Soft Computing Based Prediction of Support Pressure in Tunnels
Rakesh Kumar Dutta1, Viswas Nandakishor Khatri2, Sanjay Kumar3
1Rakesh Kumar Dutta, Department of Civil Engineering, National Institute of Technology, Hamirpur, India.
2Viswas Nandakishor Khatri, Department of Civil Engineering, IIT Dhanbad, India.
3Sanjay Kumar, Department of Civil Engineering, National Institute of Technology, Hamirpur, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 856-863 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8045088619/2019©BEIESP | DOI: 10.35940/ijeat.F8045.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of Tunnel support pressure up to an accurate and reliable degree is difficult, but of utmost importance. Empirical models are available with different set of parameters, mostly are based on the rock classification parameters. A feed forward neural network based predictive models from the data collected from literature for the Himalayan tunnels have been developed. The input variables in the developed neural network models were depth of over burden, radius of tunnel, normalised closure. The fourth input variable was rock mass quality or rock mass number or rock mass rating. The output was a support pressure. Sensitivity analysis relating the variables affecting the support pressure has been performed. The developed neural network models were compared with models developed based on the multiple linear regression analysis as well as with empirical models already available in literature. Finally, model equations have been presented based on the connection weight.
Keywords: Tunnel support pressure; ANN; MVLRA; Sensitivity Analysis; Rock classification parameters.