Rolling Dynamic Compaction（RDC）,which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especially with respect to a greater influence depth and a higher speed of compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the effectiveness of RDC in a range of ground conditions. This paper presents a new and unique predictive tool developed by means of artificial neural networks（ANNs） that permits a priori prediction of density improvement resulting from a range of ground improvement projects that employed 4-sided RDC modules;commercially known as"impact rollers". The strong coefficient of correlation（i.e. R>0.86） and the parametric behavior achieved in this study indicate that the model is successful in providing reliable predictions of the effectiveness of RDC in various ground conditions.
类型: 期刊论文
作者: R.A.T.M.Ranasinghe,M.B.Jaksa,F.Pooya Nejad,Y.L.Kuo
来源: 岩石力学与工程学报 2019年01期
年度: 2019
分类: 工程科技Ⅰ辑,工程科技Ⅱ辑
专业: 建筑科学与工程
单位: School of Civil,Environmental and Mining Engineering,University of Adelaide
基金: supported under Australian Research Council′s Discovery Projects funding scheme (project number DP120101761)
分类号: TU472.31
DOI: 10.13722/j.cnki.jrme.2017.1586
页码: 153-170
总页数: 18
文件大小: 1559K
下载量: 85
本文来源: https://www.lunwen90.cn/article/5770d912f7772f05adb6789f.html