Field tests in geotechnical engineering are fundamental for identification of the underground conditions.The standard penetration test(SPT) is the most commonly used geotechnical approach. There has been an increase b...Field tests in geotechnical engineering are fundamental for identification of the underground conditions.The standard penetration test(SPT) is the most commonly used geotechnical approach. There has been an increase both in the use and application of the in situ tests: cone penetration test(CPT) and dynamic probing(DP). Several empirical SPT-CPT and dynamic probing light(DPL)-CPT correlations for sandy soils have been discussed in the literature. New SPT-CPT and DPL-CPT correlations for the sandy soils of the city of Vitoria, in the southeast of Brazil, are suggested in this paper. Statistical analyses to evaluate the quality of the data used are performed, and the suggested correlations are validated with several previous published datasets. The paper also provides some insights into SPT-CPT correlations and soil characteristics(i.e. the mean particle size and the fines fraction of the soil).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
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
基金the sponsorship from the Brazilian government agencies CNPqFAPES
文摘Field tests in geotechnical engineering are fundamental for identification of the underground conditions.The standard penetration test(SPT) is the most commonly used geotechnical approach. There has been an increase both in the use and application of the in situ tests: cone penetration test(CPT) and dynamic probing(DP). Several empirical SPT-CPT and dynamic probing light(DPL)-CPT correlations for sandy soils have been discussed in the literature. New SPT-CPT and DPL-CPT correlations for the sandy soils of the city of Vitoria, in the southeast of Brazil, are suggested in this paper. Statistical analyses to evaluate the quality of the data used are performed, and the suggested correlations are validated with several previous published datasets. The paper also provides some insights into SPT-CPT correlations and soil characteristics(i.e. the mean particle size and the fines fraction of the soil).
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.
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.
