优化的灰色离散Verhulst模型在基坑沉降预测中的应用

发布时间：2018-05-29 07:35

本文选题：沉降预测 + 优化的灰色离散Verhulst模型　；参考：《中南大学学报(自然科学版)》2017年11期

【摘要】：基于传统的灰色Verhulst模型在基坑沉降预测中精度较低的问题,提出优化的灰色离散Verhulst模型。在基坑沉降监测中,由于有新的监测沉降值不断补充到原始数据序列中,各种因素会带来新的扰动,原来的模型精度降低,为避免由此产生的误差,用新陈代谢方法建立优化灰色离散Verhulst一维、二维新陈代谢模型。将传统Verhulst模型、优化的灰色离散Verhulst模型及优化灰色离散Verhulst一维、二维新陈代谢模型进行比较。研究结果表明:该模型通过采用离散化思维对原数据序列进行倒数变换,从连续形式向离散形式变化,减小了传统Verhulst模型建模过程中从微分方程到差分方程带来的误差;采用新陈代谢方法的优化灰色离散Verhulst模型精度更高,可选用该模型对基坑进行沉降预测。
[Abstract]:Based on the low precision of traditional grey Verhulst model in foundation pit settlement prediction, an optimized grey discrete Verhulst model is proposed. In foundation pit settlement monitoring, new monitoring settlement values are added to the original data series, various factors will bring new disturbances, the accuracy of the original model will be reduced, in order to avoid the resulting errors, An optimal grey discrete Verhulst model of one and two dimensional metabolism was established by metabolic method. The traditional Verhulst model, the optimized grey discrete Verhulst model and the optimized grey discrete Verhulst model are compared. The results show that the model changes from continuous form to discrete form by the reciprocal transformation of the original data sequence with discrete thinking, which reduces the error from differential equation to difference equation in the modeling process of traditional Verhulst model. The optimal grey discrete Verhulst model with metabolic method is more accurate and can be used to predict the settlement of foundation pit.
【作者单位】：中南大学地球科学与信息物理学院;
【基金】：国家自然科学基金资助项目(50878212) 中南大学中央高校基本科研业务费专项资金资助项目(2016zzts435)~~
【分类号】：TU433;TU753

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