Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results

作     者：R.A.T.M.Ranasinghe M.B.Jaksa F.Pooya Nejad Y.L.Kuo 

作者机构：School of CivilEnvironmental and Mining Engineering The University of Adelaide 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2019年第11卷第4期

页      面：815-823页

核心收录：

学科分类：08[工学] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0813[工学-建筑学] 0814[工学-土木工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：supported under Australian Research Council’s Discovery Projects funding scheme(project No. DP120101761) 

主　　题：Ground improvement Rolling dynamic compaction (RDC) Linear genetic programming (LGP) Dynamic cone penetrometer (DCP) test 

摘      要：Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement *** involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and ***,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its *** this,in this context,a prediction model is developed based on linear genetic programming (LGP),which is one of the common approaches in application of artificial intelligence for nonlinear *** model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided,8-t impact roller (BH-1300).It is shown that the model is accurate and reliable over a range of soil ***,a series of parametric studies confirms its robustness in generalizing *** addition,the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.