Identifying locally induced loads in hard rock tunnel linings by distributed optical fibre sensors
Licentiate thesis, 2024

In an infrastructure system, tunnels enable fast means of transportation in
impassable terrain or city areas. However, inspections and maintenance can be
expensive due to long down time and, in hard rock tunnels, the complexity of
the structure may lead to over designing.
This thesis presents an experimental series aiming to reproduce loading conditions
acting on shotcrete tunnel linings in hard rock. The specimens consisted of
two concrete layers, a top fibre reinforced concrete layer simulating a shotcrete
lining, and a 300 mm thick substrate layer simulating rock. The top FRC layer
response was measured using strain sensing distributed optical fibre sensors
(DOFS) installed in two layers. A full factorial design with four factors and
two levels was applied, where the varied factors were load conditions, load size,
lining thickness and tensile bond strength between lining and substrate.
From the experimental results, it is concluded that using DOFS to monitor
loads in tunnel linings are suitable as the global behaviour is captured at
low load magnitudes and that performance indicators, such as cracks, can be
used to identify the load type.
The work presented is part of a research project aiming to develop a machine
learning model, trained on synthetic data from finite element analyses
calibrated with the acquired experimental results, to identify loads and predict
the structural integrity of a tunnel lining. The experimental results will ultimately
be used to verify the machine learning model and, thus, implications
for future finite element and machine learning models are discussed based on
the experimental results.

Distributed optical fibre sensors

Structural health monitoring

Tunnel lining experiments

SB-H2
Opponent: Eric Hegardt, Trafikverket, Svergie

Author

August Jansson

Chalmers, Architecture and Civil Engineering, Structural Engineering

August Jansson, Andreas Sjölander, Ignasi Fernandez, Carlos G. Berrocal, Rasmus Rempling, Investigating the response of tunnel linings through distributed optical fiber sensing

August Jansson, Carlos Gil Berrocal, Ignasi Fernandez, Rasmus Rempling, Strain distributions for shotcrete failure in hard rock tunnels

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

Publisher

Chalmers

SB-H2

Online

Opponent: Eric Hegardt, Trafikverket, Svergie

More information

Latest update

4/15/2024