土工基础

›› 2015, Vol. 29 ›› Issue (4): 61-64.

• 工程实录 • 上一篇    下一篇

基于BP模型与ARX模型的基坑变形预测研究

李上钦，罗武章，陈雄图，唐封强   

  1. （华南农业大学水利与土木工程学院，广州 510642）
  • 收稿日期:2014-07-23 出版日期:2015-08-20 发布日期:2015-11-06
  • 作者简介:李上钦(1989-)，工学学士，研究方向为测绘工程与土木工程。

Deep Excavation Deflection Prediction Using on BP and ARX Models

LI Shangqin， LUO Wuzhang， CHEN Xiongtu, TANG Fengqiang   

  1. (College of Water Conservancy and Civil Engineering, South China Agricultural University，Guangzhou 510642)
  • Received:2014-07-23 Online:2015-08-20 Published:2015-11-06

摘要： 随着建设工程施工的信息化与安全化，基坑的变形预测是基坑设计和施工的重要补充手段。基于BP人工神经网络模型以及时序分析ARX自回归各态历经模型，对基坑的沉降变形进行了预测，数据结果表明两种模型均能较好地对未来值进行较真实的预测；从BP模型与ARX模型的预测结果均方误差值大小的角度而言，BP模型的预测对于未来趋势的判断比ARX模型要更强一些。试验结果说明两种预测模型应用于实际工程的监测预测具有实际意义。

关键词: 基坑变形预测, BP模型, ARX模型, 效果比较

Abstract: With the development of data collection, transfer and the requirement of safety in the construction, the deflection monitoring of the deep excavation becomes an integral part of the design as well as construction. The neural network approach through Back Propagation (BP) model and Auto Regressive with eXtra Input Signal (ARX) model has been applied in the deep excavation deflection monitoring and prediction. The results indicate that both models can satisfactorily predict the construction deflections. However, from the mean square error point view, BP has a better prediction results compared with the predictions made by ARX model.

Key words: Deep Excavation Deflection Prediction, Back Propagation (BP) Model, Auto Regressive with eXtra Input Signal (ARX) Model, Learning Efficiency, Result Efficiency, Result Comparison