Publikationer vid avdelningen för Betongbyggnad

Senast publicerade artiklar från avdelningen för Betongbyggnad

• Assessing the Effectiveness of Dowel Bars in Jointed Plain Concrete Pavements Using Finite Element Modelling

  Aggregate interlocking and dowel bar systems are the two primary mechanisms in a jointed plain concrete pavement for transferring the wheel loads from the loaded slab to the adjacent unloaded slab, avoiding critical stresses and excessive deformations across the joint. Aggregate interlocking is suitable for small joint openings, while the dowel bar provides effective load transmission for both smaller and wider joint openings. In this study, a three-dimensional finite element model was developed to investigate the structural performance of dowelled jointed plain concrete pavements. The developed model was compared with an analytical solution, i.e., Westergaard’s method. The current study investigated the effectiveness of the dowel bars in jointed plain concrete pavements considering the modulus of elasticity and the thickness of the base layer, as well as dowel bar diameter and length. Furthermore, the load transfer efficiency (LTE) of a rounded dowel bar was compared with that of plate dowel bars (i.e., rectangular and diamond-shaped dowel bars) of a similar cross-sectional area and length. This study showed that the LTE was enhanced by 4% when the base layer’s modulus of elasticity increased from 450 MPa to 6000 MPa, while the increase in stress was 23%. A 1.2% improvement in the LTE and a 2.1% reduction in flexural stress were observed as the base layer’s thickness increased from 100 to 250 mm. Moreover, increasing the dowel bar’s diameter from 20 mm to 38 mm enhanced the LTE by 4.3% and 3.8% for base layer moduli of 450 MPa and 4000 MPa, respectively. The corresponding rise in stresses was 10% and 5%. The diamond-shaped dowel bar of a 50 × 32 mm size showed a 0.48% increase in the LTE, while sizes of 100 × 16 mm and 200 × 8 mm reduced the stress 6.7% and 23.1%, respectively, compared to that in the rounded dowel bar. With rectangular dowel bars, a 4% rise in the stress was noted compared to that with the rounded dowel bar. Increasing the length of the diamond-shaped dowel bar slightly improved the LTE but had no impact on the stress in the concrete slab. The findings from this study can help highway engineers improve pavements’ durability, make cost-effective decisions, contribute to resource savings in large-scale concrete pavement projects, and enhance the overall quality of infrastructure.

• Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach

  Dissolved oxygen (DO) is a vital water quality index influencing biological processes in aquatic environments. Accurate modeling of DO levels is crucial for maintaining ecosystem health and managing freshwater resources. To this end, the present study contributes a Bayesian-optimized explainable machine learning (ML) model to reveal DO dynamics and predict DO concentrations. Three ML models, support vector regression (SVR), regression tree (RT), and boosting ensemble, coupled with Bayesian optimization (BO), are employed to estimate DO levels in the Mississippi River. It is concluded that the BO-SVR model outperforms others, achieving a coefficient of determination (CD) of 0.97 and minimal error metrics (root mean square error = 0.395 mg/L, mean absolute error = 0.303 mg/L). Shapley Additive Explanation (SHAP) analysis identifies temperature, discharge, and gage height as the most dominant factors affecting DO levels. Sensitivity analysis confirms the robustness of the models under varying input conditions. With perturbations from 5% to 30%, the temperature sensitivity ranges from 1.0% to 6.1%, discharge from 0.9% to 5.2%, and gage height from 0.8% to 5.0%. Although the models experience reduced accuracy with extended prediction horizons, they still achieve satisfactory results (CD > 0.75) for forecasting periods of up to 30 days. The established models also exhibit higher accuracy than many prior approaches. This study highlights the potential of BO-optimized explainable ML models for reliable DO forecasting, offering valuable insights for water resource management.

• Experimental investigation on prevalent local failure mechanisms in hard rock tunnel linings using distributed optical fibre sensors

  In today's hard rock tunnel construction, the most common support system consists of rock bolts and shotcrete linings. The support system is effective to build, and structural safety has empirically been established. However, the utilization rate of shotcrete linings is usually unknown as no method exists today that determines the type and magnitude of loads acting on the linings. This paper investigates the implementation of distributed optical fiber sensors (DOFS) as a promising solution for monitoring of local loads in shotcrete tunnel linings. This approach enables the identification of local loads, facilitating targeted inspections in areas with deviating measurements and allowing for more informed repair and maintenance decisions. In the study, two typical local load conditions in shotcrete linings were analysed using strain measurements from DOFS installed in experimental specimens designed to replicate sections of tunnel linings. The results revealed that the examined load conditions can be distinguished based on the measured strains. While the lining thickness had a significant effect on the peak load capacity, the roughness of the substrate influenced the strain distribution in linings subjected to bending. It was also shown that DOFS outside the loaded area could detect load-induced strains for shear loaded specimens at low load levels, but not for flexurally loaded specimens.

• Mesoscopic modeling the interaction of two attached-wall cavitation bubbles

  A hybrid thermal lattice Boltzmann cavitation model based on a nonorthogonal framework is developed to investigate the interaction of two attached-wall cavitation bubbles. The interaction modes are systematically analyzed, with an emphasis on how varying contact angles influence the flow and temperature distributions, as well as the evolution of wall heat flux under strong and weak interaction conditions. Bubbles formed on the hydrophobic surface display increased contact radius and greater curvature radii compared to those on the hydrophilic wall, leading to greater volumes but weaker collapse intensity. The growth rate of the bubble equivalent radius for the weak interaction modes consistently follows the relation U∝2p∞/3ρl. Additionally, bubble coalescence occurs at the interface regions along the hydrophobic surface, altering the final collapse dynamics and resulting in distinct temperature and velocity distributions. Finally, the instantaneous heat flux characteristics are explored. Due to differences in the contact points motion rate and microjet angle with the solid wall, the peak value and number of heat flux peaks vary on walls with different wettability.

• 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.