Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid ...Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.展开更多
Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environ...Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.展开更多
文摘Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.
文摘由于交通事故是小概率随机事件,难以在全时空域上开展交通安全分析,也无法基于此制定事故发生前的交通安全风险主动防控策略。为辨识混杂因素干扰下安全风险及其诱发本质,使用激进驾驶行为数据与速度变异系数计算交通秩序指数(traffic order index,TOI),形成事故替代指标,并通过K-means聚类算法将TOI划分为3种交通安全风险等级。在此基础上,利用Catboost算法构建交通流特征、天气条件、道路条件等因素与交通安全风险等级间的关联关系,并基于基尼系数的特征重要性确定高速公路交通安全风险要素。使用部分依赖图算法解析风险要素与交通安全风险的依赖关系,获取风险要素对交通安全风险的边际效应。结果表明:(1)Catboost算法对风险等级识别的准确率、精确率、召回率依次为85.95%、88.56%、86.75%,证明交通秩序指数与外部风险要素具有较强相关性;(2)交通流量、拥堵指数对风险识别有较大影响,且与交通安全风险等级呈现非线性关系,交通流量>450 veh/h或拥堵指数>1.5时,交通安全风险均会显著增长,交通安全风险分别上升16.9%、29.5%;(3)当连续1 km道路内设有1~2个交通标志时,交通安全风险最高,路段识别为高风险的概率为38.1%;匝道出入口和隧道内部道路的交通安全风险最高;(4)侧风作用会小幅度影响高速公路交通安全风险,当风力等级由0级增至5级时,交通安全风险上升4.99%。
文摘Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.
