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A platform for research: civil engineering, architecture and urbanism
A semi-Naïve Bayesian rock burst intensity prediction model based on average one-dependent estimator and incremental learning
https://orcid.org/0000-0002-4646-8253
Abstract Rock burst is one of the common disasters in the field of engineering. The traditional rock burst prediction model assumes that the data distribution is fixed or stationary and that the training samples are independent and identically distributed. The model is prone to catastrophic forgetting problems during use. In this study, a Bayesian model with incremental learning (IL) properties based on the Averaged One-Dependent Estimator (AODE) is proposed. The Tangential stress (σθ), Uniaxial tensile strength (σt), Uniaxial compressive strength (σc), Stress coefficient (σθ/σc), Brittleness coefficient (σc/σt), and elastic energy index (Wet) are taken as the study parameters. A total of 382 sets of rock burst data were used as research parameters for training and testing, including the elastic energy index Wet; Evaluate the overall performance of the model using an 8-fold cross-validation method. Finally, compare the model with intelligent algorithms such as naïve Bayes (NB), k-nearest neighbor (KNN), Artificial Neutral Network (ANN), quadratic discriminant analysis (QDA), and single indicator prediction methods. The results show that the prediction accuracy of the model reaches 92.9%, and it has excellent stability, applicability, and generalization ability. Compared with the two data inputs, the accuracy of the incremental learning attribute model improved by 10.8%. Compared with non-incremental learning models, the prediction accuracy of incremental learning models has increased by 6.4% and 13.9%, respectively. Compared with other rock burst prediction models, this model has significantly better prediction performance than other models. The model was applied to a tunnel on the CZ railway for engineering verification, and the accuracy of the prediction results reached 100%, proving that the model can continuously learn while ensuring high prediction accuracy.
A semi-Naïve Bayesian rock burst intensity prediction model based on average one-dependent estimator and incremental learning
https://orcid.org/0000-0002-4646-8253
Abstract Rock burst is one of the common disasters in the field of engineering. The traditional rock burst prediction model assumes that the data distribution is fixed or stationary and that the training samples are independent and identically distributed. The model is prone to catastrophic forgetting problems during use. In this study, a Bayesian model with incremental learning (IL) properties based on the Averaged One-Dependent Estimator (AODE) is proposed. The Tangential stress (σθ), Uniaxial tensile strength (σt), Uniaxial compressive strength (σc), Stress coefficient (σθ/σc), Brittleness coefficient (σc/σt), and elastic energy index (Wet) are taken as the study parameters. A total of 382 sets of rock burst data were used as research parameters for training and testing, including the elastic energy index Wet; Evaluate the overall performance of the model using an 8-fold cross-validation method. Finally, compare the model with intelligent algorithms such as naïve Bayes (NB), k-nearest neighbor (KNN), Artificial Neutral Network (ANN), quadratic discriminant analysis (QDA), and single indicator prediction methods. The results show that the prediction accuracy of the model reaches 92.9%, and it has excellent stability, applicability, and generalization ability. Compared with the two data inputs, the accuracy of the incremental learning attribute model improved by 10.8%. Compared with non-incremental learning models, the prediction accuracy of incremental learning models has increased by 6.4% and 13.9%, respectively. Compared with other rock burst prediction models, this model has significantly better prediction performance than other models. The model was applied to a tunnel on the CZ railway for engineering verification, and the accuracy of the prediction results reached 100%, proving that the model can continuously learn while ensuring high prediction accuracy.
A semi-Naïve Bayesian rock burst intensity prediction model based on average one-dependent estimator and incremental learning
https://orcid.org/0000-0002-4646-8253
Abstract Rock burst is one of the common disasters in the field of engineering. The traditional rock burst prediction model assumes that the data distribution is fixed or stationary and that the training samples are independent and identically distributed. The model is prone to catastrophic forgetting problems during use. In this study, a Bayesian model with incremental learning (IL) properties based on the Averaged One-Dependent Estimator (AODE) is proposed. The Tangential stress (σθ), Uniaxial tensile strength (σt), Uniaxial compressive strength (σc), Stress coefficient (σθ/σc), Brittleness coefficient (σc/σt), and elastic energy index (Wet) are taken as the study parameters. A total of 382 sets of rock burst data were used as research parameters for training and testing, including the elastic energy index Wet; Evaluate the overall performance of the model using an 8-fold cross-validation method. Finally, compare the model with intelligent algorithms such as naïve Bayes (NB), k-nearest neighbor (KNN), Artificial Neutral Network (ANN), quadratic discriminant analysis (QDA), and single indicator prediction methods. The results show that the prediction accuracy of the model reaches 92.9%, and it has excellent stability, applicability, and generalization ability. Compared with the two data inputs, the accuracy of the incremental learning attribute model improved by 10.8%. Compared with non-incremental learning models, the prediction accuracy of incremental learning models has increased by 6.4% and 13.9%, respectively. Compared with other rock burst prediction models, this model has significantly better prediction performance than other models. The model was applied to a tunnel on the CZ railway for engineering verification, and the accuracy of the prediction results reached 100%, proving that the model can continuously learn while ensuring high prediction accuracy.
A semi-Naïve Bayesian rock burst intensity prediction model based on average one-dependent estimator and incremental learning
Zhang, Qinghe (author) / Zheng, Tianle (author) / Yuan, Liang (author) / Li, Xue (author) / Li, Weiguo (author) / Wang, Xiaorui (author)
2024-02-14
Article (Journal)
Electronic Resource
English
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