Recognition of shear and tension signals based on acoustic emission parameters and waveform using machine learning methods

F Ren, C Zhu, Z Yuan, M Karakus, S Tang… - International Journal of …, 2023 - Elsevier
Acoustic emission (AE) technology is widely used to monitor the damage evolution of rock.
Identifying AE signals is crucial to reveal the rock cracking mechanism. The current tension …

Review on acoustic emission source location, damage recognition and lifetime prediction of fiber-reinforced composites

W Zhou, Z Pan, J Wang, S Qiao, L Ma, J Liu… - Journal of Materials …, 2023 - Springer
Acoustic emission technology is an effective nondestructive testing method for fiber-
reinforced composites, which can monitor the damage process in real time. The main …

A waveform-based clustering and machine learning method for damage mode identification in CFRP laminates

J Wang, W Zhou, X Ren, M Su, J Liu - Composite Structures, 2023 - Elsevier
To gain an insight into the damage mechanism in carbon fiber reinforced polymer, a real-
time analytical approach for damage mode identification of composite based on machine …

State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic …

S Gholizadeh, Z Leman, B Baharudin - Ultrasonics, 2023 - Elsevier
Fatigue strength is one of the most important properties of composite materials because it
directly relates to their lifespan. Acoustic emission (AE) is a passive structural health …

Identifying the types of loading mode for rock fracture via convolutional neural networks

Z Song, Z Zhang, G Zhang, J Huang… - Journal of geophysical …, 2022 - Wiley Online Library
For decades, monitoring and identification of the dynamic stress states of rock masses in
reservoirs have been challenging tasks owing to the lack of effective observation and …

Shield tunneling parameters matching based on support vector machine and improved particle swarm optimization

S Sun - Scientific Programming, 2022 - Wiley Online Library
Of late, emerging algorithms such as machine learning have been increasingly used in
shield tunneling construction management and control. This research article proposes a …

Towards Long-Term Monitoring of the Structural Health of Deep Rock Tunnels with Remote Sensing Techniques

W Frenelus, H Peng - Frattura ed Integrità Strutturale, 2023 - fracturae.com
Due to the substantial need to continuously ensure safe excavations and sustainable
operation of deep engineering structures, structural health monitoring based on remote …

Influence of acoustic emission sequence length on intelligent identification accuracy of 3-D loaded rock's fracture stage

Z Song, J Huang, B Deng, M Li, Q Li, Q Liang… - Engineering Failure …, 2024 - Elsevier
Accurate prediction of impending disasters in underground projects is crucial and requires
the identification of rock fracture stages. Currently, rock fractures are commonly analyzed …

Characterization of acoustic emissions from analogue rocks using sparse regression‐DMDC

C Fieseler, CA Mitchell… - Journal of Geophysical …, 2022 - Wiley Online Library
Moisture loss in rock is known to generate acoustic emissions (AE). Phenomena that result
in AE during drying are related to the movement of fluids through the pores and induced …

Crack pattern identification in cementitious materials based on acoustic emission and machine learning

X Wang, Q Yue, X Liu - Journal of Building Engineering, 2024 - Elsevier
The cracking patterns in cementitious materials before failure are closely related to acoustic
emission (AE) monitoring signals. Traditional rise angle-average frequency analysis …