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Bearing Capacity Prediction of Inclined Loaded Strip Footing on Reinforced Sand by ANN

机译：基于ANN的加筋砂倾斜条形地基承载力预测。

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Laboratory model tests have been conducted on a strip foundation resting over multi-layered geogrid-reinforced dense and loose sand subjected to inclined load. Based on the laboratory model test results, a neural network model is developed to estimate the reduction factor for bearing capacity. The reduction factor obtained by ANN can be used to estimate the ultimate bearing capacity of a strip foundation subjected to centric inclined load from the ultimate bearing capacity of the same foundation under centric vertical loading. A thorough sensitivity analysis was carried out to find out the important parameters affecting the reduction factor. Emphasis was given on the construction of neural interpretation diagram, based on the weights developed in the neural network model, to determine the direct or inverse effect of input parameters to the output. An ANN model equation is developed based on trained weights of the neural network model. The results from artificial neural network (ANN) were compared with the laboratory model test results and these results are in good agreement.
机译：实验室模型测试是在条形基础上进行的，该条形基础搁置在多层土工格栅加固的承受倾斜载荷的致密和疏松沙子上。根据实验室模型的测试结果，开发了一个神经网络模型来估计承载力的降低因子。通过ANN获得的折减系数可用于根据中心竖向载荷下相同基础的极限承载力来估算承受中心倾斜载荷的条形基础的极限承载力。进行了彻底的灵敏度分析，以找出影响降低因子的重要参数。基于神经网络模型中开发的权重，着重于神经解释图的构建，以确定输入参数对输出的正向或逆向影响。基于神经网络模型的训练权重，开发了一个ANN模型方程。将人工神经网络（ANN）的结果与实验室模型测试结果进行了比较，这些结果吻合良好。

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