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Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

Structural health monitoring is an essential process for ensuring the safety and serviceability of civil structures. When a structure suffers 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 classifiers using a training dataset that consists of features regarding both the undamaged and damaged states of the structure before the maintenance program in the first level. Once the classifiers 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 predefined class labels of the training and test samples in the first 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 effectiveness 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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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran

    Alireza Entezami, Hashem Shariatmadar & Hassan Sarmadi

  2. Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milan, Italy

    Alireza Entezami

  3. Research and Development, Ideh Pardazan Etebar Sazeh Fanavar Pooya (IPESFP) Company, 29th Reza St., Reza Blvd, P.O. Box: 9176768544, Mashhad, Razavi Khorasan, Iran

    Hassan Sarmadi

Authors
  1. Alireza Entezami
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  2. Hashem Shariatmadar
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  3. Hassan Sarmadi
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Correspondence to Hashem Shariatmadar.

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Entezami, A., Shariatmadar, H. & Sarmadi, H. Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods. Iran J Sci Technol Trans Civ Eng 44 (Suppl 1), 51–66 (2020). https://doi.org/10.1007/s40996-020-00463-0

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  • DOI: https://doi.org/10.1007/s40996-020-00463-0

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