Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical appl...Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R2 values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.展开更多
科学准确地模拟分析森林火灾蔓延动态对防灾减灾救灾工作具有重要意义。现有的林火蔓延模拟方法在林火蔓延计算和可视化表达上,耦合程度低且难以将动态物理模型计算结果实时可视化表达。针对这一问题,本文在综合考虑国内外各种火灾蔓延...科学准确地模拟分析森林火灾蔓延动态对防灾减灾救灾工作具有重要意义。现有的林火蔓延模拟方法在林火蔓延计算和可视化表达上,耦合程度低且难以将动态物理模型计算结果实时可视化表达。针对这一问题,本文在综合考虑国内外各种火灾蔓延模型优缺点的基础上,选取应用广泛的Rothermel模型作为物理模型。通过惠更斯理论优化了火灾演进范围边界点割裂的不足,采取着火点密度阈值控制种子点数量与模拟可视化效率的平衡;利用布尔运算提高多着火点蔓延范围计算效率,将火灾模型与Open Scene Graph的粒子系统进行紧密耦合,完成火灾演进可视化表达。本文方法实现了对森林火灾蔓延的精确计算和实时、逼真模拟,为灾害应急部门提供信息化支撑。展开更多
目的基于数据挖掘技术,建立三七叶面积生长预测模型,对于三七整个生长期的精准管理与决策提供参考。方法基于粒子群-随机森林算法,采用2018、2019年4~10月云南省红河自治州泸西县三七种植基地棚内气象因子数据以及三七叶面积生长数据作...目的基于数据挖掘技术,建立三七叶面积生长预测模型,对于三七整个生长期的精准管理与决策提供参考。方法基于粒子群-随机森林算法,采用2018、2019年4~10月云南省红河自治州泸西县三七种植基地棚内气象因子数据以及三七叶面积生长数据作为训练集和测试集构建生长预测模型。结果通过特征工程中皮尔森系数分析可知,三七叶生长与土壤温度、上方水蒸气压和下方水蒸气压等气象因子呈正相关,其中土壤温度正相关程度最大,其皮尔森相关系数在0.75~0.90;下方土壤热通量与三七叶生长呈负相关,其皮尔森相关系数为−0.4~−0.3;通过粒子群优化随机森林算法训练的生长预测模型,其均方根误差(root mean square error,RMSE)收敛时值为0.02182,模型优化后的三七叶生长预测模型决定系数R 2达到0.99997。结论通过多种算法对比实验结果表明,粒子群-随机森林算法构建的三七叶面积生长预测模型具有较高的预测精度。该方法为三七叶的生长预测提供了新的研究思路。展开更多
基金supported by the National Science Foundation of China(42107183).
文摘Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R2 values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.
文摘科学准确地模拟分析森林火灾蔓延动态对防灾减灾救灾工作具有重要意义。现有的林火蔓延模拟方法在林火蔓延计算和可视化表达上,耦合程度低且难以将动态物理模型计算结果实时可视化表达。针对这一问题,本文在综合考虑国内外各种火灾蔓延模型优缺点的基础上,选取应用广泛的Rothermel模型作为物理模型。通过惠更斯理论优化了火灾演进范围边界点割裂的不足,采取着火点密度阈值控制种子点数量与模拟可视化效率的平衡;利用布尔运算提高多着火点蔓延范围计算效率,将火灾模型与Open Scene Graph的粒子系统进行紧密耦合,完成火灾演进可视化表达。本文方法实现了对森林火灾蔓延的精确计算和实时、逼真模拟,为灾害应急部门提供信息化支撑。
文摘目的基于数据挖掘技术,建立三七叶面积生长预测模型,对于三七整个生长期的精准管理与决策提供参考。方法基于粒子群-随机森林算法,采用2018、2019年4~10月云南省红河自治州泸西县三七种植基地棚内气象因子数据以及三七叶面积生长数据作为训练集和测试集构建生长预测模型。结果通过特征工程中皮尔森系数分析可知,三七叶生长与土壤温度、上方水蒸气压和下方水蒸气压等气象因子呈正相关,其中土壤温度正相关程度最大,其皮尔森相关系数在0.75~0.90;下方土壤热通量与三七叶生长呈负相关,其皮尔森相关系数为−0.4~−0.3;通过粒子群优化随机森林算法训练的生长预测模型,其均方根误差(root mean square error,RMSE)收敛时值为0.02182,模型优化后的三七叶生长预测模型决定系数R 2达到0.99997。结论通过多种算法对比实验结果表明,粒子群-随机森林算法构建的三七叶面积生长预测模型具有较高的预测精度。该方法为三七叶的生长预测提供了新的研究思路。
