干湿循环硫酸盐侵蚀粉煤灰混凝土耐久性分析及抗蚀等级预测

Durability Analysis of Fly Ash Concrete Under Dry-Wet Cycle Sulfate Erosion and Prediction of Anticorrosion Grade

针对云南楚雄州地区含盐地质环境及公路隧道衬砌混凝土所面临的耐久性问题,探究粉煤灰对混凝土抗硫酸盐侵蚀性能的影响,采用干湿循环和硫酸盐侵蚀耦合的试验方法,根据混凝土表观形貌与质量损失率分析其劣化机制,并建立基于Weibull函数的混凝土侵蚀损伤模型与试验结果进行对比。结果表明:1)侵蚀初期钙矾石和石膏填充于孔隙之间,试件质量增加;侵蚀后期,侵蚀产物膨胀应力超过混凝土结构内应力时,裂缝开始形成和扩展,混凝土受损严重,试件质量减小。2)适量粉煤灰提升了混凝土的抗蚀等级,其中以掺入20%粉煤灰提升效果最优;当粉煤灰掺入过量(25%)时,混凝土抗蚀等级下降。3)以耐蚀系数为损伤度建立Weibull分布函数抗蚀等级预测模型,能够较好地反映粉煤灰混凝土受侵蚀损伤劣化规律,各组模型相关度R2均在0.95以上,模型预测与试验数据误差率在15%以内,精度较高。

To address the durability challenges faced by concrete linings in highway tunnels in the salt-containing geological environment of Chuxiong Prefecture,Yunnan Province,China,the authors investigate the influence of fly ash on the resistance of concrete to sulfate erosion.Experimental methods coupling dry-wet cycles with sulfate erosion are employed.The deterioration mechanism is analyzed based on the surface morphology and mass loss rate of concrete.Additionally,a concrete erosion damage model based on the Weibull function is established and compared with experimental results.The results indicate the following:(1)In the initial stages of erosion,ettringite and gypsum fill the voids,leading to an increase in specimen mass.(2)In the later stages of erosion,when the expansion stress of erosion products exceeds the internal stress of the concrete structure,cracks appear and propagate,resulting in severe damage to the concrete and a decrease in specimen mass.The addition of an appropriate amount of fly ash enhances the anticorrosion grade of concrete,with the most optimal improvement observed when 20%fly ash is added.However,when fly ash is added excessively (25% ), the anticorrosion grade of concrete decreases. (3) The predictive model for anticorrosion grade based on the Weibull distribution function using the erosion coefficient as damage severity effectively reflects the degradation pattern of fly ash concrete under erosion. The correlation coefficients (R2 ) of all model sets are above 0.95, with prediction errors relative to experimental data within 15% , indicating high precision.