| 摘要: |
| 在海工混凝土结构中，由于氯离子侵蚀导致的钢筋锈蚀问题十分普遍。为准确判断钢筋初锈时间，预测结构耐久性劣化规律，在现有氯离子扩散模型的基础上，基于蒙特卡罗理论，利用MATLAB软件对钢筋周围氯离子浓度分布进行随机抽样模拟。结果表明一定时间段内（10年、50年、100年）钢筋周围氯离子浓度服从对数正态分布，并提出判断钢筋锈蚀的概率模型，以概率方法判断钢筋锈蚀，其可靠性将大大增加。结合该模型对连云港港区现场服役混凝土构件进行了氯离子含量预测，现场取粉试验结果基本符合该模型分布，其均值吻合较好，并对港区混凝土结构耐久性使用寿命失效概率进行了风险评估。 |
| 关键词: 蒙特卡罗法 钢筋初锈 氯离子浓度分布 概率模型 |
| DOI： |
| 分类号:TU528 |
| 基金项目:国家自然科学基金资助项目(51278230, 51378241); 教育部博士点基金资助项目(20123227110006); 江苏大学高级人才科研基金资助项目(11JDG132) |
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| Application analysis of probability model for chloride ion erosion distribution in marine concrete structure |
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GAO Yuan, LU Chun-hua, YUAN Si-qi, CHEN Su-bi, YANG Jin-mu
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Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang
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| Abstract: |
| As for the durability of the concrete structure, the problems of steel corrosion caused by chloride ion erosion are common in the marine works. In order to accurately forecast and predict reinforcement corrosion and structure durability degradation law, the basic idea of Monte Carlo is used, combined with MATLAB toolbox to simulate the chloride ion concentration distribution around the surrounding reinforement bar. The analysis results show that in a certain period of time (10 a, 50 a, 100 a) the chloride ion concentration around reinforced bar obeys the lognormal distribution. The probability model of chloride ion diffusion is established. Judging the reinforcement corrosion by a probability method, the accuracy can be greatly increased. At the same time, the probability model is used to predict the concrete structure durability of the Lianyungang port service components, and the results are in conformity with those of the field tests. And the risk assessment is made for the failure probability of the concrete structure service life. Therefore, this model can provide a theoretical basis for the nondestructive testing of the chloride ions content in concrete and for the judgment of steel-bar corrosion in the marine concrete works. |
| Key words: Monte Carlo method corrosion initiation chloride ion concentration profile probability model |
