Application of neural networks for the reliability design of a tunnel in karst rock mass

Scientific paper - Original scientific paper

• Author: Meho Saša Kovačević
• Author: Mario Bačić
• Author: Kenneth Gavin
• Author's institution: University of Zagreb; Faculty of Civil Engineering
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Civil Engineering

Abstract (english)

This paper offers a solution to overcome time-consuming numerical analysis for the evaluation of the impact of tunnel construction in a complex karst environment by implementing Monte-Carlo simulation (MCS) using a neural network (NN) tool. The rock mass is described using three parameters, Geological Strength Index, the uniaxial compression strength of the intact rock and the Hoek-Brown parameter for the intact rock mi. By using their probabilistic distribution as an input, a developed neural network NetTUNN produces probabilistic distributions of tunnel crown displacement, rock bolt axial load and shotcrete uniaxial compression stress. A full MCS is then applied on these NetTUNN outputs to determine the reliability index and probability of failure for the relevant limit states. To demonstrate the potential of NN in tunnel design, a case study tunnel Pećine in Croatia is used, where the NetTUNN-assisted MCS assessment served as a benchmark to evaluate approximate reliability assessment techniques. It was shown that the developed NN can be used as an accurate surrogate model for determination of probabilistic distributions of tunnel design parameters. Further, it was shown that approximate reliability assessment techniques generally overestimate the reliability index and underestimate the probability of failure when compared to the NetTUNN assisted MCS.

• Keywords (english): neural network; reliability design; tunnel; numerical modelling; karst
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Journal title: Canadian geotechnical journal
• Numbering: vol. n/a, pp. 1-15
• e-ISSN: 1208-6010
• DOI: https://doi.org/10.1139/cgj-2019-0693
• URN:NBN: urn:nbn:hr:237:802172
• Publication: 2019
• Type of resource: Text
• Access conditions: Open access
• Created on: 2024-09-04 10:22:32