Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.T...Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.展开更多
随机森林模型多采用网格搜索的参数优化方法,存在搜索间隔固定、搜索效率低下的问题。为了克服以上缺陷,提出一种基于自适应遗传算法的随机森林模型参数优化方法,通过动态调节遗传操作的交叉、变异概率,在尽可能多保留优势粒子的同时更...随机森林模型多采用网格搜索的参数优化方法,存在搜索间隔固定、搜索效率低下的问题。为了克服以上缺陷,提出一种基于自适应遗传算法的随机森林模型参数优化方法,通过动态调节遗传操作的交叉、变异概率,在尽可能多保留优势粒子的同时更有效地产生新优势粒子,达到跳出局部最优并快速到达全局最优点的目的。利用提出的参数优化方法对随机森林算法中的决策树数目、最大树深度进行参数优化。使用Boston house price数据集仿真的结果表明,使用该参数优化方法优化后的随机森林模型的回归预测效果得到一定提高。展开更多
基金financial support provided by the RIF project(Grant No.PolyU R5037-18F)from the Research Grants Council(RGC)of Hong Kong is gratefully acknowledged。
文摘Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.
文摘随机森林模型多采用网格搜索的参数优化方法,存在搜索间隔固定、搜索效率低下的问题。为了克服以上缺陷,提出一种基于自适应遗传算法的随机森林模型参数优化方法,通过动态调节遗传操作的交叉、变异概率,在尽可能多保留优势粒子的同时更有效地产生新优势粒子,达到跳出局部最优并快速到达全局最优点的目的。利用提出的参数优化方法对随机森林算法中的决策树数目、最大树深度进行参数优化。使用Boston house price数据集仿真的结果表明,使用该参数优化方法优化后的随机森林模型的回归预测效果得到一定提高。
