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Compressive Strength of Sandy Soils Stabilized with Alkali-Activated Volcanic Ash and Slag

机译:用碱活化的火山灰和炉渣稳定砂土抗压强度

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In recent years, compared with the traditional portland cement, environmentally friendly geopolymers have gained more attention as construction materials. This paper considered volcanic ash (VA) and ground granulated blast furnace slag (GGBFS) in different percentages (0%, 3%, 7%, and 10%) as a replacement for the conventionally used portland cement to stabilize sandy soils. NaOH and Na_2SiO_3 in different concentrations (4, 8, and 12 M) and alkali to binder ratios (1, 1.5, 2, and 3) were used as alkali activator solutions to build new geopolymers. Samples were cured at both ambient and oven temperatures and for 1, 7, and 28 days. Unconfined compressive strength (UCS) of samples then was evaluated. Two predictive approaches, artificial neural network (ANN) modeling and the evolutionary polynomial regression technique (EPR), were applied to model UCS of geopolymerized sand samples. Regarding the high value of the coefficient of determination of the proposed ANN, 97%, and acceptable prediction errors, RMS error of 0.0439 and MAE of 0.0336, an 8-5-10-1 ANN was introduced as a more accurate tool for the prediction of UCS. Next, three-dimensional parametrical studies investigated the effects of simultaneous changes in alkali solution, binder, and curing condition parameters on UCS values of geopolymerized samples. Sensitivity analysis based on the cosine amplitude method introduced the Si/Al ratio as the parameter most affecting and VA content as the parameter least affecting the compressive strength of samples. Results were analyzed further using pH and electrical conductivity tests and interpreted based on microstructural investigations using scanning electron microscopy (SEM) images and X-ray diffraction analysis.
机译:近年来,与传统的波特兰水泥相比,环保的地缘聚合物在建筑材料中获得了更多的关注。本文认为火山灰(VA)和地面粒状高炉渣(GGBF)以不同的百分比(0%,3%,7%和10%),作为常规使用的波特兰水泥的替代品,以稳定砂土土壤。用不同浓度(4,8和12μm)和碱与粘合剂比(1,1.5,2和3)的NaOH和Na_2SiO_3用作碱活化剂溶液,以构建新的地质聚合物。将样品在环境和烘箱温度下固化,并为1,7和28天。然后评价样品的非排除的压缩强度(UCS)。两种预测方法,人工神经网络(ANN)建模和进化多项式回归技术(EPR)被应用于地质聚合砂样的模型UCS。关于所提出的ANN的测定系数的高值,97%和可接受的预测误差,0.0439和MAE为0.0336的RMS误差,作为预测的更准确的工具,引入了8-5-10-1个ANN UCS。接下来,三维参数研究研究了碱溶液,粘合剂和固化条件参数同时变化对地质聚合样品的UCS值的影响。基于余弦振幅法的灵敏度分析引入了Si / Al比作为最大的参数和VA含量作为样品的压缩强度的参数。进一步使用pH和电导率试验进行分析结果,并根据使用扫描电子显微镜(SEM)图像和X射线衍射分析来解释微观结构研究。

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