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Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy

机译:使用应变能概念和ANN模型评估液化触发条件:容量能量

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摘要

In the present study, an artificial neural network (ANN) model was developed to establish a correlation between soils initial parameters and the strain energy required to trigger liquefaction in sands and silty sands. A relatively large set of data including 284 previously published cyclic triaxial, torsional shear and simple shear test results were employed to develop the model. A subsequent parametric study was carried out and the trends of the results have been confirmed via some previous laboratory studies. In addition, the data recorded during some real earthquakes at Wildlife, Lotung and Port Island Kobe sites plus some available centrifuge tests data have been utilized in order to validate the proposed ANN-based liquefaction energy model. The results clearly demonstrate the capability of the proposed model and the strain energy concept to assess liquefaction resistance (capacity energy) of soils.
机译:在本研究中,开发了一个人工神经网络(ANN)模型来建立土壤初始参数与触发砂土和粉质砂土液化所需的应变能之间的相关性。使用相对较大的数据集(包括284个先前发布的循环三轴,扭剪和简单剪力测试结果)来开发模型。随后进行了参数研究,结果已经通过一些先前的实验室研究得到了证实。此外,还利用了在野生动物,洛通和Port Island神户的一些真实地震中记录的数据以及一些可用的离心机测试数据,以验证所提出的基于人工神经网络的液化能模型。结果清楚地证明了所提出模型的能力和应变能概念来评估土壤的抗液化性(容量能)。

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