Deep learning-based key-block classification framework for discontinuous rock slopes
The key-blocks are the main reason accounting for structural failure in discontinuous rock slopes, and automated identification of these block types is critical for evaluating the stability conditions. This paper presents a classification framework to categorize rock blocks based on the principles o...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775522001366 |
| _version_ | 1818507303707475968 |
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| author | Honghu Zhu Mohammad Azarafza Haluk Akgün |
| author_facet | Honghu Zhu Mohammad Azarafza Haluk Akgün |
| author_sort | Honghu Zhu |
| collection | DOAJ |
| description | The key-blocks are the main reason accounting for structural failure in discontinuous rock slopes, and automated identification of these block types is critical for evaluating the stability conditions. This paper presents a classification framework to categorize rock blocks based on the principles of block theory. The deep convolutional neural network (CNN) procedure was utilized to analyze a total of 1240 high-resolution images from 130 slope masses at the South Pars Special Zone, Assalouyeh, Southwest Iran. Based on Goodman's theory, a recognition system has been implemented to classify three types of rock blocks, namely, key blocks, trapped blocks, and stable blocks. The proposed prediction model has been validated with the loss function, root mean square error (RMSE), and mean square error (MSE). As a justification of the model, the support vector machine (SVM), random forest (RF), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli naïve Bayes (BNB), and decision tree (DT) classifiers have been used to evaluate the accuracy, precision, recall, F1-score, and confusion matrix. Accuracy and precision of the proposed model are 0.95 and 0.93, respectively, in comparison with SVM (accuracy = 0.85, precision = 0.85), RF (accuracy = 0.71, precision = 0.71), GNB (accuracy = 0.75, precision = 0.65), MLP (accuracy = 0.88, precision = 0.9), BNB (accuracy = 0.75, precision = 0.69), and DT (accuracy = 0.85, precision = 0.76). In addition, the proposed model reduced the loss function to less than 0.3 and the RMSE and MSE to less than 0.2, which demonstrated a low error rate during processing. |
| first_indexed | 2024-12-10T22:16:40Z |
| format | Article |
| id | doaj.art-82684b6d591d40b7b25ae89f520ccaa3 |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-12-10T22:16:40Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-82684b6d591d40b7b25ae89f520ccaa32022-12-22T01:31:26ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552022-08-0114411311139Deep learning-based key-block classification framework for discontinuous rock slopesHonghu Zhu0Mohammad Azarafza1Haluk Akgün2School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China; Corresponding author.Department of Civil Engineering, Tabriz University, Tabriz, 5166616471, IranDepartment of Geological Engineering, Middle East Technical University, Ankara, 06800, TurkeyThe key-blocks are the main reason accounting for structural failure in discontinuous rock slopes, and automated identification of these block types is critical for evaluating the stability conditions. This paper presents a classification framework to categorize rock blocks based on the principles of block theory. The deep convolutional neural network (CNN) procedure was utilized to analyze a total of 1240 high-resolution images from 130 slope masses at the South Pars Special Zone, Assalouyeh, Southwest Iran. Based on Goodman's theory, a recognition system has been implemented to classify three types of rock blocks, namely, key blocks, trapped blocks, and stable blocks. The proposed prediction model has been validated with the loss function, root mean square error (RMSE), and mean square error (MSE). As a justification of the model, the support vector machine (SVM), random forest (RF), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli naïve Bayes (BNB), and decision tree (DT) classifiers have been used to evaluate the accuracy, precision, recall, F1-score, and confusion matrix. Accuracy and precision of the proposed model are 0.95 and 0.93, respectively, in comparison with SVM (accuracy = 0.85, precision = 0.85), RF (accuracy = 0.71, precision = 0.71), GNB (accuracy = 0.75, precision = 0.65), MLP (accuracy = 0.88, precision = 0.9), BNB (accuracy = 0.75, precision = 0.69), and DT (accuracy = 0.85, precision = 0.76). In addition, the proposed model reduced the loss function to less than 0.3 and the RMSE and MSE to less than 0.2, which demonstrated a low error rate during processing.http://www.sciencedirect.com/science/article/pii/S1674775522001366Block theoryDiscontinuous rock slopeDeep learningConvolutional neural networkImage-based classification |
| spellingShingle | Honghu Zhu Mohammad Azarafza Haluk Akgün Deep learning-based key-block classification framework for discontinuous rock slopes Journal of Rock Mechanics and Geotechnical Engineering Block theory Discontinuous rock slope Deep learning Convolutional neural network Image-based classification |
| title | Deep learning-based key-block classification framework for discontinuous rock slopes |
| title_full | Deep learning-based key-block classification framework for discontinuous rock slopes |
| title_fullStr | Deep learning-based key-block classification framework for discontinuous rock slopes |
| title_full_unstemmed | Deep learning-based key-block classification framework for discontinuous rock slopes |
| title_short | Deep learning-based key-block classification framework for discontinuous rock slopes |
| title_sort | deep learning based key block classification framework for discontinuous rock slopes |
| topic | Block theory Discontinuous rock slope Deep learning Convolutional neural network Image-based classification |
| url | http://www.sciencedirect.com/science/article/pii/S1674775522001366 |
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