Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning
Abstract The propagation of rock fractures is essential for maintaining engineering safety, yet traditional theoretical methods are burdened by challenges such as complex sample collection and lengthy prediction processes. To address these challenges, this study develops a deep learning model based...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06661-7 |
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| _version_ | 1850040197714018304 |
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| author | Runze Liu Ziwei Wang Yanbo Zhang Xulong Yao Shaohong Yan Zhiyuan Chen Shuai Wang Hua Li Qi Wang |
| author_facet | Runze Liu Ziwei Wang Yanbo Zhang Xulong Yao Shaohong Yan Zhiyuan Chen Shuai Wang Hua Li Qi Wang |
| author_sort | Runze Liu |
| collection | DOAJ |
| description | Abstract The propagation of rock fractures is essential for maintaining engineering safety, yet traditional theoretical methods are burdened by challenges such as complex sample collection and lengthy prediction processes. To address these challenges, this study develops a deep learning model based on an adaptive moment estimation optimized convolutional long short-term memory neural network (Adam-ConvLSTM) to predict the evolution of rock fractures. We generated a rock fracture dataset through numerical simulation and then incorporated it into a machine-learning imagework to produce a predictive model. Initially, PFC2D numerical simulations were conducted on rocks with various pore defects under uniaxial compression, resulting in five sets of fracture propagation images. These images were processed using sliding window techniques to construct a foundational dataset. Considering the spatiotemporal correlations among different rock fractures, one dataset was used to train the Adam-ConvLSTM model, yielding an initial model that accurately predicts fracture propagation for a single rock dataset. Utilizing transfer learning, this initial model was adapted and independently fine-tuned for four additional datasets with varying pore defect sizes, resulting in four distinct predictive models. These models were integrated to form a more comprehensive predictive system. In practical applications, the comprehensive model uses structure similarity index measure to align test samples with the most similar images from the model, selecting the predictive model with the most similar images for forecasting fracture evolution. Comparative validation indicates that this comprehensive model outperforms traditional methods and basic deep learning algorithms in both prediction efficiency and accuracy. This model not only enhances the efficiency and precision of rock fracture evolution forecasting but also offers a practical approach for monitoring rock mass fractures, substantially enhancing engineering safety. |
| format | Article |
| id | doaj-art-c4401035eb5c4548bf4c5fb2b2ebd39e |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c4401035eb5c4548bf4c5fb2b2ebd39e2025-08-20T02:56:09ZengSpringerDiscover Applied Sciences3004-92612025-03-017312610.1007/s42452-025-06661-7Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learningRunze Liu0Ziwei Wang1Yanbo Zhang2Xulong Yao3Shaohong Yan4Zhiyuan Chen5Shuai Wang6Hua Li7Qi Wang8College of Science, North China University of Science and TechnologyCollege of Science, North China University of Science and TechnologyCollege of Mining Engineering, North China University of Science and TechnologyCollege of Mining Engineering, North China University of Science and TechnologyCollege of Science, North China University of Science and TechnologyCollege of Artificial Intelligence, North China University of Science and TechnologyCollege of Mining Engineering, North China University of Science and TechnologyHebei Lron and Steel Group Mining Co.,Ltd.Key Laboratory for Geo-Mechanics and Deep Underground Engineering, China University of Mining & TechnologyAbstract The propagation of rock fractures is essential for maintaining engineering safety, yet traditional theoretical methods are burdened by challenges such as complex sample collection and lengthy prediction processes. To address these challenges, this study develops a deep learning model based on an adaptive moment estimation optimized convolutional long short-term memory neural network (Adam-ConvLSTM) to predict the evolution of rock fractures. We generated a rock fracture dataset through numerical simulation and then incorporated it into a machine-learning imagework to produce a predictive model. Initially, PFC2D numerical simulations were conducted on rocks with various pore defects under uniaxial compression, resulting in five sets of fracture propagation images. These images were processed using sliding window techniques to construct a foundational dataset. Considering the spatiotemporal correlations among different rock fractures, one dataset was used to train the Adam-ConvLSTM model, yielding an initial model that accurately predicts fracture propagation for a single rock dataset. Utilizing transfer learning, this initial model was adapted and independently fine-tuned for four additional datasets with varying pore defect sizes, resulting in four distinct predictive models. These models were integrated to form a more comprehensive predictive system. In practical applications, the comprehensive model uses structure similarity index measure to align test samples with the most similar images from the model, selecting the predictive model with the most similar images for forecasting fracture evolution. Comparative validation indicates that this comprehensive model outperforms traditional methods and basic deep learning algorithms in both prediction efficiency and accuracy. This model not only enhances the efficiency and precision of rock fracture evolution forecasting but also offers a practical approach for monitoring rock mass fractures, substantially enhancing engineering safety.https://doi.org/10.1007/s42452-025-06661-7Rock fracture predictionNumerical simulationConvolutional long short-term memory neural networkAdaptive moment estimationTransfer learningStructure similarity index measure |
| spellingShingle | Runze Liu Ziwei Wang Yanbo Zhang Xulong Yao Shaohong Yan Zhiyuan Chen Shuai Wang Hua Li Qi Wang Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning Discover Applied Sciences Rock fracture prediction Numerical simulation Convolutional long short-term memory neural network Adaptive moment estimation Transfer learning Structure similarity index measure |
| title | Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning |
| title_full | Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning |
| title_fullStr | Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning |
| title_full_unstemmed | Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning |
| title_short | Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning |
| title_sort | research on rock fracture evolution prediction model based on adam convlstm and transfer learning |
| topic | Rock fracture prediction Numerical simulation Convolutional long short-term memory neural network Adaptive moment estimation Transfer learning Structure similarity index measure |
| url | https://doi.org/10.1007/s42452-025-06661-7 |
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