The quality of grouted sleeve has a significant influence on the performance of the sleeve splice. Incompactness of the infilled grout is inevitable in sleeve grouting. To investigate the tensile behavior of grouted splice sleeves due to different grout compactness, monotonic tensile tests on grouted splice sleeve connectors were performed at grout compactness of 100%, 90%, 70%, and 50%, respectively. The bond-slip analytical model of rebar-grout was deduced by fitting the tensile test data, and the formula for the tensile capacity of the grouted splice sleeve was proposed in the paper. The results show that the tensile strength of the splice sleeve reduces as the grout compactness decreases. It was found from the experiment that the calculated values of tensile capacity are in good agreement with the experimental values. The proposed formula can be adopted in determining whether reinforcing remedies or re-grouting should be taken in the case of incompact grout in grouted splice sleeve connectors.
This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition.
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