Naslov
Monitoring of long term deformations in Bobova tunnel

Autori
Kovačević, Meho Saša ; Gavin, Ken ; Stipanović Oslaković, Irina ; Librić, Lovorka ; Rossi, Nicola

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Road and Rail Infrastructure V, Proceedings of the Conference CETRA 2018 / Lakušić, Stjepan - Zagreb : Građevinski fakultet Sveučilišta u Zagrebu, 2018, 1507-1513

Skup
5th International Conference on Road and Rail Infrastructure (CETRA 2018)

Mjesto i datum
Zadar, Hrvatska, 17.05.2018. - 19.05.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Bobova tunnel, long-term deformations, karst, monitoring

Sažetak
The 189.50 m long Bobova tunnel constructed in 2005 is located on the D404 state highway and passes under a part of the Vežica – Sušak town area in Rijeka. The overburden above the tunnel pipe is between 2 m to 18 m thick. The rock mass along the tunnel route is made up of karst deposits (transient carbonate breccia, dolomite and limestone interlinked with rudist limestones). Extensive geotechnical instrumentation was installed in the tunnel by researchers at the faculty of Civil Engineering at the University of Zagreb. This consisted of: measurements in the tunnel area from the ground surface using inclinometers for horizontal, and sliding micrometers for vertical soil displacements, measuring displacements around underground openings using sliding micrometers supplemented with scanning of the tunnel interior using laser scanners, and measuring the stress and deformations in elements of support complex by pressure cells and measuring anchors. Data collected during construction and in the twelve years in which the three-lane highway tunnel has been operational, presented in this paper reveal that deformations and stresses in Bobova tunnel have continued to increase with time. The possible role of the shotcrete lining, rock creep and rock-bolt corrosion in the ongoing deformations are discussed. The application of continuous monitoring data from instruments as inputs for numerical model training using a machine learning model, with the objective to improve the predictions of existing probabilistic failure models is considered. The ultimate aim of this work is to develop improved predictive maintenance plans.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

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