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Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine

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Abstract

During the life cycle of buildings and infrastructure systems, the deflection of reinforced-concrete members generally increases due to both internal and external factors. Accurate forecasting of long-term deflection of these members can significantly enhance the effectiveness of structural maintenance processes. This research develops a hybrid data-driven method which employs the extreme gradient boosting machine and the particle swarm optimization metaheuristic for predicting long-term deflections of reinforced-concrete members. The former, a machine learning technique, generalizes a non-linear mapping function that helps to infer long-term deflection results from the input data. The later, a swarm-based metaheuristic, aims at optimizing the machine learning model by fine-tuning its hyper-parameters. The proposed hybridization of machine learning and swarm intelligence is constructed and verified by a dataset consisting of 217 experiments. The experiment results, supported by statistical tests, point out that the hybrid framework is able to attain good predictive performances with average root-mean-square error of 11.38 (a reduction of 17.4%), and average coefficient of determination of 0.88 (an increase of 6.0%) compared to the non-hybrid model. These results also outperform those obtained by other popular techniques, including Backpropagation Neural Networks and Regression Tree in several popular benchmarks, such as root-mean-square error, mean absolute percentage error, and the coefficient of determination R2. This is backed up by statistical tests with the level of significance \(\alpha = 0.05\). Therefore, the newly developed model can be a promising tool to assist civil engineers in forecasting deflections of reinforced-concrete members.

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Notes

  1. XGBoost can also be used for classification. In this case, classification trees are used instead.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2017.302.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, P809-03 Quang Trung, Da Nang, 550000, Vietnam

    Hieu Nguyen, Nhat-Duc Hoang & Xuan-Linh Tran

  2. Faculty of Natural Sciences, Duy Tan University, P809-03 Quang Trung, Da Nang, 550000, Vietnam

    Hieu Nguyen

  3. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, No. 43, Keelung Rd., Sec.4, Da’an Dist., Taipei, 10607, Taiwan, ROC

    Ngoc-Mai Nguyen

  4. Department and Institute of Civil Engineering and Environmental Informatics, Minghsin University of Science and Technology, No. 1, Xinxing Rd., Xinfeng, Hsinchu, 30401, Taiwan, ROC

    Minh-Tu Cao

  5. Faculty of Civil Engineering, Duy Tan University, P809-03 Quang Trung, Da Nang, 550000, Vietnam

    Nhat-Duc Hoang & Xuan-Linh Tran

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  1. Hieu Nguyen
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Nguyen, H., Nguyen, NM., Cao, MT. et al. Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine. Engineering with Computers 38 (Suppl 2), 1255–1267 (2022). https://doi.org/10.1007/s00366-020-01260-z

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