Hyperspectral Remote Sensing of TiO₂ Concentration in Cementitious Material Based on Machine Learning Approaches

Abstract

Titanium dioxide (TiO2) is a photocatalyst that can be used to remove nitrogen oxide (NOx). When applied to cementitious materials, it reacts with photons in sunlight or artificially generated light to reduce the concentration of particulate matter in the atmosphere. The concentration of TiO2 applied to the cementitious surface is difficult to quantify in a non-destructive manner after its application; however, knowledge of this residual amount is important for inspection and the evaluation of life expectancy. This study proposes a remote sensing technique that can estimate the concentration of TiO2 in the cementitious surface using a hyperspectral sensor. In the experiment, cement cores of varying TiO2 concentration and carbon contents were prepared and the surfaces were observed by TriOS RAMSES, a directional hyperspectral sensor. Machine-learning-based algorithms were then trained to estimate the TiO2 concentration under varying base material conditions. The results revealed that the best-performing algorithms produced TiO2 concentration estimates with a ~6% RMSE and a correlation close to 0.8. This study presents a robust machine learning model to estimate TiO2 and activated carbon concentration with high accuracy, which can be applied to abrasion monitoring of TiO2 and activated carbon in concrete structures.

Keywords

• hyperspectral remote sensing
• TiO₂
• cementitious material
• machine learning