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
Field investigations following the 2008 Ms8.0 Wenchuan earthquake identified 118 liquefaction sites, most of which are underlain by gravelly sediment in the Chengdu Plain and adjacent Mianyang area, in the Sichuan Pro...Field investigations following the 2008 Ms8.0 Wenchuan earthquake identified 118 liquefaction sites, most of which are underlain by gravelly sediment in the Chengdu Plain and adjacent Mianyang area, in the Sichuan Province. Gravel sediment in the Sichuan province is widely distributed; hence it is necessary to develop a method for prediction and evaluation of gravel liquefaction behavior. Based on liquefaction investigation data and in-situ testing, and with reference to existing procedures for sandy soil liquefaction evaluation, a fundamental procedure for gravel liquefaction evaluation using dynamic penetration tests （DPT） is proposed along with a corresponding model and calculation formula. The procedure contains two stages, i.e., pre-determination and re-determination. Pre-determination excludes impossible liquefiable or non-liquefiable soils, and re-determination explores a DPT-based critical N120 blows calculation model. Pre-determination includes three criteria, i.e., geological age, gravel contents, gravel sediment depths and water tables. The re-determination model consists of five parameters, i.e., DPT reference values, gravel contents, gravel sediment depths, water tables and seismic intensities. A normalization method is used for DPT reference values and an optimization method is used for the gravel sediment depth coefficient and water table coefficient. The gravel liquefaction evaluation method proposed herein is simple and takes most influencing factors on gravel sediment liquefaction into account.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Fundamental Research Funds of Institute of Engineering Mechanics Under Grant No.2009B01 and No.200708001National Natural Science Foundation of China Under Grant No.90715017International Corporation Project of Science and Technology Administration of China Under Grant No.2009DFA71720
文摘Field investigations following the 2008 Ms8.0 Wenchuan earthquake identified 118 liquefaction sites, most of which are underlain by gravelly sediment in the Chengdu Plain and adjacent Mianyang area, in the Sichuan Province. Gravel sediment in the Sichuan province is widely distributed; hence it is necessary to develop a method for prediction and evaluation of gravel liquefaction behavior. Based on liquefaction investigation data and in-situ testing, and with reference to existing procedures for sandy soil liquefaction evaluation, a fundamental procedure for gravel liquefaction evaluation using dynamic penetration tests （DPT） is proposed along with a corresponding model and calculation formula. The procedure contains two stages, i.e., pre-determination and re-determination. Pre-determination excludes impossible liquefiable or non-liquefiable soils, and re-determination explores a DPT-based critical N120 blows calculation model. Pre-determination includes three criteria, i.e., geological age, gravel contents, gravel sediment depths and water tables. The re-determination model consists of five parameters, i.e., DPT reference values, gravel contents, gravel sediment depths, water tables and seismic intensities. A normalization method is used for DPT reference values and an optimization method is used for the gravel sediment depth coefficient and water table coefficient. The gravel liquefaction evaluation method proposed herein is simple and takes most influencing factors on gravel sediment liquefaction into account.
