Paper Information

Journal:   INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING   2017 , Volume 51 , Number 1; Page(s) 71 To 78.
 
Paper: 

PREDICTION OF STRUCTURAL FORCES OF SEGMENTAL TUNNEL LINING USING FEM BASED ARTIFICIAL NEURAL NETWORK

 
 
Author(s):  RASTBOOD ARMIN*, MAJDI ABBAS, GHOLIPOUR YAGHOOB
 
* SCHOOL OF MINING, COLLEGE OF ENGINEERING, UNIVERSITY OF TEHRAN, TEHRAN, IRAN
 
Abstract: 

The critical parameters in investigating the performance of designed support system of tunnels are the structural forces i.e. peak values of axial and shear forces, and moments. In this research, a complete database was firstly prepared using finite element method. Using finite element models, we modeled the segmental tunnel lining that was composed of 5+1 concrete segments in one ring. Then, an artificial neural network (ANN) model of multi-layer perceptron was developed to estimate the lining structural forces. To do this, the number of neurons and their arrangement were optimized based on the obtained minimum values from the root mean square error (RMSE). To prove the efficiency of the developed ANN model, we calculated the coefficient of efficiency (CE), determination coefficient (R2), variance account for (VAF), and RMSE values. The results demonstrated a promising precision and high efficiency of the presented ANN method for estimating the structural forces of tunnel lining composed of concrete segments instead of alternative costly and tedious solutions. Finally, the sensitivity analysis showed that among the input variables, the tunnel cover is the most influencing variable on the lining structural forces. However, other input variables, i.e. lateral earth pressure and key segment position were the second important variables affecting the induced stresses on tunnel lining.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK, LINING, MULTI-LAYER PERCEPTRON, SEGMENT, TUNNEL
 
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