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首页> 外文期刊>KSCE journal of civil engineering >A Methodology to Build a Groutability Formula via a Heuristic Algorithm
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A Methodology to Build a Groutability Formula via a Heuristic Algorithm

机译:通过启发式算法建立可灌浆性公式的方法

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The goal of this study is to provide a methodology to develop a groutability (N) formula of sandy silt soils using microfrne cement grouts in a permeation grouting. Because the Fines Content (FC) of the sandy silt soils studied is relatively high, and the size of the grouts used is significantly smaller than the Portland cement, the existing empirical formulas cannot deliver a promising prediction of N. Support Vector Machines (SVMs) is an alternative tool used to predict N. However, SVMs do not provide an explicit formula, which creates an obstacle for practical engineers. Thus, a heuristic algorithm (the Tabu search, TS) was used to build the prediction formula. A total of 240 in-situ data samples were analyzed to ensure the accuracy of the proposed formula. The format of the existing empirical formula was adopted in the proposed TS-based formula. Four parameters were considered in our TS models: the effective soil particle size (D_(10)), the soil particle size (D_(15)), the water-to-cement ratio (w/c) and the FC. The prediction accuracy of the TS-based formula was approximately 94.17%, indicating that the proposed formula is a suitable tool. Because the proposed formula has a similar format to that of formulas that are typically used, the proposed approach can be implemented readily in practical engineering settings. Note that the proposed formula was only verified by the collected data samples, the suitability of applying the built formula to other conditions needs more investigation.
机译:这项研究的目的是提供一种方法,通过在渗透灌浆中使用微粉水泥灌浆来开发砂质粉土的灌浆性(N)公式。由于所研究的沙质粉土的细度(FC)相对较高,并且所使用的灌浆尺寸明显小于波特兰水泥,因此现有的经验公式无法对N做出有希望的预测。支持向量机(SVM)是用于预测N的替代工具。但是,SVM没有提供明确的公式,这给实际工程师带来了障碍。因此,使用启发式算法(禁忌搜索,TS)建立预测公式。共分析了240个原位数据样本,以确保所提出公式的准确性。提议的基于TS的公式中采用了现有经验公式的格式。我们的TS模型考虑了四个参数:有效土壤粒径(D_(10)),土壤粒径(D_(15)),水灰比(w / c)和FC。基于TS的公式的预测准确性约为94.17%,表明所提出的公式是合适的工具。因为建议的公式具有与通常使用的公式相似的格式,所以可以在实际工程环境中轻松实现建议的方法。注意,建议的公式仅由收集的数据样本验证,将构建的公式应用于其他条件的适用性需要进一步研究。

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