摘要: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.
关键词:soil mechanics rolling dynamic compaction
单位:School; of; Civil; Environmental; and; Mining; Engineering; University; of; Adelaide; Adelaide; 5005; Australia
注:因版权方要求,不能公开全文,如需全文,请咨询杂志社
相关期刊
China Petroleum Processing Petrochemical Technology Petroleum Science Chinese Optics Letters Applied Mathematics:A Journal of Chinese Universities Acta Pharmacologica Sinica Chinese Geographical Science Chinese Journal of Chemical Physics Journal of Geographical Sciences Journal of Integrative Plant Biology Journal of Systems Science and Complexity