Non-Destructive Estimation of Moisture Percentage in Fired Red Brick Using Digital Image Processing and Artificial Intelligence

Abstract

In this study, we present a novel methodology for reducing uncertainties in detecting high-permeability regions in bricks by integrating brick imagery, color theory, unsupervised learning, and petrophysical concepts. Leveraging smartphone technology, our methodology identifies and analyzes moisture regions in red bricks, demonstrating its potential as a cost-effective tool for moisture characterization. This approach complements specialized moisture detection devices, highlighting the versatility of existing technology. Applied within the context of traditional red brick manufacturing in San Agustín Yatareni, Oaxaca, México, our results show that this methodology effectively identifies moisture-related anomalies, with water absorption values verified according to the NMX-C-404-ONNCCE-2012 and NMX-C-037-ONNCCE-2013 Mexican standards. We observed a significant inverse correlation between luminosity and moisture content, and a direct correlation between hue and moisture content. These findings suggest a reliable, non-invasive indicator of moisture levels, potentially improving the longevity of construction materials. The broader applicability of this approach in construction material analysis, particularly for bricks incorporating organic fibers, underscores its value as a tool for quality control. Furthermore, the integration of smartphone technology and interdisciplinary techniques contributes to advancing sustainable construction practices and improving material durability.