In this paper, a probabilistic study on durability concrete was carried out. In such a design, initiation time of corrosion must be expressed as a mathematical model using Fick's second law and the statistical distributions properties of theirs parameters was included in this model. The scatter both in the environmental exposure conditions and structural properties was considered as random fields in the mathematical model with a probabilistic design. The main objective of this study is predicted initiation time of corrosion of concrete structures in chloride containing environments. This probabilistic study is developed using Monte Carlo simulation to determine the contribution of each input parameters and the statistical parameters of the random variables on the probability distribution functions of the initiation time of corrosion. Also, a comparison study was carried out to analyze the impact of the probability distribution on the response (the initiation time of corrosion).
Reinforcement corrosion is one of the main phenomena determining the life of a structure. It can be tracked using methods based on several indicators of the probability of corrosion. These measures can be more or less lengthy and can require very specific equipment. In recent years, several non-destructive tests have been developed that are relatively fast and less costly based on the measurement of corrosion potential. In this study, a statistical analysis is performed, using a multiple linear regression, to test the reliability of the data obtained by experimental measurement of the corrosion potential. Artificial neural networks (ANN) are then used to develop a model to predict the corrosion potential of reinforcement in a concrete or mortar. The results indicate that the artificial neural network can predict corrosion potential with an acceptable degree of accuracy.
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