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| Probability Analysis on Service Life Prediction of Reinforced Concrete Structures |
CHEN Xuandong1,2, ZHANG Qing1( ), GU Xin1, LI Xing1 |
1.College of Mechanics and Materials, Hohai University, Nanjing 211100, China
2.Guangxi Key Laboratory of New Energy and Building Energy Saving, College of Civil and Architecture Engineering, Guilin University of Technology, Guilin 541004, China |
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Abstract Chloride ingress is one of the key factors that lead to the durability deterioration of reinforced concrete (RC) structures in marine environment. Therefore, it is of great significance to constructed the service life prediction of RC structures serving in chloride containing environments to evaluate the safety performance of structures. In this paper, the probability model of durability of RC structure is established by using Monte Carlo method in order to acquire the probability distribution of service life of RC structures. The results show that the optimal number of Monte Carlo simulations is 10000 times, which not only saves computational resources but also meets the computational accuracy. The service life of the RC structures calculated by the probabilistic method (pfmax=5% and 10%) is shorter than that calculated by the deterministic method, which indicates the deterministic model underestimates the durability deterioration caused by chloride ingress. The parameterized analysis results of water-cement ratio, concrete cover thickness, fly ash content, surface chloride ion concentration and critical chloride ion concentration show that the thickness of concrete cover has the most significant effect on the service life, and the safety reserve period of the RC structure of long service life is also longer, and the general safety reserve period is between 5 and 10 a. Therefore, the service life prediction model based on reliability method provides a theoretical basis for the repair and health monitoring of reinforced concrete structures.
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Received: 26 October 2020
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| Fund: National Natural Science Foundation of China(11932006);National Key R&D Program;of China(2018YFC0406703);Guangxi Universities Scientific Research Project(2020KY06029) |
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Corresponding Authors:
ZHANG Qing
E-mail: lxzhangqing@hhu.edu.cn
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About author: ZHANG Qing, E-mail: lxzhangqing@hhu.edu.cn
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