Acoustic Emission Signal Strength Research Articles

Mechanical Properties and Acoustic Emission Characteristics of Weakly Cemented Sandstone With Different Grain Sizes

The weakly cemented sandstone is widely distributed in the Western Mining Area, which is mainly formed by mineral grains and cemented minerals through compaction and cementation. To study the influence of grain size on the mechanical properties and acoustic emission (AE) characteristics of weakly cemented sandstone, uniaxial compression and Brazilian splitting AE tests were carried out on four weakly cemented sandstone specimens with different grain sizes. The physical properties, mechanical behaviors, and AE characteristics of sandstone under two conditions were analyzed, and the microfailure mechanism was investigated. The results show that the P-wave velocity, density, uniaxial compressive strength (UCS), and tensile strength of weakly cemented sandstones with different grain sizes decrease with the increase of grain size. The medium sandstone and coarse sandstone exhibit ductile failure, while the siltstone and fine sandstone exhibit brittle failure under the two conditions. The distribution of AE signal strength is nearly Gaussian in the time domain. The peak frequency and upper limit of signal strength are negatively correlated with grain size, and the occurrences of lots of high-strength AE signals can be used as the precursor of sandstone failure. The damage evolution shows the trend of low-speed damage-accelerated damage-low-speed damage, and the damage increase at the peak load is negatively related to the grain size. The microfailure mechanism is the tension-shear mixed failure, which is dominated by tensile failure, with few shear failures. The proportion of shear cracks is positively correlated with the grain size under uniaxial compression, while there is no significant correlation between shear cracks and grain size under Brazilian splitting.

Experimental Investigation on Acoustic Emission Characteristics of Reinforced Concrete Beam Strengthened with CFRP

Reinforced concrete (RC) structures are generally subjected to increasing loads, age-related deterioration and design changes that affect the integrity of structures. Therefore, this paper presents an experimental investigation of a carbon fibre reinforced polymer (CFRP) reinforced RC beam under three-point loading. Acoustic emission (AE) was used to monitor the progression of damage. The loading, AE intensity and AE signal strength were analysed and discussed. The analyses were performed using the AE signal collected from CH6 and CH7. The result is that crack nucleation produces high signal intensity compared to other crack modes. Moreover, the intensity plots fit the identified crack modes when the beams were reinforced with CFRP. Therefore, AE can be used to evaluate the performance of the CFRP reinforced RC beam.

Crack monitoring and damage assessment of BFRP-jacketed concrete cylinders under compression load based on acoustic emission techniques

Fiber reinforced polymer (FRP) composites have been widely used in strengthening of existing concrete columns. However, the concrete cracks inside the FRP-strengthened columns caused by external loading cannot be observed by traditional field inspection techniques due to the coverage of FRP composites. In the present study, axial compression loading tests were performed on two unjacketed concrete and six BFRP-jacketed concrete cylinders. Acoustic emission (AE) techniques were utilized to monitor the cracking process of concrete specimens. The conventional AE hit and AE amplitude analysis were effective in detecting the first occurrence of concrete crack. The first noticeable concrete crack and final major crack can be detected by the crack intensity analysis. Then, five stages of cracking process of unjacketed concrete and BFRP-jacketed concrete specimens can be determined by a combination of the AE hit analysis and crack intensity analysis. AE analysis revealed that the cracking process of BFRP-jacketed concrete differed from that of unjacketed concrete, due to the presence of BFRP composites: (1) the time range of AE stable developing stage of BFRP-jacketed concrete was longer than that of the unjacketed concrete and the time range increased with an increase in FRP layers; (2) the crack intensity analysis parameters, which are based on AE signal strength, (i.e., severity index (Sr) and historic index (Hs)) of BFRP-jacketed specimens presented noticeable higher values at the final failure stage; (3) the RA value-average frequency (RA-AF) analysis revealed that the occurrence of shear cracks was delayed in the BFRP-jacketed concrete. The present study demonstrated that AE monitoring can effectively capture the characteristics of the cracking process of FRP-jacketed concrete.

Estimation and Comparison of Electrode Wear and Ae Parameters of Titanium Material in Wire Electric Discharge Machining Using ANN

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. Selection of process parameters for obtaining higher cutting efficiency or accuracy in WEDM is still not fully solved, even with most up-to-date CNC wire EDM machine. It is widely recognised that Acoustic Emission (AE) is gaining ground as a monitoring method for health diagnosis on rotating machinery. The advantage of AE monitoring over vibration monitoring is that the AE monitoring can detect the growth of subsurface cracks whereas the vibration monitoring can detect defects only when they appear on the surface. This study outlines the optimization of titanium material using L16design of experiment. Each experiment has been performed varying the process parameters like pulse-on time, pulse-off time, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Simple functional relationships between the parameters were plotted to arrive at possible information on Electrode Wear (EW) and AE signals. But these simpler methods of analysis did not provide any information about the status of the electrode. Thus, there is a requirement for more sophisticated methods that are capable of integrating information from the multiple sensors. Hence, method like Artificial Neural Network (ANN) has been applied for the estimation of EW, AE signal strength, AE count and AE RMS. The ANN algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into three sets: the training set, validation set and testing set. The training set is used to make the ANN learn the process and the testing set will check the performance of ANN. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 60% and 70%. The best model is selected from the said percentages of data. Estimation of the EW and AE signals parameters by ANN at 70% of data training set showed the best correlation with the measured value.

Evaluation of severe damaged reinforced concrete beam repaired with epoxy injection and retrofitted with CFRP using acoustic emission technique

This paper presents the investigation on severe damaged-RC beams repaired with epoxy injection and retrofitted with carbon fibre reinforced polymer (CFRP) on the soffit subjected to monotonic loading concurrent with acoustic emission (AE) monitoring. Ten beams were prepared namely reference beam (S1C) and damaged RC beam repaired with epoxy injection and strengthened with CFRP (S3B). The load, signal strength, average frequency-RA value and crack modes were analysed and discussed. As a result, the S3B produced highest maximum load and AE signal strength compared to S1C. In a nutshell, the AE can be used to identify the integrity of the damaged-RC beam repaired with epoxy injection and retrofitted with CFRP.

Crack intensity of the damaged RC beam repaired with epoxy injection using acoustic emission technique

This paper presents the intensity crack zones of a damaged reinforced concrete (RC) beam with the size of 200 mm x 300 mm x 1500 mm repaired with an epoxy injection under monotonic loading. The acoustic emission (AE) technique concurrent with three-point loading was used to monitor the behaviour of the beam. The intensity analysis based on AE signal strength was carried out on CH6 and CH7 at each crack mode. It was found that the intensity plots are well matched with the progression of the crack in the beam. It is inferred that the intensity zones can be utilised to characterise the damage in the beam.

Investigation of Bonding Behavior of FRP and Steel Bars in Self-Compacting Concrete Structures Using Acoustic Emission Method.

To extend understanding of the bonding behavior of fiber reinforced polymer (FRP) and steel bars in self-compacting concrete (SCC), an experimental series consisting of 36 direct pull-out tests monitored by acoustic emission (AE) were performed in this paper. The test variables involved rebar type, bar diameter, embedded length, and polypropylene (PP) fiber volume content. For each test, the pull-out force and free end slip were continuously measured and compared with the corresponding AE signals. It was found that the proposed AE method was effective in detecting the debonding process between the FRP/steel bars and the hosting concrete. The AE signal strength exhibited a good correlation with the actual bond stress-slip relationship measured in each specimen. Based on the AE location technique, the invisible non-uniform distribution of bonding stress along the bar was further revealed, the initial location of damage and the debonding process were captured. Additionally, the contribution of bar-to-concrete load-bearing mechanism (chemical adhesion, friction, and mechanical interlocking) to sustain the pull-out force was effectively clarified by studying the collected signals in the frequency domain of AE methods. The experimental results demonstrate that the proposed AE method has potential to detect the debonding damage of FRP/steel bar reinforced SCC structures accurately.

Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique

This paper introduces suitable features and methods to define hazard rate function by acoustic emission (AE) parametric analysis to develop robust damage statement index and reliability analysis. AE signal energy was first examined to find out the relation between damage progress and AE signal energy so that a damage index based on AE signal energy could be proposed to quantify progressive damage imposed to ferrocement composite slabs. Moreover, by using AE signal strength, historic index could be computed and utilized to develop a modified hazard rate function through integration of bathtub curve and Weibull function. Furthermore, to provide a practical scheme for real condition monitoring, support vector regression was utilized to produce a robust tools for failure prediction considering uncertainties exist in real structures.

Acoustic emission-based analysis of bond behavior of corroded reinforcement in existing concrete structures

Summary This experimental study exploits acoustic emission (AE) monitoring to assess the bond behavior of corroded bars in reinforced concrete prism samples under pull-out tests. The analysis of AE parameters obtained in this study including amplitude, duration, cumulative number of hits, and signal strength was performed to identify different stages of bond degradation. The results of bond behavior and free end slip were analyzed and compared with the corresponding AE data. An intensity analysis of AE signal strength was also completed to quantify the bond damage using two additional AE parameters: historic index (H (t)) and severity (Sr). The outcomes of AE analysis enabled the detection of early stages of bond deterioration (micro-cracking) as well as identification of the macro-cracking stage prior to the occurrence of bond splitting failure. The results also showed a good correlation between AE cumulative number of hits and AE signal strength parameters with the steel-to-concrete bond degradation because of corrosion. Finally, the results of H (t) and Sr were employed to generate intensity classification charts to characterize the stages of micro- and macro-cracking and range of slip of corroded reinforcement. Copyright © 2016 John Wiley & Sons, Ltd.

Assessment of concrete-to-steel bond behaviour of reinforced concrete structures using acoustic emission intensity analysis

Abstract. An experimental study was performed to utilize acoustic emission (AE) intensity analysis for the assessment of the concrete-to-steel bond behaviour of reinforced concrete structures. A total of 18 reinforced concrete unconfined prism samples were tested in a direct pullout test setup under incrementally increasing monotonic loading as being constantly monitored with attached AE sensors. The samples were cast using variable bar diameter (10, 20, 35 mm) and bar embedded length (50, 100, 200 mm). Different AE signals parameters were recorded throughout the tests until failure including rise time, counts, number of hits, signal strength, energy, amplitude, duration, and frequency values. Moreover, an AE intensity analysis was applied on AE signal strength results to produce two additional AE parameters: historic index (H (t)) and severity (Sr). Results demonstrated that cumulative signal strength (CSS) correlated well with different degrees of loss of bond from micro-cracking till bond splitting failure, which resulted in cover cracking or delamination. The review of CSS, H (t), and Sr curves allowed the detection of two progressive stages of bond deterioration (micro-cracking and macro-cracking) in all tested specimens. Intensity analysis parameters (H (t) and Sr) were employed to create bond damage classification chart to evaluate the concrete-to-steel bond condition in reinforced concrete structures.

Evaluation of damage progression in concrete structures due to reinforcing steel corrosion using acoustic emission monitoring

Acoustic emission (AE) monitoring was employed to detect and identify the extent of degradation resulting from reinforcing steel corrosion in concrete structures. Reinforced concrete samples were subjected to an accelerated corrosion test and corroded until they reached five different levels of mass loss of steel: 1, 2, 3, 4, and 5 %. The corrosion activity of all samples was continuously monitored using attached piezoelectric AE sensors and a multichannel data acquisition system. The results obtained from AE monitoring were analysed and compared to those obtained from half-cell potential (HCP) measurements, the amount of anodic current passing with time, and concrete cover cracking. The study analysed AE signal strength parameters including cumulative signal strength, historic index [H(t)], and severity (Sr) to detect the corrosion initiation, and subsequently, to classify the extent of damage following the corrosion progression. The study also aimed to correlate these parameters to both the degree of mass loss and concrete cover crack width. The results showed that the AE signal strength parameters can be exploited to characterize different degrees of damage from corrosion initiation and up to 5 % steel mass loss. These parameters can be analysed to detect the corrosion initiation earlier than the HCP test. This study also presented damage classification charts that can be used to correlate the level of steel mass loss and/or crack width to the AE signal strength parameters. Given these points, the outcomes of this investigation are substantial for advancing the application of acoustic emission for structural health monitoring in concrete structures.

Acoustic emission monitoring of early corrosion in prestressed concrete piles

SUMMARY Prestressed concrete (PC) piles in marine environments are vulnerable to corrosion damage especially at their tidal zone because of the concurrent exposure of steel reinforcement to moisture, chlorides, and oxygen. Early detection of corrosion in prestressed strands is desirable to enable the effective planning and prioritizing of maintenance operations. This paper reports on experiments aimed at testing the hypothesis that acoustic emission (AE) monitoring is suitable to recognize and classify early corrosion damage in PC piles exposed to saltwater. In fact, AE is sensitive to the formation and growth of microcracks in both steel and concrete, which may develop upon depassivation of reinforcement and buildup of corrosion by-products. In addition, the influence of AE signals produced through flexural cracks in the concrete is of less concern for PC piles as such cracks are seldom produced under service loads. Five specimens representative of full-scale portions of PC piles were designed to accelerate corrosion and subjected for 1 year to wet/dry cycles in saltwater simulating tidal action. The specimens were monitored continuously with AE sensors. Benchmark electrochemical measurements were routinely performed to understand when depassivation of the steel strands occurred. Visual evidence of early corrosion was collected from strands removed from two decommissioned specimens. It is shown that AE intensity analysis is more effective than AE signal strength and cumulative signal strength analysis in recognizing corrosion damage. Intensity analysis-based criteria for the assessment of early corrosion damage are proposed, complementing previously developed criteria for more severe corrosion damage in steel strands. Copyright © 2014 John Wiley & Sons, Ltd. Received 22 May 2014; Revised 15 September 2014; Accepted 2 November 2014

Diagnostic of fatigue damage severity on reinforced concrete beam using acoustic emission technique

Fatigue damage assessment using non-destructive testing on structures as well as reinforced concrete (RC) beam has become an attention for recent decades. In this paper, diagnostic of fatigue damage in RC beams using acoustic emission (AE) technique was investigated. Based on severity analysis of AE signal strength during service life of the beams, bath–tub curves derived from AE signal are presented and divided into three stages; burn-in, steady state and burn-out. At the same time, deflection in the RC beams also has been analyzed.

Intensity Analysis Method for Measurement the Damage Severity of Concrete Structure by Utilizing the Acoustic Emission Technique

Evaluation methods such as acoustic emission (AE) are required for assessing the deterioration on concrete structures. This paper gives a brief on the evaluations of the acoustic emission to the health monitoring of reinforced concrete structure beam. Small scale size beam have been used for this investigation and the AE signal processing are the main principal data in this work for assessing by using the statistical technique, which is known as intensity analysis method (IA). This technique is capable to quantify and evaluate the damage severity on concrete structures. Eventually, by using the AE signal strength data, the results indicates are greater instruments in determining the damage mechanism level on concretes structure.

Health Index Evaluation on Acoustic Emission Signal for Concrete Structure by Intensity Analysis Method

Concrete structures have been facing several types of damage mechanisms during their lifetime. Mostly, the types are commonly defects in concretes structures are crack formations. Therefore, evaluation methods such as acoustic emission (AE) are required for assessing the deterioration on concrete structures. This paper gives a brief on the evaluations of the acoustic emission signal strength to the health monitoring of reinforced concrete structure beam. Small scale size beam have been used for this investigation and the AE signal processing are the main principal data in this work for assessing by using the statistical quantitative technique, which is known as intensity analysis (IA).This type of technique is able to quantify and evaluate the damage severity on concrete structures. Eventually, by using the AE signal strength data, the results indicates are greater instruments in determining the damage mechanism level on concretes structure.