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
Dynamic cone penetrometer（DCP） has been used for decades to estimate the shear strength and stiffness properties of the subgrade soils. There are several empirical correlations in the literature to predict the resil...Dynamic cone penetrometer（DCP） has been used for decades to estimate the shear strength and stiffness properties of the subgrade soils. There are several empirical correlations in the literature to predict the resilient modulus values at only a specific stress state from DCP data, corresponding to the predefined thicknesses of pavement layers（a 50 mm asphalt wearing course, a 100 mm asphalt binder course and a200 mm aggregate base course）. In this study, field-measured DCP data were utilized to estimate the resilient modulus of low-plasticity subgrade Piedmont residual soil. Piedmont residual soils are in-place weathered soils from igneous and metamorphic rocks, as opposed to transported or compacted soils.Hence the existing empirical correlations might not be applicable for these soils. An experimental program was conducted incorporating field DCP and laboratory resilient modulus tests on "undisturbed" soil specimens. The DCP tests were carried out at various locations in four test sections to evaluate subgrade stiffness variation laterally and with depth. Laboratory resilient modulus test results were analyzed in the context of the mechanistic-empirical pavement design guide（MEPDG） recommended universal constitutive model. A new approach for predicting the resilient modulus from DCP by estimating MEPDG constitutive model coefficients（k;,k;and k;） was developed through statistical analyses. The new model is capable of not only taking into account the in situ soil condition on the basis of field measurements,but also representing the resilient modulus at any stress state which addresses a limitation with existing empirical DCP models and its applicability for a specific case. Validation of the model is demonstrated by using data that were not used for model development, as well as data reported in the literature.展开更多
In order to minimise the cost of constructing low volume roads (LVRs), it is essential that optimum use is made of locally available, naturally occurring materials. However, conflicts often arise between material acce...In order to minimise the cost of constructing low volume roads (LVRs), it is essential that optimum use is made of locally available, naturally occurring materials. However, conflicts often arise between material acceptability, as defined by conventional test methods and specifications, and material suitability in terms of actual engineering performance as a “fit-for-purpose” road construction material. To avoid such conflicts, it is crucial to adopt appropriate test methods and specifications for selecting construction materials. This paper presents a materials evaluation framework for optimizing the utilisation of materials in LVR pavements based on the use of the Dynamic Cone Penetrometer (DCP). This approach enables materials to be selected on the basis of their “fitness-for-purpose”. It avoids suitable materials from being rejected based on one or other of the traditionally specified parameters not being complied with, even though their strength, as measured by the DCP penetration rate (DN value in mm/blow) of the soil, may be adequate. Investigations of the properties of a wide range of locally available materials that have been used successfully in the construction of LVRs have confirmed the validity of the materials evaluation framework.展开更多
Among the geotechnical in situ tests,the dynamic penetration test(DPT)is commonly used around the world.However,DPT remains a rough technique and provides only one failure parameter:blow count or cone resistance.This ...Among the geotechnical in situ tests,the dynamic penetration test(DPT)is commonly used around the world.However,DPT remains a rough technique and provides only one failure parameter:blow count or cone resistance.This paper presents an improvement of the dynamic cone penetration test(DCPT)for soil characterisation based on the wave equation theory.Implemented on an instrumented lightweight dynamic penetrometer driving with variable energy,the main process of the test involves the separation and reconstruction of the waves propagating in the rods after each blow and provides a dynamic cone load-penetration(DCLT)curve.An analytical methodology is used to analyse this curve and to estimate additional strength and deformation parameters of the soil:dynamic and pseudo-static cone resistances,deformation modulus and wave velocity.Tests carried out in the laboratory on different specimens(wood,concrete,sand and clay)in an experimental sand pit and in the field demonstrated that the resulting DCLT curve is reproducible,sensitive and reliable to the test conditions(rod length,driving energy,etc.)as well as to the soil properties(nature,density,etc.).Obtained results also showed that the method based on shock polar analysis makes it possible to evaluate mechanical impedance and wave velocity of soils,as demonstrated by the comparisons with cone penetration test(CPT)and shear wave velocity measurements made in the field.This technique improves the method and interpretation of DPT and provides reliable data for shallow foundation design.展开更多
Widespread implementation of the DCP-DN design method for low volume roads has been promoted internationally over the past decade or so. The method has progressed from a simple determination of the in situ CBR investi...Widespread implementation of the DCP-DN design method for low volume roads has been promoted internationally over the past decade or so. The method has progressed from a simple determination of the in situ CBR investigation based on DCP-CBR correlations with respective cover requirements to a more sophisticated method using the DCP penetration data directly and omitting any need to use correlations with the CBR. This paper summarises the development of the method, and some of its advantages and compares the design structures with other recognised and widely implemented designs.展开更多
To provide a safe transportation system in an extremely cold region,evaluation needs to be conducted of the thickness and the volumetric water content of the active layer,as they significantly affect frost heave.The o...To provide a safe transportation system in an extremely cold region,evaluation needs to be conducted of the thickness and the volumetric water content of the active layer,as they significantly affect frost heave.The objective of this study was to evaluate the dielectric constant(κ)of the active layer using ground-penetrating radar(GPR)and a dynamic cone penetrometer(DCP);this evaluation was then used to estimate the thickness and the volumetric water content of the active layer.A field located in midwest Alaska was selected as the study site.A GPR survey and two DCP tests were conducted on the surface of the ground,and the ground temperature was measured.From the GPR survey,travel times of the electromagnetic wave in the active layer were obtained.In addition,the thickness of the active layer was determined by using the dynamic cone penetration index(DCPI)and ground temperature.By using the travel time and travel distance of the electromagnetic wave in the active layer,dielectric constants were calculated as 26.3 and 26.4 for two DCP points.From the mean dielectric constant,the volumetric water content was estimated to be 40%~43%,and the thickness of the active layer was evaluated along the GPR survey line.The spatial-scaled GPR image showed that the thickness of the active layer varied from 520 mm to 700 mm due to the presence of a puddle,which accelerated the heat exchange.The results show that evaluation of the dielectric constant using the GPR survey and the DCP test can be effectively used to estimate the thickness and the volumetric water content of the active layer.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Dynamic cone penetrometer（DCP） has been used for decades to estimate the shear strength and stiffness properties of the subgrade soils. There are several empirical correlations in the literature to predict the resilient modulus values at only a specific stress state from DCP data, corresponding to the predefined thicknesses of pavement layers（a 50 mm asphalt wearing course, a 100 mm asphalt binder course and a200 mm aggregate base course）. In this study, field-measured DCP data were utilized to estimate the resilient modulus of low-plasticity subgrade Piedmont residual soil. Piedmont residual soils are in-place weathered soils from igneous and metamorphic rocks, as opposed to transported or compacted soils.Hence the existing empirical correlations might not be applicable for these soils. An experimental program was conducted incorporating field DCP and laboratory resilient modulus tests on "undisturbed" soil specimens. The DCP tests were carried out at various locations in four test sections to evaluate subgrade stiffness variation laterally and with depth. Laboratory resilient modulus test results were analyzed in the context of the mechanistic-empirical pavement design guide（MEPDG） recommended universal constitutive model. A new approach for predicting the resilient modulus from DCP by estimating MEPDG constitutive model coefficients（k;,k;and k;） was developed through statistical analyses. The new model is capable of not only taking into account the in situ soil condition on the basis of field measurements,but also representing the resilient modulus at any stress state which addresses a limitation with existing empirical DCP models and its applicability for a specific case. Validation of the model is demonstrated by using data that were not used for model development, as well as data reported in the literature.
文摘In order to minimise the cost of constructing low volume roads (LVRs), it is essential that optimum use is made of locally available, naturally occurring materials. However, conflicts often arise between material acceptability, as defined by conventional test methods and specifications, and material suitability in terms of actual engineering performance as a “fit-for-purpose” road construction material. To avoid such conflicts, it is crucial to adopt appropriate test methods and specifications for selecting construction materials. This paper presents a materials evaluation framework for optimizing the utilisation of materials in LVR pavements based on the use of the Dynamic Cone Penetrometer (DCP). This approach enables materials to be selected on the basis of their “fitness-for-purpose”. It avoids suitable materials from being rejected based on one or other of the traditionally specified parameters not being complied with, even though their strength, as measured by the DCP penetration rate (DN value in mm/blow) of the soil, may be adequate. Investigations of the properties of a wide range of locally available materials that have been used successfully in the construction of LVRs have confirmed the validity of the materials evaluation framework.
文摘Among the geotechnical in situ tests,the dynamic penetration test(DPT)is commonly used around the world.However,DPT remains a rough technique and provides only one failure parameter:blow count or cone resistance.This paper presents an improvement of the dynamic cone penetration test(DCPT)for soil characterisation based on the wave equation theory.Implemented on an instrumented lightweight dynamic penetrometer driving with variable energy,the main process of the test involves the separation and reconstruction of the waves propagating in the rods after each blow and provides a dynamic cone load-penetration(DCLT)curve.An analytical methodology is used to analyse this curve and to estimate additional strength and deformation parameters of the soil:dynamic and pseudo-static cone resistances,deformation modulus and wave velocity.Tests carried out in the laboratory on different specimens(wood,concrete,sand and clay)in an experimental sand pit and in the field demonstrated that the resulting DCLT curve is reproducible,sensitive and reliable to the test conditions(rod length,driving energy,etc.)as well as to the soil properties(nature,density,etc.).Obtained results also showed that the method based on shock polar analysis makes it possible to evaluate mechanical impedance and wave velocity of soils,as demonstrated by the comparisons with cone penetration test(CPT)and shear wave velocity measurements made in the field.This technique improves the method and interpretation of DPT and provides reliable data for shallow foundation design.
文摘Widespread implementation of the DCP-DN design method for low volume roads has been promoted internationally over the past decade or so. The method has progressed from a simple determination of the in situ CBR investigation based on DCP-CBR correlations with respective cover requirements to a more sophisticated method using the DCP penetration data directly and omitting any need to use correlations with the CBR. This paper summarises the development of the method, and some of its advantages and compares the design structures with other recognised and widely implemented designs.
基金Project(2017YFC0805307) supported by the National Key Research and Development Program of ChinaProjects(51878078, 51927814, 51911530215) supported by the National Natural Science Foundation of China+4 种基金Project(2018-025) supported by the Training Program for High-level Technical Personnel in Transportation Industry, ChinaProject (2018JJ1026) supported by the Excellent Youth Foundation of Natural Science Foundation of Hunan Province, ChinaProject(17A008) supported by the Key Project of Education Department of Hunan Province, ChinaProjects(kfj150103, kfj170104) supported by the Open Research Fund of State Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science & Technology, ChinaProject(CX20190644) supported by the Postgraduate Scientific Research Innovation Project of Hunan Province, China。
基金supported by the National Research Council of Science & Technology (NST) grant by the Korean government (MSIP) (No. CRC-14-02-ETRI)
文摘To provide a safe transportation system in an extremely cold region,evaluation needs to be conducted of the thickness and the volumetric water content of the active layer,as they significantly affect frost heave.The objective of this study was to evaluate the dielectric constant(κ)of the active layer using ground-penetrating radar(GPR)and a dynamic cone penetrometer(DCP);this evaluation was then used to estimate the thickness and the volumetric water content of the active layer.A field located in midwest Alaska was selected as the study site.A GPR survey and two DCP tests were conducted on the surface of the ground,and the ground temperature was measured.From the GPR survey,travel times of the electromagnetic wave in the active layer were obtained.In addition,the thickness of the active layer was determined by using the dynamic cone penetration index(DCPI)and ground temperature.By using the travel time and travel distance of the electromagnetic wave in the active layer,dielectric constants were calculated as 26.3 and 26.4 for two DCP points.From the mean dielectric constant,the volumetric water content was estimated to be 40%~43%,and the thickness of the active layer was evaluated along the GPR survey line.The spatial-scaled GPR image showed that the thickness of the active layer varied from 520 mm to 700 mm due to the presence of a puddle,which accelerated the heat exchange.The results show that evaluation of the dielectric constant using the GPR survey and the DCP test can be effectively used to estimate the thickness and the volumetric water content of the active layer.
