Experimental dataset for loads on hard rock shotcrete tunnel linings in a laboratory environment
Journal article, 2024

To improve the understanding of failure mechanisms and behaviour of hard rock tunnel linings, local load conditions were experimentally simulated and monitored using a comprehensive set of sensors and imaging techniques. The data includes measurements from distributed optical fiber sensors (DOFS), high-resolution cameras, load cells, pressure cells and LVDTs. Two types of loads were examined: rock block load and bond loss combined with a distributed load over the area of lost bond. The experiments replicated these conditions and were conducted in a laboratory setting where the shotcrete and substrate rock were substituted by cast fiber reinforced concrete (FRC) and cast concrete, respectively. To facilitate the loads, concrete cones were cast into the substrate concrete and pushed through the FRC top layer with a hydraulic jack to mimic rock block loads. To simulate the bond loss and the associated distributed load, lifting bags were installed and inflated between the FRC layer and substrate cast concrete. All specimens were monitored using DOFS embedded in two perpendicular directions and in two layers in the top FRC layer. In addition, the hydraulic jack was instrumented with LVDTs and load cells to measure displacement and load, and the pressure in the lifting bags was monitored using a pressure cell. Two cameras continuously photographed the top surface of the FRC layer, which had been painted with a speckle pattern, during the testing and the pictures can be used for digital image correlation (DIC). Lastly, each specimen was scanned with a 3D scanner prior to and after testing of the specimen.

Block load in tunnels

Distributed optical fiber sensors

Distributed load in tunnels

Digital image correlation

Author

August Jansson

Chalmers, Architecture and Civil Engineering, Structural Engineering

Ignasi Fernandez

Chalmers, Architecture and Civil Engineering, Structural Engineering

Carlos Gil Berrocal

Chalmers, Architecture and Civil Engineering, Structural Engineering

Rasmus Rempling

NCC

Chalmers, Architecture and Civil Engineering, Structural Engineering

Data in Brief

23523409 (eISSN)

Vol. 57 110920

SensIT – Verifiering och prognostisering av tekniska funktionskrav på tunnelinfattning av betong – sensorbaseras prognosmetod med artificiell intelligens

Swedish Transport Administration (TRV2021/66599), 2021-11-01 -- 2024-12-31.

Subject Categories

Infrastructure Engineering

DOI

10.1016/j.dib.2024.110920

Related datasets

Experimental data for loads on tunnel linings including distributed optical fiber sensing and digital image correlation [dataset]

DOI: 10.5878/dvcn-bg03

More information

Latest update

10/18/2024