Mechanical Properties and Elastic Modulus Prediction of Mixed Coal Gangue Concrete
Xipeng Qin, Zhengyi Xu, Mingyu Liu, Yingying Zhang, Yixiang Wang, Zhongnian Yang, Xianzhang LingCoal gangue, representing an industrial waste with the highest annual emissions and largest cumulative stocks worldwide, urgently requires resource utilization. This article uses mixed coal gangue aggregates (spontaneous-combustion coal gangue aggregate (SCGA) and rock coal gangue aggregate (RCGA)) as the research subject. The aim is to solve the technical problem of producing high-performance concrete with gangue instead of coarse aggregate. The research investigates the impact of various strength grades (C20, C30, C40, C50) and aggregate replacement ratios (0%, 20%, 40%, 60%, 80%, 100%) on the compressive strength of concrete. It explores the mechanical behaviors and properties of concrete mixed with coal gangue and develops a predictive model for its elastic modulus. The results show that (1) as the substitution rate of aggregates increases, the elastic modulus and compressive strength of the mixed coal gangue concrete significantly decrease. When the substitution rate is 100%, the elastic modulus and compressive strength of C30 concrete decrease by 3.5% and 11.3%, respectively, and the higher the grade, the more significant the reduction. For C50 concrete, the elastic modulus and compressive strength decrease by 10% and 35%, respectively. (2) A regression equation has been formulated to delineate the relationship between the compressive strength and axial compressive strength of mixed coal gangue concrete, taking into account the mix ratio of coal gangue and the compressive strength of standard concrete. This equation elucidates the correlation between the mechanical properties of concrete with varying coal gangue mix ratios and ordinary concrete across different strength grades. (3) Based on the correlation between elastic modulus and compressive strength, a prediction model for the elastic modulus of mixed gangue concrete was established, which effectively improved its prediction accuracy.