Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable predic...Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.展开更多
Three diferent kinds of artificially frozen soils are tested for artificial ground freezing(AGF) project in the tunnel construction of Stonecutters Island Sewage Treatment Works, Hong Kong. Uniaxial compressive test i...Three diferent kinds of artificially frozen soils are tested for artificial ground freezing(AGF) project in the tunnel construction of Stonecutters Island Sewage Treatment Works, Hong Kong. Uniaxial compressive test is conducted and uniaxial compressive strength, modulus of elasticity and Poisson's ratio are obtained. Meanwhile, relations of all these three parameters and temperature are fitted by linear function. The linear relationship between the above-mentioned parameters and temperature is suitable for engineering practice. Splitting tensile test of frozen soil is conducted to obtain tensile strength and find out failure pattern in test. All the parameters obtained are necessities in design and practice.展开更多
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,especi...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.展开更多
基金supported under Australian Research Council's Discovery Projects funding scheme(project No.DP120101761)
文摘Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.
基金the National Natural Science Foundation of China(No.51178336)
文摘Three diferent kinds of artificially frozen soils are tested for artificial ground freezing(AGF) project in the tunnel construction of Stonecutters Island Sewage Treatment Works, Hong Kong. Uniaxial compressive test is conducted and uniaxial compressive strength, modulus of elasticity and Poisson's ratio are obtained. Meanwhile, relations of all these three parameters and temperature are fitted by linear function. The linear relationship between the above-mentioned parameters and temperature is suitable for engineering practice. Splitting tensile test of frozen soil is conducted to obtain tensile strength and find out failure pattern in test. All the parameters obtained are necessities in design and practice.
基金supported under Australian Research Council's Discovery Projects funding scheme (project number DP120101761)
文摘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.
