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Modeling of compressive strength of cemented sandy soil

机译:粘液砂土压缩强度建模

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摘要

This paper attempted to show the application of particle swarm optimization in the prediction of the compressive strength of cement sandy soil from the curing period, porosity of sample and percentage of cement. The results of the study show that the unconfined compressive strength of the cement stabilized sandy soil increases with an increasing cement content curing time period. Moreover the compressive strength decreases with an increasing porosity. The compressive strength improvement due to cement treatment has a larger increase in samples with less porosity. In addition, particle swarm optimization algorithm is and accurate technique in estimation of compressive strength of cement stabilized sandy soil. In order to compare of existing correlations, a total number of 100 unconfined compressive tests and 15 scanning electron microscope tests have been conducted on cemented Babolsar sand. It can be concluded that compared to existing correlations models, particle swarm optimization algorithm models give more reliable prediction about compressive strength of cement satblized sandy soil. Moreover, the sensitivity analysis of the polynomial model shows that cement content and porosity have significant impact on predicting unconfined compressive strength.
机译:本文试图展示粒子群优化在从固化期,样品孔隙率和水泥百分比上预测水泥砂土的抗压强度的应用。该研究的结果表明,水泥稳定的砂土的非整合抗压强度随着水泥含量固化时间段的增加而增加。此外,压缩强度随着孔隙率的增加而降低。由于水泥处理引起的抗压强度改善具有较小的孔隙率的样品较大。此外,粒子群优化算法和准确的技术估计水泥稳定的砂土抗压强度。为了比较现有的相关性,在粘合的Babolsar沙子上进行了100个无凝结的压缩试验和15个扫描电子显微镜测试。可以得出结论,与现有的相关模型相比,粒子群优化算法模型对水泥饱和砂土的抗压强度提供了更可靠的预测。此外,多项式模型的敏感性分析表明,水泥含量和孔隙率对预测无凝固的抗压强度具有显着影响。

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