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Application of.artif.cial neural networks f.r predicting the impact of.rolling dynamic compaction using dynamic cone penetrometer test results

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Journal of.Rock Mechanics and Geotechnical Engineering 2017年第2期9卷 340-349页

作者： R.A.T.M. Ranasinghe M.B. Jaksa Y.L. Kuo f. pooya nejad School of.Civil Environmental and Mining EngineeringUniversity of AdelaideAdelaideAustralia

Rolling dynamic compaction(RDC),which involves the towing of.a noncircular module,is now widespread and accepted among many other soil compaction ***,to date,there is no accurate method f.r reliable prediction of.the densif.cation of.soil and the extent of.ground improvement by means of.*** study presents the application of.artif.cial neural networks(ANNs) f.r a priori prediction of.the ef.ectiveness of.*** models are trained with in situ dynamic cone penetration(DCP) test data obtained f.om previous civil projects associated with the 4-sided impact *** predictions f.om the ANN models are in good agreement with the measured f.eld data,as indicated by the model correlation coef.icient of.approximately *** is concluded that the ANN models developed in this study can be successf.lly employed to provide more accurate prediction of.the perf.rmance of.the RDC on a range of.soil types.

关键词： Rolling dynamic compaction(RDC) Ground improvement Artif.cial neural network(ANN) Dynamic cone penetration(DCP) test

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Prediction of.the ef.ectiveness of.rolling dynamic compaction using artif.cial neural networks and cone penetration test data

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岩石力学与工程学报 2019年第1期38卷 153-170页

作者： R.A.T.M.Ranasinghe M.B.Jaksa f.pooya nejad Y.L.Kuo School of.Civil Environmental and Mining EngineeringUniversity of Adelaide

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 inf.uence depth and a higher speed of.compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the ef.ectiveness of.RDC in a range of.ground conditions. This paper presents a new and unique predictive tool developed by means of.artif.cial neural networks(ANNs) that permits a priori prediction of.density improvement resulting f.om a range of.ground improvement projects that employed 4-sided RDC modules;commercially known as"impact rollers". The strong coef.icient of.correlation(i.e. R>0.86) and the parametric behavior achieved in this study indicate that the model is successf.l in providing reliable predictions of.the ef.ectiveness of.RDC in various ground conditions.

关键词： soil mechanics rolling dynamic compaction artif.cial neural networks cone penetration test ground improvement

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Genetic programming f.r predictions of.ef.ectiveness of.rolling dynamic compaction with dynamic cone penetrometer test results

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Journal of.Rock Mechanics and Geotechnical Engineering 2019年第4期11卷 815-823页

作者： R.A.T.M.Ranasinghe M.B.Jaksa f.pooya nejad Y.L.Kuo School of.Civil Environmental and Mining Engineering The University of Adelaide

Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successf.l in many ground improvement *** involves repeatedly delivering high-energy impact blows onto the ground surf.ce,which improves soil density and thus soil strength and ***,there exists a lack of.methods to predict the ef.ectiveness of.RDC in dif.erent 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.artif.cial intelligence f.r nonlinear *** model is based on in situ density-related data in terms of.dynamic cone penetrometer (DCP) results obtained f.om 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 conf.rms its robustness in generalizing *** addition,the results of.the comparative study indicate that the optimal LGP model has a better predictive perf.rmance than the existing artif.cial neural network (ANN) model developed earlier by the authors.

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

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