Mechanisms of Diagonal-Shear Failure in Reinforced Concrete Beams analyzed by AE-SiGMA

Ohno, Kentaro; Shimozono, Shinichiro; Sawada, Yosuke; Ohtsu, Masayasu

doi:10.1299/jmmp.2.462

Cited by 18 publications

(4 citation statements)

References 2 publications

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“…According to the elastic wave dynamics theory, under the action of crack propagation, the displacement generated at time t at position x can be expressed as [19] :…”

Section: Moment Tensor Inversion Analysis Methodsmentioning

confidence: 99%

Experimental Study on Source Failure Mechanism and Acoustic Emission Characteristics of Red Sandstone Under Uniaxial Compression

Zhu

Song

et al. 2021

Preprint

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In order to study the rock fracture mechanism and precursor characteristics, uniaxial compression experiments of red sandstone were carried out. Using acoustic emission technology and digital speckle correlation method as experimental observation means, the evolution characteristics of deformation field and acoustic emission index during rock deformation were studied. The results show that : (1) The deformation concentration of rock deformation localization zone is the main cause of nonlinear evolution of rock stress-strain curve. (2) The volume parameters of different types of cracks in rock acoustic emission change with the relative displacement rate and dislocation rate of deformation localization zone. (3) In terms of failure types, there are more high-frequency components of tensile fracture main frequency, more low-frequency components of shear fracture main frequency, and wider distribution of mixed fracture main frequency. In the time sequence, the spectrum distribution of acoustic emission signals is wide and the amplitude is small at the sudden change time. At the sudden change time, the spectrum distribution of acoustic emission signals becomes narrow, the amplitude increases, and the spectrum distribution of peak points is greatly narrowed. Therefore, it is considered that the spectrum distribution is greatly narrowed can be used as an early warning precursor.

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“…According to the elastic wave dynamics theory, under the action of crack propagation, the displacement generated at time t at position x can be expressed as [19] :…”

Section: Moment Tensor Inversion Analysis Methodsmentioning

confidence: 99%

Experimental Study on Source Failure Mechanism and Acoustic Emission Characteristics of Red Sandstone Under Uniaxial Compression

Zhu

Song

et al. 2021

Preprint

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“…In addition, AE parameter analysis and AE-SiGMA analysis can be used to classify cracks [10,11]. Using AE-SiGMA analysis, the identification of crack kinematics of reinforced concrete (RC) elements in terms of the crack type, orientation and locations of cracks has been effectively identified [11][12][13][14]. The abovementioned studies have been proven well in the detection and evaluation of damage; however, due to the anisotropic properties of concrete or composite materials, it becomes difficult to identify the sources of AE signals due to spurious events or noises while detecting damage.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Inderyas,

Alver,

Tayfur

et al. 2024

Arab J Sci Eng

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Acoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

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“…Various techniques are available in analyzing AE data in terms of count and count rates such as spectral analysis, amplitude distribution analysis, autocorrelation function and root mean square (RMS) signal analysis [16,17]. One of the most promising and efficient methods in data analyzing in acoustic emission by means of crack characterization is AE-SiGMA (Acoustic Emission-Simplified Green's Functions for Moment Tensor Analysis) [12][13][14][15][18][19][20][21]. AE-SiGMA is applied to identify AE sources in order to classify micro-crack formations into tensile, shear and mixed mode.…”

Section: Introductionmentioning

confidence: 99%

“…AE-SiGMA is applied to identify AE sources in order to classify micro-crack formations into tensile, shear and mixed mode. By AE-SiGMA, quantitative crack evaluation has been made in reinforced concrete structures by extensive research [18][19][20][21][22] and the success is proved when the experimental results are examined. Therefore, AE has been successfully applied to provide any information about structural systems in the last decade [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Study of fracture evolution in FRP-strengthened reinforced concrete beam under cyclic load by acoustic emission technique: An integrated mechanical-acoustic energy approach

Selman

Ghiami

Alver

2015

Construction and Building Materials

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Mechanisms of Diagonal-Shear Failure in Reinforced Concrete Beams analyzed by AE-SiGMA

Cited by 18 publications

References 2 publications

Experimental Study on Source Failure Mechanism and Acoustic Emission Characteristics of Red Sandstone Under Uniaxial Compression

Experimental Study on Source Failure Mechanism and Acoustic Emission Characteristics of Red Sandstone Under Uniaxial Compression

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Study of fracture evolution in FRP-strengthened reinforced concrete beam under cyclic load by acoustic emission technique: An integrated mechanical-acoustic energy approach

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