[关键词]
[摘要]
深基坑施工过程中,对围护结构进行水平位移监测是保证施工安全的重要措施之一,而分析预测围护结构的变形趋势更是重中之重。对此,本文对围护结构自动化监测设备进行了实地调研,提出了结合BP人工神经网络模型对围护结构水平位移进行多步滚动预测的方法。以南宁地铁5号线车站深基坑施工围护结构真实监测数据为训练样本,对样本数据分别进行三种模式学习,第一种,不同桩学习后在同一时间的预测结果作对比;第二种,同一根桩在不同时间间隔样本学习后在同一时间的预测结果作对比;第三种,同一根桩在实现多步滚动预测后对预测结果作对比。结果表明:三种模式的预测误差均可可满足要求。为实现围护结构变形自动预测提供实用性强、可信度高的方法。
[Key word]
[Abstract]
In the process of deep foundation pit construction, the horizontal displacement monitoring of the retaining structure is one of the important measures to ensure the construction safety, and the analysis and prediction of the deformation trend of the retaining structure are the most important. In this regard, this paper conducted a field investigation on the automatic monitoring equipment of the enclosure structure, and proposed a method of multi-step rolling prediction of the horizontal displacement of the enclosure structure combined with the BP artificial neural network model. The real monitoring data of the deep foundation pit construction enclosure structure of the Nanning Metro Line 5 station was taken as the training sample, three modes of learning were performed on the sample data. The first one was to compare the prediction results of different piles at the same time after learning; secondly, one was to compare the prediction results of the same pile at the same time after sample learning at different time intervals; the third was to compare the prediction results of the same pile after multi-step rolling prediction is achieved. The results show that the prediction errors of the three modes can meet the requirements. It provides a method with strong practicability and high credibility to realize the automatic prediction of the deformation of the enclosure structure.
[中图分类号]
TU473.2
[基金项目]
考虑时空效应的软弱围岩隧道施工失稳演化机理与控制理论(51678164),国家自然科学基金项目(面上项目,重点项目,重大项目)