Effects of Organosilane-modified PCE on the Fluidity and Hydration Properties of Cement-Fly Ash Composite Binder

Effects of organosilane-modified PCE (OS-PCE) on the fluidity and the hydration properties of cement-fly ash (FA) composite binder were systematically analyzed.The experimental results show that OS-PCE possesses respectively 36.98% and 36.67% higher saturated adsorption amount on cement and FA,in comparison with ordinary PCE,and can contribute to higher fluidity of cement-FA composite binder.The addition of OS-PCE retards hydration process of cement-FA composite binder proportionally with the dosage of OS-PCE,but promotes the hydration kinetics of the composite binder.The reactivity enhancement is attributed to the well-dispersed FA by OS-PCE,which provides more nucleation sites for the reaction of heterogeneous C-S-H and enhances the contact with water to react with CH forming pozzolanic C-S-H.Well-distributed hydration products are exhibited in the hardened binder added with OS-PCE,with a large number of hydrated gels uniformly fill in the pores and gaps,which improves the compaction of the hardened structure.

1960-2016年中国黄土高原气象干旱趋势的归因

This study uses two forms of the Palmer Drought Severity Index(PDSI),namely the PDSI_TH(potential evapotranspiration estimated-by the Thornthwaite equation)and the PDSI_PM(potential evapotranspiration estimated by the FAO Penman-Monteith equation),to characterize the meteorological drought trends during 1960–2016 in the Loess Plateau(LP)and its four subregions.By designing a series of numerical experiments,we mainly investigated various climatic factors'contributions to the drought trends at annual,summer,and autumn time scales.Overall,the drying trend in the PDSI_TH is much larger than that in the PDSI_PM.The former is more sensitive to air temperature than precipitation,while the latter is the most sensitive to precipitation among all meteorological factors.Increasing temperature results in a decreasing trend(drying)in the PDSI_TH,which is further aggravated by decreasing precipitation,jointly leading to a relatively severe drying trend.For the PDSI_PM that considers more comprehensive climatic factors,the drying trend is partly counteracted by the declining wind speed and solar radiation.Therefore,the PDSI_PM ultimately shows a much smaller drying trend in the past decades.

基于机器学习方法日尺度下模拟三峡对鄱阳湖水位的影响

Lake water level is an essential indicator of environmental changes caused by natural and human factors.The water level of Poyang Lake,the largest freshwater lake in China,has exhibited a dramatic variation for the past few years,especially after the completion of the Three Gorges Dam(TGD).However,there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level(PLWL)at finer temporal scales(e.g.,the daily scale).Here,we used three machine learning models,namely,an Artificial Neural Network(ANN),a Nonlinear Autoregressive model with exogenous input(NARX),and a Gated Recurrent Unit(GRU),to simulate the daily lake level during 2003-2016.We found that machine learning models with historical memory(i.e.,the GRU model)are more suitable for simulating the PLWL under the influence of the TGD.The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods.Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL,the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level.This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.