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In order to enhance the practical application of carbon-fiber-reinforced concrete (CFRC) in engineering, it is necessary to study the damage mechanism of CFRC. Experimental research on the mechanical properties of CFRC under multiple strain rates was conducted. Five different fiber contents were analyzed to study the compressive strength and tensile strength of CFRC, and the damage characteristics of CFRC under multiple strain rates were analyzed based on failure modes and energy changes. An energy-based damage constitutive model was established. The results showed the following: (1) When the carbon fiber content was 0.4%, CFRC had the best comprehensive performance, with a 15.02% increase in compressive strength and a 51.12% increase in tensile strength. With the increase in strain rate, the compressive strength of the concrete increased. (2) Under high strain rates, carbon fiber significantly enhanced the compressive strength of the concrete, and the input energy, elastic strain energy, and dissipated energy increased. The peak value of the elastic strain energy conversion rate increased, and the minimum value of the dissipated energy conversion rate decreased. (3) Under the same strain rate, the CFRC had a larger inflection point of dissipated energy corresponding to the strain compared to the reference group of concrete during the loading process. A constitutive model for CFRC was established based on damage mechanics and probability statistics. The research results will provide theoretical references for the application of carbon-fiber-reinforced concrete. [ABSTRACT FROM AUTHOR]

There are many technical difficulties in antireaction construction of cast-in-place onland final joints of immersed tunnels, i.e., multiple underwater operations, complex procedures, and quality control. Therefore, a case study is conducted on the Hanjiang river-crossing immersed tunnel in Yuliangzhou, Xingyang, China. No independent antireaction structure is arranged, and the friction of the pipe joint is used as antireaction function. The results demonstrate the following: (1) The friction coefficient between pebble foundation bed and immersed tunnel and the friction coefficient between pebble foundation bed and immersed tunnel considering the influence of back-silting are obtained by laboratory and field tests. (2) The principle of friction antireaction of the final joint is studied, the land final joint of the immersed tunnel with friction antireaction is developed for the first time, the end antireaction structure is eliminated, prestressed coarse reinforcement is adopted as a temporary connection measure, and self-lubricating thrust bearing is set up to eliminate the shear force. The engineering practice validates that the force and the tensioning amount of the pipe joint meet the requirements, and the land final joint of immersed pipe tunnel with friction-antireaction is safe and reliable. [ABSTRACT FROM AUTHOR]

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems, yet still faces many challenges. Building on convolutional neural networks (CNNs), medical image segmentation has achieved tremendous progress. However, owing to the locality of convolution operations, CNNs have the inherent limitation in learning global context. To address the limitation in building global context relationship from CNNs, we propose LGNet, a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper. Specifically, we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features. LGNet has two key insights: (1) We bridge two-branch to learn local and global features in an interactive way; (2) we present a novel multi-feature fusion model (MSFFM) to leverage the global contexture information from transformer and the local representational features from convolutions. Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse, ACDC and MOST. Specifically, LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15% on Synapse, of 91.70% on ACDC, and of 95.56% on MOST. Meanwhile, the inference speed attains at 172 frames per second with 2 2 4 × 2 2 4 input resolution. The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation. [ABSTRACT FROM AUTHOR]