Publications at the division of Concrete Structures

Latest publications from the division of Concrete Structures

Systematic Literature Search and Meta Regression of Measured Static Ice Loads on Concrete Dams
This study presents a systematic literature review of ice load measurements on dams. Several hypotheses about the relationship between the maximum ice load and external variables are tested using regression analysis on the data collected from the literature. The performed tests show that ice thickness, water level change category, and dam height are factors that have a significant and relevant relationship with the magnitude of all measured ice loads. The ice thickness is the only tested variable that also shows a significant and relevant relationship with differences in ice load between winters at one dam. The variation in recorded ice load from several sensor positions at one dam during the same winter is considerable. Generally, the difference between the sensor area and the structure–ice interaction area is large, resulting in extensive extrapolation and uncertainties regarding the representativeness of the measured results.
Modeling transient flow dynamics around a bluff body using deep learning techniques
The significance of understanding the flow past a bluff body (BB) lies in its relevance to ocean, structural, and environmental applications. Capturing the transient flow behaviors with fine details requires extensive computational power. To address this, the present study develops an improved method for modeling the complex flow dynamics around a BB under steady and unsteady conditions. It is a deep learning (DL)-enhanced reduced-order model (ROM) that leverages the strengths of proper orthogonal decomposition (POD) for model reduction, convolutional neural network-long short-term memory (CNN-LSTM) for feature extraction and temporal modeling, and Bayesian optimization for hyperparameter tuning. The model starts with dimensionality reduction, followed by DL optimization and forecasting, and terminates with flow field reconstruction by combining dominant POD modes and predicted amplitudes. The goal is to establish a DL-driven ROM for fast and accurate modeling of the flow evolution. Based on the comparison of millions of data samples, the predictions from the ROM and CFD are considerably consistent, with a coefficient of determination of 0.99. Furthermore, the ROM is ∼10 times faster than the CFD and exhibits a robust noise resistance capability. This study contributes a novel modeling approach for complex flows, enabling rapid decision-making and interactive visualization in various applications, e.g., digital twins and predictive maintenance.
Effect of Rotating Magnetic Field on the Thermocapillary Flow Instability in a Liquid Bridge
The stability of thermocapillary flow in a liquid bridge under a transverse rotating magnetic field (RMF) was numerically investigated by the linear stability analysis using the spectral element method. Three commonly used RMF models, namely, the infinite model, the simplified finite model and the Φ1-Φ2 model, are employed to describe the RMF and their results are compared. Additionally, for the Φ1-Φ2 model, the uniform and non-uniform RMF were also compared. The numerical results show that with the increase of magnetic Taylor number Ta, the critical Marangoni number (Mac) for the three RMF models increases firstly, then decreases sharply to a minimum, finally increases again when the RMF is strong enough to suppress the radial and axial convection induced by thermocapillary force. Two transitions between the wavenumber k=1 and k=2 mode are observed with increasing Ta. The results obtained by the simplified finite model are in good agreement with those of the Φ1-Φ2 model, however, the infinite model has a significant deviation compared to the Φ1-Φ2 model. Besides, the results indicate that the non-uniform RMF has a relatively weak action compared with the uniform RMF.
On the Design of Permanent Rock Support Using Fibre-Reinforced Shotcrete
Fibre-reinforced shotcrete (sprayed concrete) is one of the major components in the support system for tunnels in hard rock. Several empirical design methodologies have been developed over the years due to the complexity and many uncertainties involved in rock support design. Therefore, this paper aims to highlight how the choice of design methodology and fibre type impacts the structural capacity of the lining and the emission of greenhouse gases (GHG). The paper starts with a review of different design methods. Then, an experimental campaign is presented in which the structural performance of shotcrete reinforced with various dosages of fibres made of steel, synthetic and basalt was compared. A case study is presented in which the permanent rock support is designed based on the presented design methods. Here, only the structural requirements were considered, and suitable dosages of fibres were selected based on the experimental results. The emission of GHG was calculated for all design options based on environmental product declarations for each fibre type. The result in this paper indicates that synthetic fibres have the greatest potential to lower the emissions of GHG in the design phase. Moreover, the choice of design method has a significant impact on the required dosage of fibres.
Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.