Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm optimized neural network. (April 2021)
- Record Type:
- Journal Article
- Title:
- Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm optimized neural network. (April 2021)
- Main Title:
- Assessment of long-term deformation of a tunnel in soft rock by utilizing particle swarm optimized neural network
- Authors:
- Kovačević, Meho Saša
Bačić, Mario
Gavin, Kenneth
Stipanović, Irina - Abstract:
- Highlights: 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. Abstract: 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 theHighlights: 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. Abstract: 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. … (more)
- Is Part Of:
- Tunnelling and underground space technology. Volume 110(2021)
- Journal:
- Tunnelling and underground space technology
- Issue:
- Volume 110(2021)
- Issue Display:
- Volume 110, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 110
- Issue:
- 2021
- Issue Sort Value:
- 2021-0110-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-04
- Subjects:
- Soft rock tunneling -- Long-term deformation -- Rheological parameters -- Neural network -- Particle swarm optimization -- Tunnel monitoring
Tunneling -- Periodicals
Underground construction -- Periodicals
Tunnels -- Periodicals
Underground areas -- Periodicals
624.193 - Journal URLs:
- http://www.sciencedirect.com/science/journal/08867798 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tust.2021.103838 ↗
- Languages:
- English
- ISSNs:
- 0886-7798
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9071.405000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 15803.xml