A novel separation technique of flexural loading-induced acoustic emission sources in railway prestressed concrete sleepers

This study determines the effectiveness and suitability of acoustic emission in order to monitor the structural health condition of railway pre-stressed concrete sleepers since the acoustic emission sensing provides effective means for detecting cracking events and quantifying the crack density. The aim of this study is two-fold. First, we establish a novel acoustic emission source separation that offers the possibility for analyzing the content of each source separately. This study is thus the first to demonstrate that these sources can be separated from each other by employing a classification algorithm based on decision trees. Hence, a technically sound decision can be made regarding the severity of the damage and appropriate measures can be taken. Second, the paper presents a new method for compressing the essential acoustic emission information of the loaded structure for further post-processing. Data compression of 70 % with respect to the full set of features is achieved through the elimination of those acoustic emission signatures that yield the largest mutual correlation with other signatures. The reduced set of acoustic emission signatures (only two uncorrelated features) still possesses the essential information of structural health of the structure of interest at a cost of a relatively small drop in classification accuracy from around 96% to 84%. The proposed method is validated experimentally on full-scale pre-stressed concrete sleepers under positive and negative bending moment configurations under static three-point bending tests, which correspond to anticipated failure modes at rail seats by industry.