Effect of longitudinal joints on seismic response of the large shield tunnel in liquefiable soils

In view of the damages to shield tunnels in recent strong earthquakes, this paper focuses on the effect of longitudinal joints on the seismic response of the shield tunnel in liquefiable soils. First, the normal and shear behaviors of high-strength concrete interface at the joint were studied through direct shear tests under monotonic and cyclic loadings. The results show that the flatness rather than the roughness is the dominant factor influencing the behavior of the interface. As the confining stress and cyclic number increase, the total contact area increases, thus resulting in the increases of the normal stiffness and the friction coefficient. Based on the testing results, the seismic response of a shield tunnel in liquefiable soils was studied using numerical analysis. According to the results, the longitudinal joints have a significant effect on the seismic response of the shield tunnel. When neglecting the joints, the tensile stresses in concrete, the tunnel uplift and the surface deformation remarkably decrease, and the shield tunnel becomes less vulnerable to earthquakes. Due to the soil liquefaction, the joints located near the waist are more likely to get opened and corresponding bolts bear large tensile forces, which are the main reasons for the common damages to shield tunnels. Based on this experimental and numerical study, the effect of the joints and the seismic damages of the large shield tunnel are better understood, consisting an important step in the development of appropriate specifications for the seismic design of the large shield tunnel

In Situ Synthesis of Reduced Graphene Oxide-Reinforced Silicone-Acrylate Resin Composite Films Applied in Erosion Resistance

The reduced graphene oxide reinforced silicone-acrylate resin composite films (rGO/SAR composite films) were prepared by in situ synthesis method. The structure of rGO/SAR composite films was characterized by Raman spectrum, atomic force microscope, scanning electron microscopy, and thermogravimetric analyzer. The results showed that the rGO were uniformly dispersed in silicone-acrylate resin matrix. Furthermore, the effect of rGO loading on mechanical properties of composite films was investigated by bulge test. A significant enhancement (ca. 290% and 320%) in Young’s modulus and yield stress was obtained by adding the rGO to silicone-acrylate resin. At the same time, the adhesive energy between the composite films and metal substrate was also improved to be about 200%. Moreover, the erosion resistance of the composite films was also investigated as function of rGO loading. The rGO had great effect on the erosion resistance of the composite films, in which the Rcorr (ca. 0.8 mm/year) of composite film was far lower than that (28.7 mm/year) of pure silicone-acrylate resin film. Thus, this approach provides a novel route to investigate mechanical stability of polymer composite films and improve erosion resistance of polymer coating, which are very important to be used in mechanical-corrosion coupling environments

Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach

In recent years, the United States has witnessed a significant surge in the popularity of vaping or e-cigarette use, leading to a notable rise in cases of e-cigarette and vaping use-associated lung injury (EVALI) that caused hospitalizations and fatalities during the EVALI outbreak in 2019, highlighting the urgency to comprehend vaping behaviors and develop effective strategies for cessation. Due to the ubiquity of social media platforms, over 4.7 billion users worldwide use them for connectivity, communications, news, and entertainment with a significant portion of the discourse related to health, thereby establishing social media data as an invaluable organic data resource for public health research. In this study, we extracted a sample dataset from one vaping sub-community on Reddit to analyze users' quit-vaping intentions. Leveraging OpenAI's latest large language model GPT-4 for sentence-level quit vaping intention detection, this study compares the outcomes of this model against layman and clinical expert annotations. Using different prompting strategies such as zero-shot, one-shot, few-shot and chain-of-thought prompting, we developed 8 prompts with varying levels of detail to explain the task to GPT-4 and also evaluated the performance of the strategies against each other. These preliminary findings emphasize the potential of GPT-4 in social media data analysis, especially in identifying users' subtle intentions that may elude human detection.Comment: Accepted for the AI Applications in Public Health and Social Services workshop at the 22nd International Conference on Artificial Intelligence in Medicine (AIME 2024