Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

脊骨梁桥——城市立交的新桥型

结合中新苏州工业园区互通式立体交叉匝道跨线桥施工图设计实践，简要介绍了国外城市立交桥中应用较为广泛的一种新桥型——脊骨梁桥的构造特点。

A New Species of the Genus Neurotettix Matsumura (Homoptera:Cicadellidae: Deltocephalinae) from China

A new leafhopper species, Neurotettix flangenus , sp. nov., is described from Hunan Province of China. A key to all the three species of Neurotettix is provi ded. The type specimens are deposited in the Entomological Museum of the Northwest Science & Technology University of Agriculture & Forestry.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output （I/O） paraUelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.

Parameters inversion of high central core rockfill dams based on a novel genetic algorithm

Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.