优化GA-BP神经网络模型及基坑变形预测

刘锦; 李峰辉; 刘秀秀

doi:10.3973/j.issn.2096-4498.2021.10.012

隧道建设 >

2021 , Vol. 41 >Issue 10: 1733 - 1739

DOI: https://doi.org/10.3973/j.issn.2096-4498.2021.10.012

研究与探索

优化GA-BP神经网络模型及基坑变形预测

刘锦 ,
李峰辉 ,
刘秀秀

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（1. 陕西铁路工程职业技术学院城轨工程学院，陕西渭南 714000；2. 郑州市交通规划勘察设计研究院，河南郑州 450000； 3. 曲阜远东职业技术学院，山东济宁 273100）

刘锦(1983—)，男，陕西渭南人，2010年毕业于重庆交通大学，桥梁与隧道工程专业，硕士，讲师，主要从事桥梁与隧道工程专业方面的研究与教学工作。 E-mail： douge1.414@163.com。

网络出版日期: 2021-11-04

基金资助

陕西铁路工程职业技术学院2020年首批科研基金项目（KY2020-27）

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Optimized Genetic AlgorithmBack Propagation Neural Network Model and Its Application in Foundation Pit Deformation Prediction

LIU Jin ,
LI Fenghui ,
LIU Xiuxiu

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(1. Urban Railway Engineering Department, Shannxi Railway Institute, Weinan 714000, Shannxi, China; 2. Zhengzhou Communications Planning, Survey and Design Institute, Zhengzhou 450000, Henan, China; 3. Qufu Fareast Vocational and Technical College, Jining 273100, Shandong, China)

Online published: 2021-11-04

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摘要

针对现有GA-BP神经网络预测模型在训练样本预处理和隐含层结构设计方面的不足，通过相关系数回归分析确定最佳归一化区间，利用统计学原理推导得到隐含层神经元个数的解析式，并提出与其相适应的最佳单隐含层神经元个数的取值范围。结果表明： 1）经相关系数回归分析确定训练样本预处理的最佳归一化区间为［0.05，0.95］； 2）通过统计对比和反复试算，得到单隐含层结构最佳神经元个数区间为［4，7］，双隐层更适用于神经元个数较多的情况（>4）； 3）“新陈代谢”方式选取训练样本可显著降低基坑变形的“时空效应”和人为因素干扰； 4）构建“4—2（7，8）—1”型GA-BP神经网络模型，对不同深度基坑进行水平位移预测，精度评价指标表明优化的GA-BP神经网络模型预测效果良好，对工程开展具有参考价值，经多模型对比可知模型优化效果良好。

关键词： 基坑工程; GA-BP神经网络; 基坑监测; 水平位移; 变形预测

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刘锦 , 李峰辉 , 刘秀秀 . 优化GA-BP神经网络模型及基坑变形预测[J]. 隧道建设, 2021 , 41(10) : 1733 -1739 . DOI: 10.3973/j.issn.2096-4498.2021.10.012

Abstract

The existing genetic algorithm (GA)back propagation (BP) neural network prediction model is inefficient in pretreating training samples and the structural design of hidden layers. The bestnormalized interval is determined using correlation coefficient regression analysis, the analytic expression of the number of neurons in the hidden layer is derived using the principle of statistics, and the corresponding range of the best number of neurons in a single hidden layer is proposed. The results show the following: (1) The bestnormalized interval of the training sample pretreatment is ［0.05, 0.95］. (2) The best number of neurons of a single hidden layer structure is in the interval of ［4, 7］, and a double hidden layer structure is more suitable for the case of neurons （> 4）. (3) The metabolism approach of selecting training samples can significantly reduce the timespace effect and human interference of foundation pit deformation. (4) The 42 (7, 8)1 GABP neural network model is constructed to predict the horizontal displacement of foundation pits with different depths, and the accuracy evaluation index shows that the prediction effect of the optimized GABP model is good, which provides references for engineering development. By comparing the optimized model with other models, its superior effect is validated.

Key words： foundation pit engineering; genetic algorithm-back propagation neural network; foundation pit monitoring; horizontal displacement; deformation prediction

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