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• A multi-class Boosting method is proposed for the multi-class imbalanced problem. • Parametrized division of exposure zone is achieved using RMS, C r , C s , and D cl , etc. • Field chloride profile data with time span of 18 years validates proposed method. • The distribution of exposure zone satisfies the Weibull distribution. The analysis of marine reinforced concrete structures using chloride profile data is a commonly used exposure zone classification method. However, chloride profile data is multi-class, unbalanced and non-parametric, which makes it difficult for the commonly used machine-learning methods to construct an appropriate classification model. To solve this problem, chloride profile is parametrized by the minimum redundancy maximum relevance algorithm and a multi-class Boosting method using F-measure as inductive bias indicator to evaluate the weight of base classifiers is put forward. The method is based on field test data of the chloride profile over a period of 18 years in Hangzhou Bay, China. The method outperforms the original Boosting method with an average F-measure improvement of 6.2 %. The results show that parametric partitioning of the exposure zone is achieved and the distribution of the exposure zone satisfies the Weibull distribution. [ABSTRACT FROM AUTHOR]