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大气环境下轻骨料混凝土耐久性能研究综述 被引量:4

Review on Durability of Lightweight Aggregate Concrete in Atmospheric Environment
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摘要 介绍了国内外学者在大气环境下轻骨料混凝土耐久性能方面的工作及研究成果,揭示了轻骨料混凝土与普通混凝土碳化机理的差异,系统分析了影响碳化的主要因素,阐述了现有碳化深度预测模型的优缺点。结果表明:现有研究广泛采用快速碳化试验,对轻骨料混凝土实际工程碳化规律的探索主要集中在20世纪;基于试验研究的宏观预测模型较多,考虑微结构内CO_2扩散机制的理论模型较少;大气环境下结构服役寿命预测模型缺乏相关试验验证。建议今后重点从细观、宏观2个层面进一步探索快速碳化及大气环境下轻骨料混凝土劣化规律,基于扩散机制及化学反应动力学建立碳化理论模型,分析复杂环境作用和环境-荷载耦合作用下构件的时变性能特点。 The work and research results on durability of lightweight aggregate concrete in atmospheric environment by domestic and foreign scholars were introduced. The difference of carbonation mechanism between lightweight aggregate concrete and normal concrete was analyzed. Main factors influencing the carbonation of lightweight aggregate concrete were expounded in details. The merits and disadvantages of existing prediction models were elaborated. The results show that the existing researches mainly focus on rapid carbonization tests, while studies of actual structures are mainly conducted in the 20th century. The prediction models are mainly established based on experiments rather than theoretical analysis. Finally, there is a lack of experimental verification for carbonization predictions in atmospheric environment. It is suggested that the researches should be conducted in the perspective of macro-scale and meso- scale. Further research on lightweight aggregate concrete time-varying regularity in the real and accelerated environments necessary. The theoretical models for lightweight aggregate concrete carbonation reaction need to be established based on chemical kinetics and diffusion mechanism. Durability of lightweight aggregate concrete under the complex corrosion environment and coupling effects of mechanics and environment should be analyzed in future.
出处 《建筑科学与工程学报》 CAS 北大核心 2017年第5期154-162,共9页 Journal of Architecture and Civil Engineering
基金 国家自然科学基金项目(51578072) 陕西省自然科学基础研究计划项目(2016JM5070 2017JQ5092) 陕西省科技统筹创新工程计划项目(2015KTZDSF03-04)
关键词 结构工程 轻骨料混凝土 碳化 预测模型 影响因素 structural engineering lightweight aggregate concrete carbonation prediction model influence factor
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