Modeling approaches to pressure balance dynamic system in shield tunneling
来源期刊：中南大学学报(英文版)2014年第3期
论文作者：LI Shou-ju(李守巨) YU Shen(于申) QU Fu-zheng(屈福政)
文章页码：1206 - 1216
Key words：intelligent modeling; neural network; pressure balance system; excavation chamber; analytically modeling approach
Abstract: In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network (ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling (MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.
LI Shou-ju(李守巨)1, YU Shen(于申)1, QU Fu-zheng(屈福政)2
(1. State Key Laboratory of Structural Analysis for Industrial Equipment (Dalian University of Technology),
Dalian 116024, China;
2. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)
Abstract:In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network (ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling (MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.
Key words:intelligent modeling; neural network; pressure balance system; excavation chamber; analytically modeling approach
