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 pres...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 highresolution 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.展开更多
Slope stability is one of the most important topics of engineering geology with a background of more than 300 years.So far,various stability assessment techniques have been developed which include a range of simple ev...Slope stability is one of the most important topics of engineering geology with a background of more than 300 years.So far,various stability assessment techniques have been developed which include a range of simple evaluations,planar failure,limit state criteria,limit equilibrium analysis,numerical methods,hybrid and high-order approaches which are implemented in two-dimensional(2D)and three-dimensional(3D)space.In the meantime,limit equilibrium methods due to their simplicity,short analysis time,coupled with probabilistic and statistics functions to estimate the safety factor(F.S),probable slip surface,application on different failure mechanisms,and varied geological conditions has been received special attention from researchers.The presented paper provides a review to limit equilibrium methods used for discontinuous rock slope stability analyses with different failure mechanisms of natural and cut slopes.The article attempted to provide a systematic review for rock slope stability analysis outlook based on limit equilibrium approaches.展开更多
基金support provided by the National Natural Science Foundation of China(Grant No.42077235)the National Key Research and Development Program of China(Grant No.2018YFC1505104).
文摘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 highresolution 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.
文摘Slope stability is one of the most important topics of engineering geology with a background of more than 300 years.So far,various stability assessment techniques have been developed which include a range of simple evaluations,planar failure,limit state criteria,limit equilibrium analysis,numerical methods,hybrid and high-order approaches which are implemented in two-dimensional(2D)and three-dimensional(3D)space.In the meantime,limit equilibrium methods due to their simplicity,short analysis time,coupled with probabilistic and statistics functions to estimate the safety factor(F.S),probable slip surface,application on different failure mechanisms,and varied geological conditions has been received special attention from researchers.The presented paper provides a review to limit equilibrium methods used for discontinuous rock slope stability analyses with different failure mechanisms of natural and cut slopes.The article attempted to provide a systematic review for rock slope stability analysis outlook based on limit equilibrium approaches.
