4/6/2025, 4:25:42 AM 星期日
基于PSO-BP神经网络的基坑周边地面沉降预测方法研究
CSTR:
作者单位:

吉林大学,吉林大学,中国建筑东北设计研究院有限公司,吉林大学,吉林大学

中图分类号:

TU433

基金项目:

吉林省省校共建计划专项“油页岩地下原位开发利用示范工程”(编号:SF2017-5-1)


Study on the Prediction Methods of Ground Settlement Surrounding the Foundation Pit Based on PSO-BP Neural Network
Author:
Affiliation:

College of Construction Engineering, Jilin University,College of Construction Engineering, Jilin University,China Northeast Architectural Design and Research Institute Co., Ltd.,College of Construction Engineering, Jilin University,College of Construction Engineering, Jilin University

Fund Project:

Jilin provincial school co-construction project special

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [18]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    基坑工程施工过程中的周边地面沉降直接关系到周围建筑物的安全,本文根据上海前滩地区某基坑工程的历史监测数据、施工工况和周边地层参数等多源数据对基坑周边地面沉降进行监测和预测。以PSO-BP神经网络为基础,通过将基于时序和基于沉降影响因素的网络模型对比发现:二者预测结果误差较小且基于时序的神经网络预测精度更高,说明利用PSO-BP神经网络能够很好地对基坑周边地面沉降进行分析与预测。为了综合考虑时间效应和空间效应的影响,在基于沉降影响因素的预测模型的基础上加入历史监测数据作为模型输入层进行优化,结果表明:优化后的PSO-BP神经网络模型具有更小的相对误差范围和更高的预测精度,在基坑周边地面沉降预测中有很好的应用前景。

    Abstract:

    The surrounding ground settlement in the process of the foundation pit construction is directly related to the safety of the surrounding buildings. In this paper, the ground settlement surrounding the foundation pit was monitored and predicted according to the historical monitoring data, the construction conditions and the surrounding stratum parameters of a foundation pit project in Qiantan district of Shanghai. Based on PSO-BP neural network, this paper compares the network model based on the time series with that based on the settlement influence factors. It is found that the prediction error of these 2 models is small and the prediction precision of neural network based on the time series is higher, which means that PSO-BP neural network can be used to analyze and predict the ground settlement surrounding the foundation pit. In order to comprehensively consider the time effect and space effect, the historical monitoring data is added as the input layer of prediction model for optimization on the basis of prediction model of settlement influencing factors. The results show that the optimized PSO-BP neural network model has a smaller relative error range and a higher prediction precision, and it has good application prospect in the prediction of ground settlement surrounding the foundation pit.

    参考文献
    [1] Ping Xu,Yuewang Han,Honghai Duan,Shitao Fang. Environmental Effects Induced by Deep Subway Foundation Pit Excavation in Yellow River Alluvial Landforms[J]. Geotechnical and Geological Engineering.2015, 33:1587–1594.
    [2] 龚晓南,宋二祥,郭红仙,徐明.基坑工程实例[M].北京:中国建筑工业出版社,2010.
    [3] PB Attwell. Soil movement induced by tunneling and their effects or pipe line sand structures[M]. Blackie: Chapman and Hall, 1986: 20-46.
    [4] 曹祖宝.人工神经网络方法在基坑变形预测中的应用研究[J].探矿工程(岩土钻掘工程),2008(05):38-40+43.
    [5] 赵富章. 上海某基坑工程周边地面沉降监测及预测模型研究[D].吉林大学,2017.
    [6] RUMELHART D, DAVID E , MCCLELLAND J,et al. Parallel distributed processing:
    explorations in the microstructure of cognition:psychological and biological models[M]. Cambridge:MIT press,1986.
    [7] 董青青,梁小丛.基于优化的BP神经网络地层可钻性预测模型[J].探矿工程(岩土钻掘工程),2012,39(11):26-28.
    [8] KENNEDY J,EBERHART R.Particle swarm optimizationProceedings of IEEE International Conference on Neural Networks[C] .Perth: Neural Networks,1995,4(8):1942-1948.
    [9] 苏自武,杨甘生,陈礼仪.利用人工神经网络原理对钻井中钻速的预测[J].探矿工程(岩土钻掘工程),2005(01):48-50.
    [10] 李钰曼. 改进的PSO-RBF神经网络在复杂工业过程中的应用[D].河北科技大学,2018.
    [11] 王新,候风艳.基于改进的PSO-BP神经网络的无刷直流电机控制[J].电子测量技术,2017,40(02):10-14.
    [12] Clerc M.. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization[C]. Proc. 1999 Congress on Evolutionary Computation, Washington, DC, pp 1951-1957. Piscataway, NJ: IEEE Service Center.
    [13] 史峰, 王小川, 郁磊, 等. MATLAB 神经网络30个案例分析[M]. 北京: 北京航空航天大学出版社, 2010: 1?18.
    [14] 刘燕,刘国彬,孙晓玲,王海平.考虑时空效应的软土地区深基坑变形分析[J].岩土工程学报,2006(S1):1433-1436.
    [15] 杨林松. 基坑施工引起坑周土体应力与位移场变化特征研究[D],同济大学,2007.
    [16] 姜晨光,范好政,盖玉松.基坑工程周边地面沉降规律的初步分析[J].探矿工程(岩土钻掘工程),2002(06):5-6.
    [17] 胡仁兵. 深基坑周边建筑物沉降预测分析[D].兰州交通大学,2009.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

陈晨,靳成才,赵富章,等.基于PSO-BP神经网络的基坑周边地面沉降预测方法研究[J].钻探工程,2018,45(12):47-52.
CHEN Chen, JIN Cheng-cai, ZHAO Fu-zhang, et al. Study on the Prediction Methods of Ground Settlement Surrounding the Foundation Pit Based on PSO-BP Neural Network[J]. Drilling Engineering, 2018,45(12):47-52.

复制
分享
文章指标
  • 点击次数:753
  • 下载次数: 885
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2018-05-28
  • 最后修改日期:2018-05-28
  • 录用日期:2018-08-20
  • 在线发布日期: 2018-12-06
文章二维码