Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wol...Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.展开更多
In order to obtain the optimal parameters of anchor bolt supporting system for large-span and jointed rock mass in Kaiyang Phosphor Mine, it is expensive and unavailable with the method of in-situ experiments. This pa...In order to obtain the optimal parameters of anchor bolt supporting system for large-span and jointed rock mass in Kaiyang Phosphor Mine, it is expensive and unavailable with the method of in-situ experiments. This paper describes a numerical modeling with discrete element method for the supporting effects of different type of anchor bolts. The anchor bolts with variant length of 0.5m, 0.8m, 1.0m, diameter of 10mm, 15mm, 20mm, setting spacing of 3.0m, 2.5m, 2.0m, and setting angle of 10°, 20°, 30°, are simulated respectively. The results show that there exist optimal parameters of anchor bolt support for large-span and jointed rock mass. For the bolt support of the concerning, the optimal length is 2.53.5m, the diameter is 2535mm, the spacing is 0.50.6m, and the setting angle is 105°.展开更多
Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of ...Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.展开更多
基金We acknowledge the funding support from the National Natural Science Foundation of China(Grant No.51808462)the Natural Science Foundation Project of Sichuan Province,China(Grant No.2023NSFSC0346)the Science and Technology Project of Inner Mongolia Transportation Department,China(Grant No.NJ-2022-14).
文摘Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.
文摘In order to obtain the optimal parameters of anchor bolt supporting system for large-span and jointed rock mass in Kaiyang Phosphor Mine, it is expensive and unavailable with the method of in-situ experiments. This paper describes a numerical modeling with discrete element method for the supporting effects of different type of anchor bolts. The anchor bolts with variant length of 0.5m, 0.8m, 1.0m, diameter of 10mm, 15mm, 20mm, setting spacing of 3.0m, 2.5m, 2.0m, and setting angle of 10°, 20°, 30°, are simulated respectively. The results show that there exist optimal parameters of anchor bolt support for large-span and jointed rock mass. For the bolt support of the concerning, the optimal length is 2.53.5m, the diameter is 2535mm, the spacing is 0.50.6m, and the setting angle is 105°.
文摘Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.