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首页> 外文期刊>International journal of geotechnical engineering >Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN, part Ⅰ
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Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN, part Ⅰ

机译：人工神经网络预测偏心加载条形基础的极限承载力，第一部分

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Extensive laboratory model tests were conducted on a strip foundation lying over sand bed subjected to an eccentrically inclined load to determine the ultimate bearing capacity. Based on the model test results, a neural network model was developed to predict the reduction factor. This reduction factor (RF) is the ratio of the ultimate bearing capacity of the foundation subjected to an eccentrically inclined load to the ultimate bearing capacity of the foundation subjected to a centric vertical load. Different sensitivity analysis was carried out to evaluate the parameters affecting the reduction factor. Emphasis is given on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of input parameters on the output. A prediction model equation is established using the trained weights of the neural network model. The predictions from artificial neural network (ANN), and those from two other approaches, were compared with the laboratory model test results. The ANN model results found to be more accurate and well matched with other results.
机译：在砂床上的条形基础上进行了广泛的实验室模型测试，承受了偏心倾斜载荷，以确定极限承载力。根据模型测试结果，开发了一个神经网络模型来预测降低因子。该减小因子（RF）是承受偏心倾斜载荷的基础极限承载力与承受垂直竖向载荷的基础极限承载力之比。进行了不同的灵敏度分析，以评估影响折减系数的参数。基于已开发的神经网络模型的权重，着重于神经解释图的构建，以找出输入参数对输出的正向或逆向影响。使用神经网络模型的训练权重，建立预测模型方程。将人工神经网络（ANN）以及其他两种方法的预测与实验室模型测试结果进行了比较。发现ANN模型的结果更准确，并且与其他结果完全匹配。

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