Print Email Facebook Twitter Application of neural networks for the reliability design of a tunnel in karst rock mass Title Application of neural networks for the reliability design of a tunnel in karst rock mass Author Kovacevic, Meho Saša (University of Zagreb) Bacic, Mario (University of Zagreb) Gavin, Kenneth (TU Delft Geo-engineering) Date 2021 Abstract 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 of Tunnel Pecine 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. Subject KarstLimit statesNeural networkReliability methodsTunnel design To reference this document use: http://resolver.tudelft.nl/uuid:cc77096d-1e35-4ac2-a598-6661018a379f DOI https://doi.org/10.1139/cgj-2019-0693 Embargo date 2020-11-21 ISSN 0008-3674 Source Canadian Geotechnical Journal, 58 (4), 455-467 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2021 Meho Saša Kovacevic, Mario Bacic, Kenneth Gavin Files PDF cgj_2019_0693.pdf 5.77 MB Close viewer /islandora/object/uuid:cc77096d-1e35-4ac2-a598-6661018a379f/datastream/OBJ/view