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Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads

  • Duc-Kien ThaiEmail author
  • Tran Minh Tu
  • Tinh Quoc Bui
  • T.-T. Bui
Original Article
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Abstract

This paper proposed a new approach in predicting the local damage of reinforced concrete (RC) panels under impact loading using gradient boosting machine learning (GBML), one of the most powerful techniques in machine learning. A number of experimental data on the impact test of RC panels were collected for training and testing of the proposed model. With the lack of test data due to the high cost and complexity of the structural behavior of the panel under impact loading, it was a challenge to predict the failure mode accurately. To overcome this challenge, this study proposed a machine-learning model that uses a robust technique to solve the problem with a minimal amount of resources. Although the accuracy of the prediction result was not as high as expected due to the lack of data and the unbalance experimental output features, this paper provided a new approach that may alternatively replace the conventional method in predicting the failure mode of RC panel under impact loading. This approach is also expected to be widely used for predicting the structural behavior of component and structures under complex and extreme loads.

Keywords

Impact loading Reinforced concrete Local damage Machine learning XGboost Gradient boosting 

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5086385).

Compliance with ethical standards

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringSejong UniversitySeoulSouth Korea
  2. 2.Faculty of Industrial and Civil EngineeringNational University of Civil EngineeringHanoiVietnam
  3. 3.Department of Civil and Environmental EngineeringTokyo Institute of TechnologyTokyoJapan
  4. 4.University of Lyon, GEOMAS, INSA LyonVilleurbanne CedexFrance

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