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

Meho Saša Kovačević, Mario Bačić, Kenneth Gavin, Irina Stipanovič

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Article number103838
Number of pages15
JournalTunnelling and Underground Space Technology
Publication statusPublished - 2021


  • Soft rock tunneling
  • Long-term deformation
  • Rheological parameters
  • Neural network
  • Particle swarm optimization
  • Tunnel monitoring

Fingerprint Dive into the research topics of 'Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm optimized neural network'. Together they form a unique fingerprint.

Cite this