Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

作者：Pin Zhang,Zhen-Yu Yin,Yin-Fu Jin,Tommy HTChan,Fu-Ping Gao

摘要：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.

发文机构：Department of Civil and Environmental Engineering School of Civil Engineering&Built Environment Key Laboratory for Mechanics in Fluid Solid Coupling Systems China School of Engineering Science

关键词：COMPRESSIBILITYClaysMachinelearningOptimizationRandomforestGeneticalgorithm

分类号： TP1[自动化与计算机技术—控制理论与控制工程]