Research on pore characteristics and macroscopic performance quantitative prediction of desert sand recycled aggregate concrete based on NMR and grey theory - Xiumei Zheng; Wenbang Zhu; Yali Cao; Xinjie Wang; Enze Hao; Dali Zhang; Wentao Chu; Baochen Tang (PLoS ONE, 2026)
Excessive extraction of river sand and the accumulation of construction waste severely restrict the green development of civil engineering. The resource utilization of desert sand (DS) and recycled aggregates (RA) is of great significance for the sustainable construction in arid desert areas of Xinjiang. To reveal the mechanism of synergistic regulation of concrete properties by the combined control of calcined mica hydroxide (CLDHs), desert sand and recycled aggregates, and to establish a quantitative correlation between microstructure and macroscopic properties, this paper uses the CLDHs dosage, DS substitution rate, and RA substitution rate as test parameters to prepare desert sand recycled aggregate concrete (DSRAC). Through testing methods such as compressive strength, rapid chloride ion migration, nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and X-ray diffraction (XRD), the macroscopic properties and microstructural evolution laws of the materials are systematically analyzed, and a quantitative performance prediction system is constructed based on grey entropy correlation analysis and GM (1,4) model. The results show that CLDHs, DS, and RA achieve synergistic optimization of pore structure through physical filling, ionic curing, and secondary hydration; when the CLDHs dosage is 6%, the DS and RA substitution rates are both 30%, the comprehensive performance of the concrete is the best, and the 28 d compressive strength is basically the same as the control group, with a 33.09% reduction in chloride ion diffusion coefficient. The internal pores of DSRAC are mainly gel pores, and the total porosity and transitional pore ratio have a significant impact on the macroscopic properties, with a correlation coefficient greater than 0.8. The GM (1,4) prediction model established based on key pore structure parameters has a relative error of less than 8% and high accuracy. The research results can provide theoretical basis and technical support for the preparation, performance evaluation, and engineering application of green recycled concrete in arid saline areas.