Abstract

Traditional mining methods damage the cultivated land and produce gangue waste that often contaminates the environment. Yet, these problems can be mitigated by transforming the waste into gangue-based cemented backfill material (GCBM), whose mechanical properties are crucial for surface protection. Therefore, in this study, an intelligent model based on laboratory tests was developed to evaluate the GCBM's mechanical properties. The strength tests and polynomial response surface model (PRSM) were used to analyze the non-linear correlation between the influencing factors and the uniaxial compressive strength (UCS). Meanwhile, the importance of multidimensional factors was analyzed by the mean impact value, revealing that concentration and gangue proportion are the most sensitive factors. In addition, an intelligent response surface model (IRSM) based on support vector regression model was constructed by enhancing an optimization algorithm with chaotic mapping and adaptive methods. The performance of the traditional PRSM and the novel IRSM was compared, and the IRSM was validated. The IRSM can predict UCS more efficiently and effectively than the traditional PRSM under high-dimensional factors, with R2 of 0.96 and MBE of 0.05. This indicated that the IRSM has the potential to promote coal mine waste reduction and environmental protection.

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