Structural health monitoring is an essential process for ensuring the safety and serviceability of civil structures. When a structure sufers from damage, it is necessary to implement maintenance programs for returning the structural performance and integrity to its initial normal condition. An important challenge is that the structure of interest may be damaged even after a sophisticated maintenance program. This conveys the great necessity of performing the second level of structural condition assessment and damage detection of maintained structures. To achieve this aim, this article proposes a novel methodology using the concept of supervised learning. The main objective of the proposed methodology is to train various supervised learning classifers using a training dataset that consists of features regarding both the undamaged and damaged states of the structure before the maintenance program in the frst level. Once the classifers have been trained, one attempts to predict the class labels of test samples associated with the current state of the structure after the maintenance program during the second level. According to the predefned class labels of the training and test samples in the frst stage, it is feasible to recognize the current state of the maintained structure in the second level and detect potential damage. The major contribution of this article is to introduce the concept of supervised learning for damage detection in an innovative manner. A numerical concrete beam and an experimental laboratory frame are used to demonstrate the efectiveness and applicability of the proposed methodology. Results show that this methodology is a practical and reliable tool for structural condition assessment and damage detection of maintained structures.

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