基于神经网络的桥下开挖竖向位移预测

doi:10.13438/j.cnki.jdzk.2022.02.009

吉首大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (2): 53-59.DOI: 10.13438/j.cnki.jdzk.2022.02.009

基于神经网络的桥下开挖竖向位移预测

李家稳，王崇旭，王浩

（北方工业大学土木工程学院，北京 100144）

出版日期:2022-03-25 发布日期:2022-07-14

Prediction of Vertical Displacement of Bridge Excavation Based on Neural Network

LI Jiawen,WANG Chongxu,WANG Hao

(School of Civil Engineering,North China University of Technology,Beijing 100144,China)

Online:2022-03-25 Published:2022-07-14
About author:LI Jiawen (1962-),male,was born in Beijing City,research fellow,doctor,post-doctoral;his main research directions are crossing railway project design,construction related.
Supported by:
National Natural Science Foundation of China （52178378）

摘要/Abstract

摘要：基于BP神经网络方法，对桥下开挖的竖向变形作了预测.从不同土层的土的参数敏感性分析结果可知,竖向位移只对首层土的参数变化很敏感,由此建立了桥下开挖竖向位移预测的神经网络，其控制参数为首层土的粘聚力、内摩擦角、弹性模量、泊松比、开挖深度和开挖长度.选用B-R方法进行迭代计算，并对建立的神经网络进行验证，结果表明该模型的预测准确度较高.

关键词： 桥下开挖, 变形预测, 神经网络, 敏感性

Abstract: Based on BP neural network method,the vertical deformation prediction of excavation under the bridge is studied.Firstly,the parameter sensitivity of different soil layers is studied and analyzed.It is concluded that the vertical displacement is only sensitive to the parameter change of the first layer of soil.And then,the control parameters of the neural network are determined as cohesion,internal friction angle,elastic modulus,Poisson's ratio,excavation depth,and excavation length of the first layer of soil.The B-R method is used for iterative calculation Verification of the established network show that the prediction accuracy of the model is high.

Key words: excavation under the bridge, deformation prediction, neural network, sensitivity

李家稳, 王崇旭, 王浩. 基于神经网络的桥下开挖竖向位移预测[J]. 吉首大学学报（自然科学版）, 2022, 43(2): 53-59.

LI Jiawen, WANG Chongxu, WANG Hao. Prediction of Vertical Displacement of Bridge Excavation Based on Neural Network[J]. Journal of Jishou University(Natural Sciences Edition), 2022, 43(2): 53-59.

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基于神经网络的桥下开挖竖向位移预测

Prediction of Vertical Displacement of Bridge Excavation Based on Neural Network

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