Applied Vision

Assessing Compressive Strength of Reclaimed Clay Bricks Using SWIR Hyperspectral Imaging and Deep Learning

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Abstract

A non-destructive approach is proposed to assess the compressive strength of reclaimed bricks using short-wave infrared (SWIR) hyperspectral imaging (HSI) and a spectral–spatial 1D-Convolutional Neural Network (CNN). Hyperspectral images of 60 bricks, capturing both outer (weathered) and inner (pristine) surfaces, were analyzed. Regression reached R2 = 0.625, while a three class (low, medium, high) compressive strength classification achieved 83 % pixel level accuracy. At the brick level, aggregating predictions with a majority-vote scheme attained an accuracy of 91 % for outer and 98 % for inner surfaces. Score-CAM identified key wavelengths around 1200–1400 nm (moisture) and 2300–2500 nm (clay minerals) as driving the predictions. The results demonstrate that SWIR HSI can capture mineral- and moisture-related signatures relevant to compressive strength, offering a rapid, non-destructive screening tool for reclaimed bricks.

How to Cite:

Andreu, J., Jernej, M., Klammer, M. & Kromoser, B., (2026) “Assessing Compressive Strength of Reclaimed Clay Bricks Using SWIR Hyperspectral Imaging and Deep Learning”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 92-96.