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 R^(2) 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
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 R^(2) 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。
