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
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 applications.RDC involves r...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 applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For 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 forecasting.The 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 types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In 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.展开更多
The influence of towing speed on the effectiveness of the 4-sided impact roller using earth pressure cells(EPCs)is investigated.Two field trials were undertaken;the first trial used three EPCs placed at varying depths...The influence of towing speed on the effectiveness of the 4-sided impact roller using earth pressure cells(EPCs)is investigated.Two field trials were undertaken;the first trial used three EPCs placed at varying depths between 0.5 m and 1.5 m with towing speeds of 9-12 km/h.The second used three EPCs placed at a uniform depth of 0.8 m,with towing speeds of 5-15 km/h.The findings from the two trials confirmed that towing speed influences the pressure imparted to the ground and hence compactive effort.This paper proposes that the energy imparted to the ground is best described in terms of work done,which is the sum of the change in both potential and kinetic energies.Current practice of using either kinetic energy or gravitational potential energy should be avoided as neither can accurately quantify rolling dynamic compaction(RDC)when towing speed is varied.展开更多
Melt shrinkage, salt bulge, and corrosiveness are common problems with saline soils, which damage highway foundations and cause huge financial losses. In order to improve the saline soil subgrade, dynamic compaction ...Melt shrinkage, salt bulge, and corrosiveness are common problems with saline soils, which damage highway foundations and cause huge financial losses. In order to improve the saline soil subgrade, dynamic compaction （DC） and rolling compaction （RC） technology were applied on the Qarhan-Golmud Highway in Qinghai Province, China. A field experi- ment was conducted in which shear strength, deformation modulus, and the working mechanism of the composite foun- dation were analyzed after reinforcement. Both the DC and RC methods were found to be effective and helped to improve the foundation strength of saline soils, although the ultimate bearing capacity and deformation modulus of the RC method were lower than that of the DC method.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supported under Australian Research Council’s Discovery Projects funding scheme(project No. DP120101761)
文摘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 applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For 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 forecasting.The 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 types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In 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.
文摘The influence of towing speed on the effectiveness of the 4-sided impact roller using earth pressure cells(EPCs)is investigated.Two field trials were undertaken;the first trial used three EPCs placed at varying depths between 0.5 m and 1.5 m with towing speeds of 9-12 km/h.The second used three EPCs placed at a uniform depth of 0.8 m,with towing speeds of 5-15 km/h.The findings from the two trials confirmed that towing speed influences the pressure imparted to the ground and hence compactive effort.This paper proposes that the energy imparted to the ground is best described in terms of work done,which is the sum of the change in both potential and kinetic energies.Current practice of using either kinetic energy or gravitational potential energy should be avoided as neither can accurately quantify rolling dynamic compaction(RDC)when towing speed is varied.
基金provided by the National 973 Project of China (No.2012CB026104)the National Natural Science Foundation of China (Nos.41171064,41271072)
文摘Melt shrinkage, salt bulge, and corrosiveness are common problems with saline soils, which damage highway foundations and cause huge financial losses. In order to improve the saline soil subgrade, dynamic compaction （DC） and rolling compaction （RC） technology were applied on the Qarhan-Golmud Highway in Qinghai Province, China. A field experi- ment was conducted in which shear strength, deformation modulus, and the working mechanism of the composite foun- dation were analyzed after reinforcement. Both the DC and RC methods were found to be effective and helped to improve the foundation strength of saline soils, although the ultimate bearing capacity and deformation modulus of the RC method were lower than that of the DC method.
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.
