95 research outputs found
Nonlinear fatigue damage of cracked cement paste after grouting enhancement
Grouting reinforcement is an important part of modern engineering and has grown in popularity due to the benefits of grouting enhancement on cyclic loading. Understanding the fatigue mechanism of grouting-enhanced structures is vital to the design and the long-term stability analysis of such structures. In this study, the fatigue mechanical properties of cracked cement paste after epoxy resin grouting enhancement under different cyclic conditions were investigated in the laboratory and an inverted S-shaped curve was proposed to describe the damage accumulation. The test results indicated that the fatigue axial deformation can be divided into three stages: the initial stage, constant velocity stage and accelerating stage. The irreversible deformation can be used to describe the damage accumulation. The fatigue process is significantly affected by the upper limit stress level and the stress amplitude. In addition, the exponential relationship between stress amplitude and fatigue life was obtained. The proposed S-shaped curve was validated by an experimental fatigue strain test. The tests result upon various load conditions and crack types represented a good agreement with the predicted data
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network
This paper presents a novel network structure with illumination-aware gamma
correction and complete image modelling to solve the low-light image
enhancement problem. Low-light environments usually lead to less informative
large-scale dark areas, directly learning deep representations from low-light
images is insensitive to recovering normal illumination. We propose to
integrate the effectiveness of gamma correction with the strong modelling
capacities of deep networks, which enables the correction factor gamma to be
learned in a coarse to elaborate manner via adaptively perceiving the deviated
illumination. Because exponential operation introduces high computational
complexity, we propose to use Taylor Series to approximate gamma correction,
accelerating the training and inference speed. Dark areas usually occupy large
scales in low-light images, common local modelling structures, e.g., CNN,
SwinIR, are thus insufficient to recover accurate illumination across whole
low-light images. We propose a novel Transformer block to completely simulate
the dependencies of all pixels across images via a local-to-global hierarchical
attention mechanism, so that dark areas could be inferred by borrowing the
information from far informative regions in a highly effective manner.
Extensive experiments on several benchmark datasets demonstrate that our
approach outperforms state-of-the-art methods.Comment: Accepted by ICCV 202
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