Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm optimized neural network

• Published in: Tunnelling and underground space technology Vol. 110; p. 103838
• Main Authors: Kovačević, Meho Saša, Bačić, Mario, Gavin, Kenneth, Stipanović, Irina
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2021; Elsevier BV
• Subjects: Algorithms; Constitutive models; Deformation; Long-term deformation; Mathematical models; Monitoring; Neural network; Neural networks; New Austrian Tunnelling Method; Parameter estimation; Particle swarm optimization; Railway tunnels; Rheological parameters; Rheological properties; Rock masses; Rocks; Soft rock tunneling; Studies; Tunnel construction; Tunnel monitoring; Tunnels; Underground construction; Tunnel monitoring; Long-term deformation; Soft rock tunneling; Neural network; Particle swarm optimization; Rheological parameters
• ISSN: 0886-7798; 1878-4364
• DOI: 10.1016/j.tust.2021.103838

•A neural network is developed as a surrogate tool fed by a numerical dataset.•PSO utilizes monitoring data to estimate most probable rheological parameters.•The methodology is validated on two adjacent tunnels in karstic rock mass.•Numerical results agreed well with the long-term monitored settlements. The continuous monitoring of long-term performance of tunnels constructed in soft rock masses shows that the rock mass deformations continue after construction, albeit at a rate that reduces with time. This is in contrast with NATM postulates which assume deformation stabilizes shortly after tunnel construction. This paper proposes the prediction of long-term vertical settlement performance of a tunnel in soft rock mass, through the inclusion of a Burger’s creep viscous-plastic constitutive law to model post-construction deformations. To overcome issues related to the complex characterization of this constitutive model, a neural network NetRHEO is developed and trained on a numerically obtained dataset. A particle swarm algorithm is then employed to estimate the most probable rheological parameter set, by utilizing the long-term in-situ monitoring data from several observation points on a real tunnel. The paper demonstrates the potential of the proposed methodology, using displacement measurements of two adjacent tunnels in karstic rock mass in Croatia. The complex interaction of a railway tunnel Brajdica and a road tunnel Pećine, conditioned by the character of the surrounding rock mass as well by the chronology of their construction, was evaluated to predict the future behavior of these tunnels.