北京交通大学学报2018，Vol.42Issue(1)：18-24,7.DOI:10.11860/j.issn.1673-0291.2018.01.003
基于神经模糊推理系统的盾构施工地表沉降预测
Prediction of ground surface settlement induced by shield tunneling construction based on neural fuzzy inference system
摘要
Abstract
Ground surface settlement induced by shield tunnel construction is affected by stratum conditions and shield tunneling parameters.The relationship between factors is complex.The traditional methods are hard to meet the prediction and forecasting demand for the safety monitoring demand during the process of shield construction because of the lack of direct consideration of tunneling parameters.Adaptive Neural Fuzzy Inference System (ANFIS) is a kind of fuzzy smart model based on neural network and the fuzzy rules are generated automatically via the subtractive clustering data subdivision technology so that the well-defined physics meaning can be obtained for network node and weight.In addition,ANFIS has a good ability of data adaptive and fuzzy knowledge representation,which is suitable for prediction and forecasting of multivariate nonlinear system.Taking the section tunnel between Dongfengbeiqiao Station and Jingshunlu Station of Beijing Subway Line 14 as an example,factors such as buried depth,standard penetration,earth pressure of chamber,rotating speed of cutter head,advance rate,the torque variation,shield tunneling thrust and synchronous grouting are selected as the input variables so that the prediction model for the maximum settlement of ground surface is established.The results show that the model has the characteristic of small amount of calculation and strong generalization ability and high precision computation,which provides a new technical solution for the surface settlement prediction and forecasting.关键词
盾构隧道/地表沉降/神经模糊推理系统/减法聚类/预测模型Key words
shield tunnel/ground surface settlement/neural fuzzy inference system/subtractive clustering/prediction model分类
交通工程引用本文复制引用
李兴春,李兴高..基于神经模糊推理系统的盾构施工地表沉降预测[J].北京交通大学学报,2018,42(1):18-24,7.基金项目
国家重点研发计划(2015CB057802)Key R&D Plan(2015CB057802) （2015CB057802）
