Clustering analysis of acoustic emission signals in the monitoring of stone monuments: case of the freeze‒thaw deterioration of tuffs

Abstract

AbstractAcoustic emission (AE) technology is a promising technique for monitoring cultural monuments due to its characteristic ability to reflect status changes and perceive the development process of deterioration and damage even before their visual appearance. This study was established on the motivation of providing basic data and a methodology that can improve the signal processing, characteristics analysis and classification for the AE technique in the long-term in-situ monitoring of deterioration processes, starting from the freeze‒thaw deterioration of tuff monuments at the Chengde site. AE monitoring was carried out with an indoor freeze–thaw deterioration experiment. As a result, a set of procedures and related methodology is proposed based on the hit-based AE waveform parameters for denoising and classification of monitored AE signals by applying hierarchical cluster analysis, k-means clustering, distribution statistics, etc. The clustering results show that some signals may indicate deterioration and signals with certain characteristics are more likely to occur at a particular deterioration phase. Signals characterized by the significant absolute energy (ABE) are presumed to be related to the propagation of cracks to the outer layer. Signals characterized by a higher indirect parameter RA (Rise time divided by peak amplitude) value may connect with the opening/closing of microcracks in the earlier phase of deterioration prior to the exposure of visible surface cracks. The peak frequency (PF) is likely to decrease as the deterioration proceeds.