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Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method

  • Yuanxin Lv
  • Xiaoyu ShiEmail author
  • Longyu Ran
  • Mingsheng Shang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

As one of the most important building materials, the quality of concrete directly affects the safety of buildings. Hence, it is an important and hot issue to predict the compressive strength of concrete with highly accuracy. Most of existing methods heavily depend on building a single model to predict the compressive strength of concrete. However, the proposed single models are not one-size-fits-all, different model have different limitations, making them a better or worse fit for different situation. To address this issue, this paper proposes a novel predict model for concrete compressive strength based on ensemble learning method. In detail, we build our ensemble framework via using the AdaBoost method, while the random forests methods as weak classifier are integrated to the AdaBoost framework. For dealing with the noisy and missing value problems, a set of statistical methods are employed. Furthermore, we utilize the Pearson correlation coefficient to analysis the relationship between different input materials, which can effectively drop out the irrelevant and redundant features. Experimental results on two industrial data sets show that proposed ensemble estimator can significantly improve the prediction accuracy in comparing with other five state-of-the-art methods.

Keywords

Concrete compressive strength Ensemble learning AdaBoost Random Forests Prediction 

Notes

Acknowledgement

This work was supported in part by Chongqing research program of key standard technologies innovation of key industries under grant cstc2017zdcy-zdyfX0076, in part by Chongqing research program of technology innovation and application under grant cstc2018jszx-cyztzxX0025 and cstc2017rgzn-zdyfX0020, in part by Youth Innovation Promotion Association CAS, No. 2017393, in part by the National Natural Science Foundation of China under Grant 61602434.

References

  1. 1.
    Wang, D., Chen, Z.: On predicting compressive strengths of mortars with ternary blends of cement, GGBFS and fly ash. Cem. Concr. Res. 27(4), 487–493 (1997)CrossRefGoogle Scholar
  2. 2.
    Popovics, S.: History of a mathematical model for strength development of Portland cement concrete. Mater. J. 95(5), 593–600 (1998)Google Scholar
  3. 3.
    Rougeron, P., Aïtcin, P.C.: Optimization of the composition of a high-performance concrete. Cem. Concr. Aggreg. 16(2), 115–124 (1994)Google Scholar
  4. 4.
    Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)CrossRefGoogle Scholar
  5. 5.
    Nguyen, T., Kashani, A., Ngo, T., et al.: Deep neural network with high-order neuron for the prediction of foamed concrete strength. Comput.-Aided Civil Infrastruct. Eng. 34(4), 316–332 (2019)CrossRefGoogle Scholar
  6. 6.
    Deng, F., He, Y., Zhou, S., et al.: Compressive strength prediction of recycled concrete based on deep learning. Constr. Build. Mater. 175, 562–569 (2018)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  8. 8.
    Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)Google Scholar
  9. 9.
    Freund, Y., Schapire, E.R.: A desicion-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory (1995)Google Scholar
  10. 10.
    Gandomi, A.H., Alavi, A.H.: A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput. Appl. 21(1), 171–187 (2012)CrossRefGoogle Scholar
  11. 11.
    Chou, J.S., Pham, A.D.: Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Constr. Build. Mater. 49, 554–563 (2013)CrossRefGoogle Scholar
  12. 12.
    Chou, J.S., Chiu, C.K., Farfoura, M., et al.: Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J. Comput. Civil Eng. 25(3), 242–253 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuanxin Lv
    • 1
    • 2
  • Xiaoyu Shi
    • 2
    Email author
  • Longyu Ran
    • 2
    • 3
  • Mingsheng Shang
    • 2
  1. 1.College of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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