592 research outputs found

    The bond behaviour between concrete and corroded reinforcement: state of the art

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    The corrosion of reinforcing steel bar embedded in concrete leads to the bond deterioration. This literature review summarises the influence of corrosion on bond strength and bond-slip behaviour. The influence of corrosion on bond strength has been intensively investigated and the main influencing parameters, including the corrosion conditions of steel bars, the geometry and the corrosive environment, have been well recognized. Based on the previous investigations and the author’s experimental work, an improved bond strength model, which can account for various parameters and is proved to agree well with experimental results in the literature, is developed. The literature survey also indicates that the surface crack width is appropriate to be the governing parameter for the evaluation of bond strength. For the bond-slip behaviour of corroded RC, the published experimental results indicate that the bond-slip mechanism is similar to that of non-corroded RC, however, the researchers have different views regarding the influence of corrosion on some of the parameters that shape the bond-slip curves. A comprehensive bond-slip model for corroded RC has been developed by the authors considering various parameters, such as the confinements and the corrosion conditions of stirrups. This paper also reviews the bond behaviour of corroded RC under repeated loading. The research by the authors suggests that the repeated loading shows no significant influence on the bond strength of corroded RC, and the bond-slip behaviour is characterized by the progressive increase of residual slip, which is the same to that of non-corroded RC. To better understand the bond behaviour of corroded RC, the further studies are needed with respect to the influence of environment on the bond deterioration, the correlations between the bond behaviour and the surface crack width, and the bond-slip behaviour of corroded RC under repeated loading with various loading scenarios

    Effects of Stirrups on Bond Behavior Between Concrete and Corroded Steel Bars

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    Steel corrosion leads to the deterioration of bond between concrete and steel bars. The serviceability and ultimate strength of concrete elements within RC structures are hence affected. Many researchers have studied the bond behavior of corroded steel bars. However, very few studies have investigated the effects of confinements on the degradation of bond strength. The present paper proposed a new kind of beam specimen based on which the effects of stirrups on degradation of bond were investigated. The test results proved that stirrups can effectively increase the bond strength between concrete and corroded steel bars

    Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

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    Simultaneous EEG-fMRI acquisition and analysis technology has been widely used in various research fields of brain science. However, how to remove the ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge. Because it is impossible to obtain clean and BCG-contaminated EEG signals at the same time, BCG artifact removal is a typical unpaired signal-to-signal problem. To solve this problem, this paper proposed a new GAN training model - Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input to better combine the characteristics of the two types of signals, instead of using two independent models for bidirectional conversion as in the past. Furthermore, the model is decomposed into multiple independent convolutional blocks with specific functions. Through additional training of the blocks, the local representation ability of the model is improved, thereby improving the overall model performance. Experimental results show that, compared with existing methods, the method proposed in this paper can remove BCG artifacts more effectively and retain the useful EEG information.Comment: 8 pages, 5 figures, 1 tabl

    Detecting Slow Wave Sleep Using a Single EEG Signal Channel

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    Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods
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