Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

Authors

  • Armin Rastbood
    Affiliation
    School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • Yaghoob Gholipour
    Affiliation
    School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • Abbas Majdi
    Affiliation
    School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
https://doi.org/10.3311/PPci.9700

Abstract

The paper describes an artificial neural network method(ANNM) to predict the stresses executed on segmental tunnellining. An ANN using multi-layer perceptron (MLP) is developed.At first, database resulted from numerical analyses wasprepared. This includes; depth of cover (H), horizontal to verticalstress ratio (K), thickness of segment (t), Young modulus ofsegment (E) and key segment position in each ring (θ) on thetunnel perimeter as input variables. Different types of stressesand extreme values of displacement have been considered asoutput parameters. Sensitivity analysis showed that the coverof the tunnel and key position are the most and less effectiveinput variables on output parameters, respectively. Resultsfor coefficient of determination (R2), variance accounted for(VAF), coefficient of efficiency (CE) and root mean squarederror (RMSE) illustrates a high accuracy of the presented ANNmodel to predict the stress types and displacements of segmentaltunnel lining.

Keywords:

artificial neural network, tunnel, segment, lining, yield criterion

Citation data from Crossref and Scopus

Published Online

2017-02-27

How to Cite

Rastbood, A., Gholipour, Y., Majdi, A. “Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network”, Periodica Polytechnica Civil Engineering, 61(4), pp. 664–676, 2017. https://doi.org/10.3311/PPci.9700

Issue

Section

Research Article