Time-varying Gamma stochastic process-based modeling method for steel corrosion

被引：0
作者：
Fang W. [1 ,2 ]
Chen M. [1 ,2 ]
Zhang R. [2 ]
Xie L. [1 ,2 ]
机构：
[1] The State Local Joint Engineering Research Center for Security Technology of Operation Maintenance in Rail Transit Infrastructures, Nanchang
[2] School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang
来源：
Jianzhu Jiegou Xuebao/Journal of Building Structures | 2020年 / 41卷
关键词：
Bayesian update; Corrosion; Gamma stochastic process; Steel; Time-varying;
D O I：
10.14006/j.jzjgxb.2020.S2.0042
中图分类号：
学科分类号：
摘要：
Aiming at the problems of long collection period, small amount and large dispersion of steel structure corrosion data in actual engineering. The accelerated corrosion test by simulating acid rain spray on steel Q235 was carried out to obtain the corrosion mass data. Combined with the stochastic process theory, a steel corrosion mass growth model based on the time-varying Gamma stochastic process was established. Using Bayesian update theory and Markov chain Monte Carlo method, the stochastic parameters of the steel corrosion mass growth model were updated, and the growth trajectory of the steel corrosion mass was predicted. The results show that this method can modify the random parameters of the stochastic process model of steel corrosion quality growth and effectively predict the law of steel corrosion quality growth. It has important reference value for predicting the corrosion life of steel and formulating maintenance strategies for steel structures. © 2020, Editorial Office of Journal of Building Structures. All right reserved.
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页码：382 / 388
页数：6
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