. Therefore, this study analyzed the AE waveform of a rock sample under compression to evaluate its failure localization and quality. From the research results, it could be seen that the initial failure was relatively calm, with clear take-off points, which can be better used for accurate AE event positioning. However, the later failure was severe, causing the take-off points of most sensors to be very unclear, and positioning methods that rely on take-off points cannot be used for positioning, let alone simply using the positioning results of the built-in software. This research result reminds researchers who use AE signals for event localization to first examine the quality and status of the original waveform, providing a basis for obtaining accurate localization results, in order to further accurately study the subsequent failure patterns. The above facts indicate that the initial failure is small and scattered, while the later failure is large and concentrated, with certain fractal characteristics.

Acoustic emission signatures for quantifying damage patterns in half grouted sleeve connection under tensile load

The half-grouted sleeve connection (HGSC) have been widely used as connection tools for prefabricated concrete (PC) buildings. It is crucial to maintain the safety and stability of the nodes and detecting internal defects in the grouting sleeve for the safety of the entire structure. Therefore, understanding the mechanical behavior and damage mechanism of HGSC is essential for the design and construction of PC. Currently, there are few on-site techniques applicable for monitoring the damage progress. The acoustic emission (AE) as a passive method shows great potential to characterize the damage in the HGSC. Therefore, in this paper, we propose to use AE to quantify the damage of the HGSC with defects under the tensile load. The artificial defects were introduced into HGSC with different defective rates. The monotonic tensile load was applied to each HGSC sample with a loading rate of 0.33 mm/s and tested to the failure. In this process, the AE signals were collected by AE transducers. For the classification and prediction of damage signals, we utilized a machine learning framework tailored for HGSC. This framework integrates unsupervised k-means++ cluster analysis and supervised K-Nearest Neighbors (K-NN) for damage classification and prediction. The experimental results showed that four AE signal clusters can be defined during the progress of tensile failure:(i) microcrack expansion;(ii) the friction signals of rebar and high-strength grouts; (iii)the generation of microcracks; (iv) plastic deformation of rebar. The corresponding AE signatures were identified. The K-NN model trained on the training data provides a high accuracy in predicting different class of defects. It was concluded that the proposed machine learning framework is a promising and powerful tool for realizing the on-site damage detection and prediction for HGSC.

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Transfer learning-based quality monitoring of laser powder bed fusion across materials

Laser powder bed fusion (LPBF) is a promising additive manufacturing (AM) technique that has gained rapid growth. Despite the unique advantages such as producing complex-shaped metal parts, LPBF has been still slowed by the poor process repeatability and part consistency. In-situ monitoring methods combining sensing sensors and machine leaning (ML) tools have attracted much attention for defect detection and quality identification. However, the current ML algorithms usually rely on the availability of massive labeled datasets, and the trained model for monitoring a certain powder material cannot be directly applied to monitoring another material with different properties. Moreover, for a new material, it is time-consuming and expensive to collect the sufficient labeled data for building the quality monitoring model from scratch. To this end, motivated by transfer learning (TL), we propose a TL-based quality monitoring approach across two different materials in the LPBF process. Three different sensing datasets of layer-wise images of the solidified layer, photodiode signals, and acoustic emission signals are separately transferred from the 316L stainless steel to the TC4 titanium alloy. A convolutional neural network (CNN)-based quality monitoring model is first built by the sufficient source datasets from 316L stainless steel material. Then, the pre-trained CNN model is fine-tuned with a small amount of the target datasets for quality monitoring on TC4 titanium alloy material. Experimental results demonstrate the effectiveness of the proposed method for quality monitoring from one material to another with limited target datasets, with a classification accuracy above 92 % just with 5 % of target datasets on all three transferred datasets.

Experimental study of the “AE quiet period” on the eve of brittle failure in hard rock

Acoustic emission (AE) signals on the eve of brittle failure in hard rock often indicate a quiet period, i.e., a significant decrease in the number of AE signals. To address the current problem of the poor understanding of the quiet period, the failure process of hard rock was simulated by tests and acquired the AE signals. The AE signal during the quiet period were analyzed. First, compared with the AE signals in other loading periods, the AE signals during the quiet period are characterized by a greater hit amplitude, a greater rise time and ring count, a sudden increase in the cumulative absolute energy, a greater amplitude of the signals in the main frequency band and so on. Second, the inhomogeneity of rock grains, stress level and loading rate all have a nonnegligible influence. The more inhomogeneous the rock grains are, the higher the stress level is, or the larger the loading rate is, the longer the duration of the quiet period is. Third, a hard rock damage evolution equation based on AE considering the effect of the quiet period is proposed, which can reasonably reflect the objective fact of accelerating damage during this period. Finally, the quiet period is due to the brittle failure of hard rock with inhomogeneous grains under high stress conditions, so identifying the quiet period is important for AE-based monitoring and warning of hard rock failure.

Detection of early fatigue damage during ultrasonic fatigue testing of steel by acoustic emission monitoring

Ultrasonic fatigue testing (USFT) is a time-efficient method for evaluation of the fatigue limit of metallic alloys in a high and very high cycle range. Propagation of a final crack at the very end of the fatigue life can be clearly detected by USFT parameters and monitoring techniques. In contrast, initiation of fatigue damage during USFT remains unclear. Despite its excellent sensitivity, the implementation of acoustic emission (AE) for damage monitoring is hindered by severe noisiness of acoustic signals from USFT. In addition to resonance-related quasi-stationary noises, USFT in a pulse-pause mode is accompanied by non-stationary ones, which makes recognition of material-related AE signals even more difficult. A special AE processing algorithm was developed to overcome this issue and get useful insights from USFT monitoring. Consistent cropping, Fourier transformation, adaptive filtration and thresholding allowed to calculate noise-free AE activity during USFT of 42CrMo4 steel. Remarkably, most of this activity was located in the very beginning of fatigue loading. In comparison to non-failed runout samples, AE activity of samples with fatigue cracks was significantly higher, indicating its relation to fatigue damage. The proposed AE processing principle can be helpful for monitoring of USFT samples and other parts, operating in resonance conditions.

Method for the P-wave arrival pickup of rock fracture acoustic emission signals under strong noise

This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. Complete ensemble empirical mode decomposition with adaptive noise + Wavelet (CEEMDAN + Wavelet) was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select intrinsic mode functions for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the short term average/long term average (STA/LTA) normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEMDAN + Wavelet + STA/LTA + AIC (Akaike information criterion) method with the absolute amplitude of the signal as the input are all within 10 ms, and 86.67% of the results are within 5 ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low signal-to-noise ratio signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters.

Reconstruction of the Amplitude of Signals of Acoustic Emission Based on Mathematically Modeling as a Stochastic Process

Reconstruction of the Amplitude of Signals of Acoustic Emission Based on Mathematically Modeling as a Stochastic Process

Fracture propagation and pore pressure evolution characteristics induced by hydraulic and pneumatic fracturing of coal.

A two-dimensional unsteady seepage model for coal using a finite element program is developed, and the temporal variations of key factors such as water pressure and hydraulic gradient are analyzed in this paper. Additionally, the triaxial rock mechanical experiment and utilized pneumatic fracturing equipment on raw coal samples to investigate both hydraulic and pneumatic fracturing processes are conducted. Through these experiments, the relationship between pressure and crack formation and expansion are examined. The analysis reveals that the pore pressure gradient at the coal inlet reaches its peak during rapid surges in water pressure but diminishes over time. Conversely, the pore pressure gradient at the outlet side exhibits a gradual increase. Hydraulic fracturing is most likely to occur at the water inlet during sudden increases in water pressure. Besides, as the permeability of coal decreases, the duration for seepage stabilization prolongs due to the intensified pore pressure gradient resulting from sudden increases in water pressure. Moreover, an extended period of high hydraulic gradient further increases the risk of hydraulic fracturing. The experimental findings indicate that coal samples initially experience tensile failure influenced by water and air pressure. Subsequently, mode I cracks form under pressure, propagating along the fracture surface and becoming visible. The main types of failure observed in hydraulic and pneumatic fracturing are diametrical tensile failure, and the development of fractures can be categorized into three distinct stages, which contains the initial stage characterized by slight volume changes while water pressure increases, the expansion stage when pressure reaches the failure strength, and the crack closure stage marked by little or even decreasing volume changes during pressure unloading. The acoustic emission signal accurately corresponds to these three stages.

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.

Deterioration evolution mechanism and damage constitutive model improvement of sandstone–coal composite samples under the effect of repeated immersion

Underground reservoirs in coal mines, consisting of goafs (By goaf, we mean the space that remains underground after the extraction of valuable minerals), are commonly utilized for mine water storage and drainage, with their primary load-bearing structures being the “roof–coal pillar” systems. Consequently, this structure must endure the repeated immersion behavior resulting from fluctuations in the mine water level, resulting in the risk of geological disasters. This paper analyzes the variation in mechanical properties of sandstone–coal composite samples after repeated immersion cycles through axial loading tests. The results indicate that the water content of the sample exhibits a notable and rapid increase with each successive immersion cycle. This corresponds to a decrease in the stress threshold and modulus parameters of the samples. Moreover, the acoustic emission signals serve as indicators of the softening characteristics of the samples. With the increase in immersion cycles, there is an augmentation in both the frequency and extent of shear cracks. The non-linear failure characteristics of the samples become more pronounced. Consequently, water significantly weakens the cementing material between rock grains. Both sandstone and coal display a decrease in deformation resistance capabilities at a macroscopic level. The constitutive model of the composite sample was improved based on the degradation characteristics of mechanical strength and strain energy parameters, which offers enhanced accuracy in analyzing the degradation process caused by water immersion. This paper offers a crucial theoretical foundation for comprehending the deterioration evolution characteristics of the “roof–coal pillar” bearing structure affected by repeated immersion.

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM.

Indirect measurement of process parameters in peripheral end milling based on acoustic emission signals

In machining, current methods for online estimation of cutting parameters heavily rely on piezoelectric dynamometers. Albeit their high precision, integration into industrial environments is limited due to high cost and practical machine mounting constraints. To address this challenge, this study presents an indirect measurement method of the cutting depths in peripheral milling based on acoustic emission sensors. The proposed method involves extracting time-based features from a tailor-made envelope of the acoustic emission signal, identifying transitions between cutting phases from its waveform, and using these features to estimate radial and axial depths of cut through a geometric model of the tool-workpiece contact zone. Experiments show that this method can achieve relative errors below 5% in measuring the depths of cut, approaching the precision levels of piezoelectric dynamometers. This suggests that acoustic emission is an alternative process variable, enriching the spectrum of monitoring systems traditionally dependent on the measurement of cutting forces.

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..

Impact of the water-wedge deterioration effect of pulsating water injection on AE waveforms and crack propagation during fracturing processes

Pulsating hydraulic fracturing (PHF) is a promising technology for enhancing the extraction of coalbed methane. In this study, the effects of pulsating water response of coal fractures were investigated. Prior to quasi-static fracturing, coal samples were subjected to pulsating water injection for 120 min. The fracturing process was monitored using an acoustic emission (AE) system and compared with direct fracturing without pulsating water injection. The Hilbert–Huang transform theory was employed for the analysis. The AE signals were examined in time and frequency domains, across various parameters, including intrinsic mode function (IMF) components, energy, three-dimensional spectrum, location features, and crack development. The results indicated a 37.82 % reduction in initiation pressure, accompanied by decreased AE counts and the promotion of gradual fracture initiation. Initially, the AE waveform amplitudes decreased, followed by an increase in distinctive patterns. Water injection primarily reduced the amplitudes during unstable fracturing, shifting the extreme IMF component forward. Moreover, it decreased the peak energy, shortened the energy release duration, and improved the signal-to-noise ratio during crack propagation. Water injection also narrowed the frequency distribution range, concentrated the energy distribution, expanded the extent of the fracture, increased the number of location points, and induced more macroscopic cracks within the coal body.

Investigation on the failure mechanism of hard rock cavern subjected to adjacent structural plane based on physical model experiments

Structural planes present significant safety challenges in the construction of deep hard rock caverns. However, the influence mechanism of a single dominant adjacent structural plane remains inadequately explored. This study conducted a series of physical model experiments with non-penetrating structural planes positioned adjacent to the cavern under uniaxial step compression, utilizing acoustic emission (AE) and digital image correlation (DIC) to monitor the failure process simultaneously. The results indicate that adjacent structural plane can mitigate stress-induced failure, reducing peak strength and elastic modulus by 10–42% and 13–31%, respectively. In the absence of a structural plane or a non-intersecting plane, stress-induced failure intensifies. Conversely, an intersecting plane contributes to greater crack stability. Precursor AE signals emerge earlier in the combination system. The intact cavern system shows an escalating tensile fracture ratio with loading, whereas the combination systems experience a diminishing tensile fracture ratio. Finally, particle flow code (PFC) simulation revealed that the presence of a single structural plane alters the cavern’s force chains, influencing stress concentration position. This research reveals that the structural plane has the potential to alter the cavern failure mechanism, providing insights into the complex interaction between structural planes and caverns in deep engineering.

Effect of unloading rate of normal stress on the frictional slip behaviour of fractured rocks

Unloading disturbances encountered during excavations in fractured rock masses significantly affect the safety of deep underground structures. However, the influence of the unloading rate on the frictional slip behaviour of fractured rocks is not fully understood. In this study, a series of fracture slip tests induced by unloading normal stress were carried out, where the effects of unloading rate, initial normal stress, and initial shear stress were considered. Acoustic emission (AE) technique was employed to monitor damage in the fractured rock specimens. The results reveal that the unloading rate played a significant role in the frictional slip behaviour and failure mode of the fractured rock specimens. At lower unloading rates, the stress path approached the failure envelope horizontally, whereas at higher unloading rates, the stress path followed a diagonal trajectory. The near-critical stress state and near-critical stress ratio were introduced to describe the normal and shear stresses during the fracture activation slip. The results show that lower unloading rates (≤0.010 MPa/s) consistently placed the near-critical stress state closer to the failure envelope compared with higher unloading rates. In addition, the near-critical stress ratio decreased with an increase in the unloading rate. The AE count exhibited a distinct peak before failure of the fractured rock. This initial peak in the AE signals corresponds to the violent rupture of the surface asperities, making it a valuable indicator for monitoring and predicting potential failures in fractured rock masses. An empirical model was developed to describe the relationship between the unloading rate and near-critical stress ratio.

Comprehensive analysis of bond stress transfer mechanisms in glass fiber-reinforced polymer-reinforced concrete beams using acoustic emission monitoring

The present study investigates the critical bond characteristics between glass fiber-reinforced polymer (GFRP) bars, which is essential for the application bars as corrosion-resistant components in structural concrete. The interfacial bond deterioration and stress transfer mechanisms have been investigated in the present study by employing acoustic emission (AE) monitoring in hinged-type GFRP-reinforced concrete (GFRP-RC) beam specimens. The bond strength of the test specimens, governed by chemical adhesion, mechanical interlocking, and frictional resistance, has been intricately examined. Various AE signal parameters as well as historic and severity indices derived from the AE signal parameters have been meticulously evaluated and correlated with bond stress and rebar slip variations. The present study has also introduced an innovative approach of using the sentry function for the assessment of bond deterioration and associated mechanisms. The results of the present study demonstrate the reliability of AE parameters in monitoring debonding and bond strength variation, the effectiveness of the sentry function in evaluating damage progression, and the utility of historic and severity indices in capturing micro-mechanism transformations. The accurate localization of debonding and the qualitative distribution of bond stress have also been demonstrated by through correlations between cumulative AE parameters and rebar slip, Additionally, the present study also highlights the significance of peak frequency variations in AE signals, serving as a crucial tool for quantifying the mechanical interlocking and interfacial friction. The results contribute significantly to precise assessments and development of effective strategies for mitigating bond deterioration in GFRP-RC elements.

Mechanical properties and damage characteristics analysis on recycled aggregate concrete with glazed hollow beads after high temperatures by acoustic emission method

This research investigates the effect of high temperature on the mechanical and acoustic emission properties of recycled aggregate concrete with glazed hollow beads (GHB-RAC). Uniaxial compression tests were conducted on GHB-RAC at varying recycled coarse aggregate (RCA) substitution rates and glazed hollow beads (GHB) contents, and acoustic emission (AE) technology was used to monitor the compression-induced damage throughout the testing process. This study delves into the mechanical properties and damage characteristics of GHB-RAC when exposed to fire. The findings reveal that AE characteristic coefficients aptly delineate the three-stage nature of axial compression damage in concrete at temperatures up to 400 °C. However, these stage-specific traits vanish at temperatures exceeding 400 °C. Additionally, through the analysis of AE signals at different temperatures, the compression damage process is elucidated based on the b-value method. The RA-AF analysis indicates that the number of shear cracks exhibits an increasing trend with increasing temperature, and an increment in GHB dosage positively influences the percentage of tensile cracking during loading, with the influence being more significant at 70 % GHB content. The acoustic emission sensing can accurately detect and localise damage. With the increase in temperature, the distribution of AE signals depicting concrete damage is more dispersed and the number of AE signals is more larger. Notably, the incidence of localized damage points diminishes with a higher GHB ratio, indicating the efficacy of GHBs in mitigating thermal damage to the concrete. Furthermore, a damage constitutive model incorporating variables such as temperature, GHB dosage, and recycled coarse aggregate replacement rate was proposed. The model employed AE energy count parameters as the damage indicator. The good fitting results suggest that the mechanics behavior of GHB-RAC under uniaxial compression can be well described by the model. Consequently, the study offers invaluable insights into the damage evolution of post-fire recycled aggregate concrete with GHB admixtures.