Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods o...Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.展开更多
Large-diameter drilling method is a prevalent method for preventing and controlling rock burst,and the spacing between the large-diameter drilling hole and anchoring hole is a critical factor influencing the roadway s...Large-diameter drilling method is a prevalent method for preventing and controlling rock burst,and the spacing between the large-diameter drilling hole and anchoring hole is a critical factor influencing the roadway stability and relief effectiveness.In this study,a mechanical model for optimal matching between the large-diameter drilling hole and anchoring hole was established following the principle of synergistic control.The influence of large-diameter drilling hole diameter on the optimal spacing under the synergistic relief effect was investigated by integrating theoretical analysis,numerical simulation,and field practice.The results suggest that the hole spacing achieved a synergistic effect in a certain range when the optimal hole spacing increased linearly with the hole diameter.For instance,when the anchoring hole diameter was 20 mm,an increase in the aperture ratio from 5 to 10 brought about an increase in the optimal spacing from 0.25 m to 0.45 m.Additionally,the vertical stress between the large-diameter drilling hole and anchor hole increased nonlinearly under the condition of constant pore ratio but varying hole spacing.Both excessively small and excessively large hole spacings were detrimental to the pressure relief effect.In the engineering practice,optimizing the hole spacing from 0.55 m to 0.45 m in the 1208 working face contributed to reducing coal body drilling cuttings and the roadway moving quantity by 33%and 19.2%,respectively.This demonstrates that the pressure relief-support reinforcement synergistic effect should be fully considered in optimization design.展开更多
The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))an...The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.展开更多
The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate...The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.展开更多
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
基金supported by the Beijing Natural Science Foundation(Grant No.8252012)the National Natural Science Foundation of China(Grant No.52378475).
文摘Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.
基金Project(52274086)supported by the National Natural Science Foundation of ChinaProject(2024KJH069)supported by the Shandong Provincial Youth Innovation and Technology Support Program,ChinaProject(tstp20221126)supported by the Project of Taishan Scholar in Shandong Province,China。
文摘Large-diameter drilling method is a prevalent method for preventing and controlling rock burst,and the spacing between the large-diameter drilling hole and anchoring hole is a critical factor influencing the roadway stability and relief effectiveness.In this study,a mechanical model for optimal matching between the large-diameter drilling hole and anchoring hole was established following the principle of synergistic control.The influence of large-diameter drilling hole diameter on the optimal spacing under the synergistic relief effect was investigated by integrating theoretical analysis,numerical simulation,and field practice.The results suggest that the hole spacing achieved a synergistic effect in a certain range when the optimal hole spacing increased linearly with the hole diameter.For instance,when the anchoring hole diameter was 20 mm,an increase in the aperture ratio from 5 to 10 brought about an increase in the optimal spacing from 0.25 m to 0.45 m.Additionally,the vertical stress between the large-diameter drilling hole and anchor hole increased nonlinearly under the condition of constant pore ratio but varying hole spacing.Both excessively small and excessively large hole spacings were detrimental to the pressure relief effect.In the engineering practice,optimizing the hole spacing from 0.55 m to 0.45 m in the 1208 working face contributed to reducing coal body drilling cuttings and the roadway moving quantity by 33%and 19.2%,respectively.This demonstrates that the pressure relief-support reinforcement synergistic effect should be fully considered in optimization design.
文摘The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.
基金the National Natural Science Foundation of China(Grant No.42272312)Ningbo Youth Science and Technology Innovation Talent Project(Grant No.2024QL057)the Zhejiang Provincial Xinmiao Talents Program(Grant No.2024R405B093).
文摘The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.
