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Influence of Grey System Parameter Identification Method on Prediction of Bearing Capacity of Piles

机译:灰色系统参数识别方法对桩承载力预测的影响

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An unequal interval grey model GM (1,1) was established according to the variation characteristics of sequence data about the bearing capacity of overlength piles, and a difference equations was usually adopted to replace the grey differential equation for determining the system parameters of the model;;however, great errors would occur when the model thus established was used to predict the bearing capacity of overlength piles, that is, the prediction results would be overestimated or underestimated. In order to improve the prediction accuracy of the model, we established an error objective function based on optimization theory in this study, employed the method of nonlinear least squares to identify the system parameters in the grey differential equation for the bearing capacity of overlength piles, and built an optimization-based grey optimization model. The model system built with optimization method was used to predict the bearing capacity of overlength piles, and the predicted values fit the test values well. In addition, the model system had a higher accuracy, compared with the grey difference model built with difference method;;therefore, the model system built with optimization method could provide reference for prediction of the bearing capacity of overlength piles.
机译:根据围绕备用堆叠承载能力的序列数据的序列数据的变化特性建立了不平等的间隔灰色模型Gm(1,1),并且通常采用差分方程来替换灰色微分方程以确定模型的系统参数;然而,当所建立的模型用于预测副桩的承载能力时,将发生巨大错误,即,预测结果将被过度归于或低估。为了提高模型的预测准确性,我们在本研究中建立了基于优化理论的误差目标函数,采用了非线性最小二乘法的方法,以识别封闭桩承载能力的灰色微分方程中的系统参数,并构建了一种基于优化的灰度优化模型。使用优化方法构建的模型系统用于预测副桩承载能力,并且预测值良好地适合测试值。此外,模型系统的准确性更高,与差异方法构建的灰色差异模型相比;;因此,用优化方法构建的模型系统可以为预测副桩承载能力提供参考。

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