Acoustic emission signatures for quantifying damage patterns in half grouted sleeve connection under tensile load

The half-grouted sleeve connection (HGSC) have been widely used as connection tools for prefabricated concrete (PC) buildings. It is crucial to maintain the safety and stability of the nodes and detecting internal defects in the grouting sleeve for the safety of the entire structure. Therefore, understanding the mechanical behavior and damage mechanism of HGSC is essential for the design and construction of PC. Currently, there are few on-site techniques applicable for monitoring the damage progress. The acoustic emission (AE) as a passive method shows great potential to characterize the damage in the HGSC. Therefore, in this paper, we propose to use AE to quantify the damage of the HGSC with defects under the tensile load. The artificial defects were introduced into HGSC with different defective rates. The monotonic tensile load was applied to each HGSC sample with a loading rate of 0.33 mm/s and tested to the failure. In this process, the AE signals were collected by AE transducers. For the classification and prediction of damage signals, we utilized a machine learning framework tailored for HGSC. This framework integrates unsupervised k-means++ cluster analysis and supervised K-Nearest Neighbors (K-NN) for damage classification and prediction. The experimental results showed that four AE signal clusters can be defined during the progress of tensile failure:(i) microcrack expansion;(ii) the friction signals of rebar and high-strength grouts; (iii)the generation of microcracks; (iv) plastic deformation of rebar. The corresponding AE signatures were identified. The K-NN model trained on the training data provides a high accuracy in predicting different class of defects. It was concluded that the proposed machine learning framework is a promising and powerful tool for realizing the on-site damage detection and prediction for HGSC. •AE signatures of rebar pullout and breakage in HGSC were identified.•The precursor AE signals were recommended for the on-site inspection.•Propose a machine learning framework to classify the AE signals and predict the damage.