ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-4-2024
https://doi.org/10.5194/isprs-annals-X-4-2024-15-2024
https://doi.org/10.5194/isprs-annals-X-4-2024-15-2024
18 Oct 2024
 | 18 Oct 2024

Analysis of Cement properties using Hyperspectral Remote Sensing methods

Ishtiaq Ahmed, Umesh Kumar Sharma, Pradeep Kumar Garg, and Aditya Kumar Thakur

Keywords: Building Material, Cement, NDT, Hyperspectral Remote Sensing, Spectral Properties

Abstract. Understanding building material properties can play a key role in analyzing the health of structural elements in buildings. However, the field and laboratory tests can be laborious, time-consuming, and cost-intensive. Generally, the standard tests employed for characterizing the material properties are destructive in nature, but in the recent past, non-destructive testing (NDT) has given us the ease of understanding the material properties. Hyperspectral remote sensing technology has been exploited as one of the NDT methods for assessing the properties of building components. This study aims to investigate the quality information about cement using the hyperspectral remote sensing method and correlate it with the results of traditional quality testing methods. The materials used in the experiments included Ordinary Portland Cement (OPC), Portland Pozzolana Cement (PPC), and White Portland Cement (WPC). The cement samples were studied and experimented with for the identification of their types, and their temporal variability and changes in their spectral properties on which the strength of the samples depended were further checked. The study concluded that for OPC, PPC, and WPC, the absorption range is 1970 nm to 2020 nm, which is mainly due to the presence of different chemical compounds in cement. Also, the spectral reflectance decreases with time, and it can be concluded that in the present work, the strength of cement decreases with the decrease in spectral reflectance. Hence, Cement properties can be found using hyperspectral remote sensing data.