A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Crack Pattern Recognition Based on Acoustic Emission Waveform Features
Abstract To better identify the crack propagation pattern of coal or rock sample during loading, such as the initiation of new cracks or the propagation of pre-existing cracks. Acoustic emission (AE) characteristics of coal sample were analyzed to illustrate the correlation between AE frequency-domain information and crack propagation pattern. By introducing unsupervised and supervised algorithms to classify AE signals caused by crack pattern in different damage stages, and compare with the experimental damage. The results show that AE peak frequency, centroid frequency and weight frequency can be used to distinguish the initiation of new cracks or the propagation of pre-existing cracks. Fuzzy C-Means (FCM) unsupervised method was used to cluster AE signals generated by specific crack propagation pattern, and the maximum error was 3.8%. In addition, we compared the performance of Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Random Forest (RF) algorithms, the accuracy of RF was higher than ELM, and the running time was significantly 20 times lower than SVM. After expanding the application of RF, it was consistent with the experimental observation. This study contributes to evaluate the crack initiation, propagation and location of engineering structures, such as mine coal pillar and bridge.
Highlights The correlation between acoustic emission waveform features and crack modes is analyzed.A framework is designed to obtain coal fracture types from AE signals based on multiple machine learning methods.Random forest is the most suitable method, which is in good agreement with the experimental results.
Crack Pattern Recognition Based on Acoustic Emission Waveform Features
Abstract To better identify the crack propagation pattern of coal or rock sample during loading, such as the initiation of new cracks or the propagation of pre-existing cracks. Acoustic emission (AE) characteristics of coal sample were analyzed to illustrate the correlation between AE frequency-domain information and crack propagation pattern. By introducing unsupervised and supervised algorithms to classify AE signals caused by crack pattern in different damage stages, and compare with the experimental damage. The results show that AE peak frequency, centroid frequency and weight frequency can be used to distinguish the initiation of new cracks or the propagation of pre-existing cracks. Fuzzy C-Means (FCM) unsupervised method was used to cluster AE signals generated by specific crack propagation pattern, and the maximum error was 3.8%. In addition, we compared the performance of Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Random Forest (RF) algorithms, the accuracy of RF was higher than ELM, and the running time was significantly 20 times lower than SVM. After expanding the application of RF, it was consistent with the experimental observation. This study contributes to evaluate the crack initiation, propagation and location of engineering structures, such as mine coal pillar and bridge.
Highlights The correlation between acoustic emission waveform features and crack modes is analyzed.A framework is designed to obtain coal fracture types from AE signals based on multiple machine learning methods.Random forest is the most suitable method, which is in good agreement with the experimental results.
Crack Pattern Recognition Based on Acoustic Emission Waveform Features
Dai, Jingjing (author) / Liu, Jianfeng (author) / Zhou, Lulin (author) / He, Xin (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB41
Crack Pattern Recognition Based on Acoustic Emission Waveform Features
| Springer Verlag | 2023
Machine learning based crack mode classification from unlabeled acoustic emission waveform features
| British Library Online Contents | 2019
Waveform Analysis Of Acoustic Emission Signals
| British Library Online Contents | 1998