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

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 Pecine, 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.

Automated summary

This paper proposes a method to predict the long-term vertical settlement performance of a tunnel in soft rock masses by incorporating a Burger's creep viscous-plastic constitutive law and utilizing neural network technology to model post-construction deformations. A particle swarm algorithm is used to estimate the most probable rheological parameter set based on long-term in-situ monitoring data from a real tunnel in Croatia. The methodology demonstrates potential in predicting the future behavior of tunnels in complex rock mass environments.