Acoustic emission-based damage analysis of steel fibre reinforced concrete in progressive cyclic uniaxial tension tests

Vandecruys, Eline; Vrijdaghs, Rutger; Verstrynge, Els

doi:10.1016/j.conbuildmat.2021.126254

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…Elastic waves will be sent out and received by the acoustic emission monitoring system during the destruction of coal samples, and then, the internal destruction characteristics of the coal samples during the destruction process will be inverted [27,28]. Te representative impact rate among acoustic emission parameters is selected as the analysis parameter to analyze the failure characteristics of coal samples.…”

Section: Acoustic Emission Characteristics Of Coal Samplesmentioning

confidence: 99%

Experimental Investigation on Damage Characteristics and Early Warning of Frozen Coal under Uniaxial Compression

Sun

Liu

Dong

et al. 2023

Advances in Materials Science and Engineering

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This article conducted acoustic emission tests of natural coal, natural frozen coal, and saturated frozen coal to explore the damage characteristics of frozen coal and in view of the frozen coal damage early warning problems and explored the strength deformation and acoustic emission characteristics of coal samples. The test results showed that the compressive strength of the frozen coal samples decreased significantly, and the strength of the saturated frozen coal samples was the lowest; more acoustic emissions were generated at the initial loading stage. The damage parameters were defined using the acoustic emission parameters. The damage characteristics of the coal samples were analyzed. The damage model of the coal samples was established based on the power function. The early warning parameter VAEi to analyze the evolution characteristics of the variance curve was proposed; the sudden jump point b of VAEi from the stable development stage to the rapid growth stage was defined as a general early warning point; the endpoint point c of the parameter rapid growth stage was defined as a key early warning point; a zonal and hierarchical early warning was finally proposed.

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Section: Acoustic Emission Characteristics Of Coal Samplesmentioning

confidence: 99%

Experimental Investigation on Damage Characteristics and Early Warning of Frozen Coal under Uniaxial Compression

Sun

Liu

Dong

et al. 2023

Advances in Materials Science and Engineering

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“…The AE technique was also used as a passive nondestructive tool to detect the damage progress in short glass fiber-reinforced composite panels [13]. De Smedt et al [14] also used AE damage monitoring to better understand the (cyclic) uniaxial tensile behavior of steel fiber-reinforced concretes. Livadiotis et al [15] monitored the corrosion of steel pipelines using the AE technique.…”

Section: Introductionmentioning

confidence: 99%

Study on Acoustic Emission Characteristics and Damage Evolution Law of Shale under Uniaxial Compression

Wu,

Chan,

et al. 2024

Advances in Civil Engineering

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Investigating the correlation between acoustic emission (AE) parameters and damage mechanisms in rock mechanics can help understand rock damage evolution under loading and provide a theoretical basis for engineering support and safety detection. Therefore, this paper presents experimental works on the correlation between AE and failure mechanisms of rock mass under uniaxial compression stress, with the aim of capturing the damage evolution leading to a new damage constitutive model. The experimental results indicate that the uniaxial compression process of shale can be divided into four stages according to AE characteristics. AE signals are minimal during the crack compaction and elastic stages. The crack initiation strength σci, which is approximately 55% of the uniaxial compressive strength, is identified when the cumulative AE counts and damage factor begin to increase slowly. When axial stress reaches the damage strength σcd, which is approximately 80% of the uniaxial compressive strength, a significant number of AE signals are generated. AE phenomena can be observed during the unstable crack development and post-crack stages. Considering the initial damage to the rock, the damage factor D initially decreases and then increases with increasing cumulative ring-down counts rather than exhibiting a monotonic increase. The damage factor D is proportional to the cumulative AE counts N in the stage before rock failure.

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“…SHM methods can be adapted to structures for identifying, locating, and sizing defects. Typical SHM methods applied to concrete structures are vibration [6][7][8], strain monitoring [9][10][11][12][13], and the acoustic emission (AE) method [14][15][16][17][18][19]. The AE method is a passive nondestructive evaluation method that detects elastic waves released from active defects.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning

Zhang,

Mahdi,

Issa

et al. 2023

Sensors

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Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.

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Acoustic emission-based damage analysis of steel fibre reinforced concrete in progressive cyclic uniaxial tension tests

Cited by 14 publications

References 35 publications

Experimental Investigation on Damage Characteristics and Early Warning of Frozen Coal under Uniaxial Compression

Experimental Investigation on Damage Characteristics and Early Warning of Frozen Coal under Uniaxial Compression

Study on Acoustic Emission Characteristics and Damage Evolution Law of Shale under Uniaxial Compression

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning

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