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.
Highlights
Considering the tested TiO2 concentrations, which ranged from 0% to 25%, models with an RMSE of less than 1% can be regarded as having high estimation precision
The results demonstrate the robustness of the suggested model (Lasso regression), the following limitations appl ness of the suggested model (Lasso regression), the following limitations apply: A certain portion in ofthe theestimation uncertaintyis in the estimation is driven byofthe heterogeneity o portion of the uncertainty driven by the heterogeneity specimen surfaces, whichdifferences include locational differences onsurface a single surface in surfaces, which include locational on a single specimen inspecimen addition to differences between different specimens
This study explored spectral reflectance characteristics according to the TiO2 and activated carbon ratios in concrete specimens using a hyperspectral sensor
Summary
Air pollution levels are continually rising due to the rapid development of industries and cities. Nitrogen oxide (NOx ) is major factor of air pollution that is mostly generated by vehicle exhaust and factory emissions in urban areas. NOx is a concern as it can lead to the generation of photochemical smog, acid rain, and particulate matter (e.g., PM 2.5). Photochemical smog is composed of nitrogen dioxide and hydrocarbon molecules contained in VOCs (volatile organic compounds) and can cause eye irritation. Acid rain, which can cause various health problems and severe damage to man-made structures, occurs when NOx reacts in the atmosphere and oxidizes with nitric acid in clouds [1]
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