基于遗传-GRNN在深基坑地连墙测斜预测中的研究

王雨; 刘国彬; 屠传豹

基于遗传-GRNN在深基坑地连墙测斜预测中的研究 English Version

(同济大学土木工程学院，上海 200092)

详细信息

作者简介:
王雨(1988– )，女，北京人，硕士研究生，同济大学隧道及地下建筑工程专业。

中图分类号: TU47
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出版历程
- 收稿日期: 2012-08-23
- 发布日期: 2012-11-12

Deformation prediction for deep excavations based on genetic algorithms-GRNN

(College of Civil Engineering, Tongji University, Shanghai 200092, China)

摘要

摘要: 基坑工程由于受多种因素的影响，目前已成为岩土工程中的重点和难点。在基坑工程施工中，需要根据现场实际情况、周围环境、建筑安全等级等对变形进行严格控制。通过现场监测的深基坑围护结构变形信息资料，对实测数据进行整理和分析，利用神经网络对围护结构的变形做出预测的智能化施工成为基坑工程的发展趋势之一。研究了一种基于遗传算法的广义回归神经网络学习算法。该算法运用遗传算法寻找广义回归神经网络唯一参数光滑因子的最优解，将最优解赋予广义回归神经网络进行预测。在时间序列预测中，工程实例计算证明了遗传–广义回归神经网络预测的有效性和可行性，为时间序列预测提供了一种新途径。
- 深基坑 /
- 地下连续墙 /
- 变形预测 /
- 遗传算法 /
- 广义回归神经网络
Abstract: Affected by various factors, the deep excavation has become one of the key problems in geotechnical engineering. In practice, the deformation must be controlled rigorously according to the actual situation, surrounding environment and building safety grade. The intelligent construction has become one of the tendencies of deep excavation engineering, that is, it is to predict the deformation of retaining structures by neural network by collecting and analyzing monitoring data which record the deformation information. The generalized regression neural network (GRNN) is studied based on the genetic algorithm (GA). In this algorithm, GA is adopted to search the optimal smooth factor which is the only factor of GRNN, and then the GA-GRNN is used for prediction. The simulation experiment indicates that the proposed method is effective in time series prediction.
- deep excavation /
- diaphragm wall /
- deformation prediction /
- genetic algorithm /
- generalized regression neural network

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参考文献(14)

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