Under Sulfate Dry–Wet Cycling: Exploring the Symmetry of the Mechanical Performance Trend and Grey Prediction of Lightweight Aggregate Concrete with Silica Powder Content

Abstract

In order to improve the mechanical properties and durability of lightweight aggregate concrete in extreme environments, this study utilized Inner Mongolia pumice as the coarse aggregate to formulate pumice lightweight aggregate concrete (P-LWAC) with a silica powder content of 0%, 2%, 4%, 6%, 8%, and 10%. Under sulfate dry–wet cycling conditions, this study mainly conducted a mass loss rate test, compressive strength test, NMR test, and SEM test to investigate the improvement effect of silica powder content on the corrosion resistance performance of P-LWAC. In addition, using grey prediction theory, the relationship between pore characteristic parameters and compressive strength was elucidated, and a grey prediction model GM (1,3) was established to predict the compressive strength of P-LWAC after cycling. Research indicates that under sulfate corrosion conditions, as the cycle times and silica powder content increased, the corrosion resistance of P-LWAC showed a trend of first increasing and then decreasing. At 60 cycles, P-LWAC with a content of 6% exhibited the lowest mass loss rate and the highest relative dynamic elastic modulus, compressive strength, and corrosion resistance coefficient. From the perspective of data distribution, various durability indicators showed a clear mirror symmetry towards both sides with a silica powder content of 6% as the symmetrical center. The addition of silica fume reduced the porosity and permeability of P-LWAC, enhanced the saturation degree of bound fluid, and facilitated internal structural development from harmful pores towards less harmful and harmless pores, a feature most prominent at the 6% silica fume mixing ratio. In addition, a bound fluid saturation and pore size of 0.02~0.05 μm/% exerted the most significant influence on the compressive strength of P-LWAC subjected to 90 dry–wet cycles. Based on these two factors, grey prediction model GM (1,3) was established. This model can accurately evaluate the durability of P-LWAC, improving the efficiency of curing decision-making and construction of concrete materials.