The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

This paper aims to develop the AE non-destructive testing equipment and signal analysis method for external tendon of PSC (Pre-Stressed Concrete) bridge. The AE NDT equipment, which has developed in this research, is composed of AE sensor, signal receiver, hitting device, driving part and main frame. Defective and non-defective specimens, designed to emulate signal characteristics for external duct with void(25%, 50% and 100%) or not, were constructed and tested to verify the effectiveness of AE NDT equipment. Test results and AE parameters comparisons between defective and non-defective specimens are presented.