Signal processing techniques for filtering acoustic emission data in prestressed concrete

Abdelrahman, Marwa; ElBatanouny, Mohamed K.; Rose, John R.; Ziehl, Paul

doi:10.1080/09349847.2018.1426800

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Applying parameter-based filters is simple by using data acquisition software. Signals with extremely short durations can be discarded using duration filters, whereas electric noise with significantly high signal strengths can be filtered using a signal strength filter [18]. Some researchers also define frequency filters to eliminate noise based on very low-frequency signals [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…It is also known as the duration-amplitude (D-A) filter and has been adopted in many experimental studies based on amplitude-duration ranges for use as rejection limits considered by [22,23]. Swansong II filter has produced promising results in a variety of studies for filtering AE data gathered from fatigue crack growth, localized corrosion and degradation in concrete, and cracking of concrete during load tests [18,[24][25][26]. Additionally, it was observed that these fundamental filters considerably improved the source location results of concrete cracking and demonstrated good agreement with visually seen cracks.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…In the common practice of AE, the primary waves known as P-waves are the first measure of the signal arrival at the sensor, utilized to determine the location of the damage. However, recorded AE signals are commonly clouded by spurious events or noise, which leads to misinterpretation and inaccurate evaluation [4]. Environmental noise (electrical effects) and internal noise (friction and reflection) are the two main sources of AE noise.…”

Section: Introductionmentioning

confidence: 99%

“…Abdelrahman et.al. investigated the use of wavelet analysis of AE signals to develop empirical-based algorithms to differentiate between clean and degraded signals in the time-frequency domain [4]. The focus was to develop a robust filtering technique for the acoustic emission dataset.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission signal filtering based on artificial intelligence

Inderyas¹,

Tayfur²,

Alver³

2022

eJNDT

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One of the factors affecting the performance of the acoustic emission method used for real-time monitoring of structures is the interference of noise signals with the damage-related AE signals. Since the signals detected from the sensors are analyzed to obtain damage information, noise-related signals cause inaccurate inspection results. Especially, in the conditions where electrical or mechanical friction is prominent, noise causes big data and errors in damage detection In this study, to solve this problem, it is aimed to develop an AE signal filtering model by artificial intelligence that can filter the noise-related signals. For this purpose, a wide range of AE data were filtered using conventional filtering techniques and noise- and damage-related signals were used to train artificial intelligence. The noise-related signals were successfully separated from the damage-related signals.

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Experimental study on compressive mechanical properties of underwater non-dispersible polymer with AE

Shi,

Tang,

Qin

et al. 2024

Construction and Building Materials

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Signal processing techniques for filtering acoustic emission data in prestressed concrete

Cited by 8 publications

References 33 publications

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

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

Acoustic emission signal filtering based on artificial intelligence

Experimental study on compressive mechanical properties of underwater non-dispersible polymer with AE

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