Measurements and monitoring
Measurement of ice pressure on a concrete dam with a prototype ice load panel
The development and installation of a prototype ice load panel and measurements of ice load from February 2016 to February 2018 at the Rätan hydropower dam in Sweden is presented. The design of the 1 × 3 m2 panel enables direct measurement of ice pressure on the concrete surface is based on previous experience from similar measurements with sea ice. Important features of the design are sufficient height and width to reduce scale effects and to cover the ice thickness and variations in water level. The Rätan dam was chosen based on several criteria so that the ice load is considered to be reasonably idealized against the dam structure. For the three winters 2016, 2016/2017, 2017/2018, the maximum ice load recorded was 161 kN/m, 164 kN/m and 61 kN/m respectively. There were significant daily fluctuations during the cold winter months, and the daily peak ice loads showed a visual correlation with the daily average temperature and with the daily pattern of operation of the power station with its corresponding water level variations.
TACK project: Tunnel and bridge automatic crack monitoring using deep learning and photogrammetry
Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. Some preliminary findings of an ongoing research project, named TACK, is presented. It combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. Some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labelled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).
Contact: Andreas Sjölander (profile pages)
Full-scale test of an unreinforced concrete dome plug for the spent nuclear fuel repository
In the planned Swedish repository for spent nuclear fuel, plugs are designed to close the deposition tunnels. The outer part of these plugs consists of a concrete dome made with selfcompacting concrete, designed to have low pH to reduce negative effects on the bentonite clay buffer. A full-scale test has been performed to evaluate the performance of the plug, to test the installation and to verify underlying design assumptions. The behaviour of the concrete dome is evaluated based on measurements, from casting the concrete until it was subjected to 4 MPa hydrostatic water pressure.
Contact: Richard Malm (profile pages)