Applying data augmentation technique on blast-induced overbreak prediction : resolving the problem of data shortage and data imbalance
Blast-induced overbreak in tunnels can cause severe damage and has therefore been a main concern in tunnel blasting. Researchers have developed many machine learning-based models to predict overbreak. Collecting overbreak data manually, however, can be challenging and might obtain insufficient or...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier Ltd.
2023
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/44854/2/Applying%20data%20augmentation%20-%20Copy.pdf http://ir.unimas.my/id/eprint/44854/ https://www.sciencedirect.com/science/article/pii/S0957417423021188 https://doi.org/10.1016/j.eswa.2023.121616 |
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| Summary: | Blast-induced overbreak in tunnels can cause severe damage and has therefore been a main concern in tunnel
blasting. Researchers have developed many machine learning-based models to predict overbreak. Collecting
overbreak data manually, however, can be challenging and might obtain insufficient or poorly structured data.
Thus, this study aims to utilise a deep generative model, namely the Conditional Tabular Generative Adversarial
Network (CTGAN), to establish an acceptable dataset for overbreak prediction. The CTGAN model was applied to
overbreak data collected from paired tunnels: a left-line tunnel and a right-line tunnel. The overbreak dataset
collected from the left-line tunnel—nominated as the true dataset—served to train the CTGAN model. Then the
well-trained CTGAN model generated a synthetic overbreak dataset. Statistical-based approaches verified the
similarity between the true and synthetic datasets; machine learning-based approaches verified the feasibility of
using the synthetic dataset to train overbreak prediction model. Lastly, this study clarified how to resolve the
problem of data shortage and data imbalance by leveraging the CTGAN model. The results evidence that the
CTGAN model can effectively generate a high-quality synthetic overbreak dataset. The synthetic overbreak
dataset not only greatly retains the properties of the true dataset but also effectively enhances its diversity. The
way, integrating the true and synthetic overbreak datasets, can dramatically resolve the problem of data shortage
and data imbalance in overbreak prediction. The findings in this study, therefore, highlight it as a promising
perspective to resolve such a particular engineering problem. |
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