IMPREGNATION RATE ESTIMATION OF POLYMER IMPREGNATED CONCRETE USING SOUND PROPAGATION CHARACTERISTICS AND MACHINE LEARNING

Abstract

Polymer impregnated concrete (PIC) has excellent durability against impact and abrasion and is applied where robustness is required. An important indicator for measuring PIC durability is the impregnation rate of the polymer. A conventional method for measuring the impregnation rate involves comparing the weight before and after impregnation. While this method of comparing weights is straightforward, it requires extensive weighing equipment to be applied to large PIC forms. Such weighing is a challenge in terms of time and risk involved because it requires tasks such as lifting. In this study, we hypothesize that there is a specific relationship between the impregnation rate and the sound propagation characteristics. A device consisting of a speaker and a microphone was prototyped, and variations in the sound propagation characteristics were estimated using machine learning. The results show that the impregnation rate can be estimated with practical accuracy.