Precise and non-destructive approach for identifying the real concentration based on cured cemented paste backfill using hyperspectral imaging

Cemented paste backfill (CPB) is a technology that achieves safe mining by filling the goaf with waste rocks, tailings, and other materials. It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations. It promotes the development of a circular economy in mines through the development of low-grade resources and the resource utilization of waste, and extends the service life of mines. The mass concentration of solid content (abbreviated as “concentration”) is a critical parameter for CPB. However, discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf, which cannot be evaluated after the solidification of CPB. This paper innovatively provides anin-situnon-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging (HSI) technology. Initially, the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples. The results demonstrate that as the CPB concentration increases from 61wt% to 73wt%, the overall spectral reflectance gradually increases, with two distinct absorption peaks observed at 1407 and 1917 nm. Notably, the reflectance at 1407 nm exhibited a strong linear relationship with the concentration. Subsequently, the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms were employed to classify and identify different concentrations. The study revealed that, with the KNN algorithm, the highest accuracy was achieved whenK(number of nearest neighbors) was 1, although this resulted in overfitting. WhenK= 3, the model displayed the optimal balance between accuracy and stability, with an accuracy of 95.03%. In the SVM algorithm, the highest accuracy of 98.24% was attained with parametersC(regularization parameter) = 200 and Gamma (kernel coefficient) = 10. A comparative analysis of precision, accuracy, and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration. Thus, HSI technology offers an effective solution for thein-situ, non-destructive monitoring of CPB concentration, presenting a promising approach for optimizing and controlling CPB characteristic parameters.