Acoustic Signal-Based Binary Classification for Brick Wall Inspection by Hammering Test

I was a co-author of the paper “Acoustic Signal-Based Binary Classification for Brick Wall Inspection by Hammering Test,” which won the Best Paper Award at CANDAR 2025. The research is significant for non-destructive evaluation of brick structures, especially in earthquake-prone regions like Nepal, and achieved a defect recognition accuracy of over 95% using acoustic signal analysis. The work was carried out collaboratively by researchers from Tribhuvan University (structural inspection and signal processing), Utsunomiya University (acoustic analysis and sensing), Port Electronics Co., Ltd. (engineering and instrumentation), and Chuo University (machine learning and pattern recognition), combining expertise across civil engineering, acoustics, and AI to develop a practical inspection approach for brick buildings.