摘要
为了进一步揭示低温硫酸盐侵蚀条件下水泥砂浆抗折强度的发展规律,本文以不同配合比砂浆试件为研究对象,进行了20℃、10℃、5℃条件下的硫酸盐侵蚀试验,并对不同龄期的抗折强度进行了测试。试验结果表明:侵蚀过程中砂浆试件抗折强度呈现出先上升后下降的变化趋势,且明显受到温度影响,温度越低,侵蚀情况越严重,主要表现为整体强度下降,上升段可达到的最大值减小,劣化开始的时间提前。为考虑温度的影响,在Irassar模型的基础上,引入温度修正系数,提出了低温硫酸盐侵蚀过程中抗折强度的预测模型。模型的计算值与实测值吻合度更高,最大误差为9%,平均误差为2.3%,故该模型可较为准确的预测水泥砂浆在5~20℃硫酸盐腐蚀环境下抗折强度的发展规律。
In order to investigate the developing law of flexural strength of cement mortar under sulfate corrosion circumstances with low temperature,the sulfate corrosion tests were carried out under temperature of 20℃,10℃and 5℃,and the flexural strength of cement mortar under different ages was also tested.The results show that the flexural strength of mortar specimens increases rapidly first and then decreases,and it is obviously affected by the temperature.The lower the temperature is,the more serious the erosion is,which is mainly manifested by the decrease of the overall strength and the maximum value of the ascending segment decrease,and the beginning time of deterioration becomes earlier.Based on the Irassar model,aprediction model of flexural strength was proposed under sulfate corrosion circumstance at low temperature.The calculated value by the presented model is more consistent with the measured value than the existing model,and the maximum error is 9% and the average error is 2.3%.Therefore,it can be concluded that the proposed model can accurately predict the variation of the flexural strength for cement mortar under sulfate corrosion at a temperature range of 5-20℃.
作者
谢超
王起才
于本田
李盛
张戎令
王云天
XIE Chao;WANG Qicai;YU Bentian;LI Sheng;ZHANG Rongling;WANG Yuntian(College of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Key Laboratory of Roadand Bridge and Underground Engineering of Gansu Province,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《复合材料学报》
EI
CAS
CSCD
北大核心
2019年第6期1520-1527,共8页
Acta Materiae Compositae Sinica
基金
国家自然科学基金(51768033)
长江学者和创新团队发展计划(IRT_15R29)
甘肃省高校协同创新科技团队支持计划(2017C-08)
关键词
低温
硫酸盐腐蚀
水泥砂浆
抗折强度
预测模型
low temperature
sulfate attack
cement mortar
flexural strength
prediction model
