| description abstract | Although the influence of gradation on soil properties has been recognized, the grain sizes have not yet been fully considered in maximum dry density (MDD) evaluation and prediction. This study explores the impacts of full grain sizes on the MDD and evaluates MDDs through different particle size distribution (PSD) curves of gravelly soils. First, full grain sizes, d10–d100, were extracted from 90 gravelly soil samples and employed as input parameters to develop the neural model for MDD, by using genetic algorithm (GA) to optimize the back-propagation (BP) neural network. A mean impact value (MIV) method was then proposed to quantify the impact of each grain size, followed by the vibrating compaction tests for 22 artificially designated gravelly soil specimens to verify the model and evaluate the MDDs based on grain sizes. The model analysis and verification tests agreed with each other and clearly showed the intrinsic dependence of grain sizes on MDD for gravelly soils. As revealed by MIV analysis, d50–d100 and d10–d40 displayed positive and negative impact to MDD during compaction, behaving as the relatively coarse and fine grain sizes, respectively. Additionally, the relative impact weight showed that d100 tends to have the largest impact to MDD, and high-sensitivity (HS), medium sensitivity (MS), and low sensitivity (LS) could be proposed to distinguish these grain sizes. As the MDD was found to correspond exactly to the unique soil gradation, the full grain sizes should be employed to precisely predict and correctly evaluate the MDD of gravelly soils. | |
