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A comparative study of artificial intelligent methods for explosive spalling diagnosis of hybrid fiber-reinforced ultra-high-performance concrete

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Abstract

Explosive spalling is a major concern to ultra-high-performance concrete at elevated temperatures. Previous physics-based numerical models for predicting explosive spalling of concrete are not validated sufficiently and still impractical for industrial applications. In this work, six machine learning models are developed to assess the thermal spalling risk of UHPC with hybrid polypropylene fibers and steel fibers, as well as to determine the minimal dosage of fibers required to eliminate spalling. Among the six models, five models are based on artificial neural network, support vector machine, decision tree, random forest, and extreme gradient boosting, respectively. Furthermore, a voting ensemble model is proposed based on the five individual machine learning models. To test the effectiveness of these six machine learning models, 36 groups of heating tests are conducted on hybrid fiber-reinforced UHPC specimens. The results show that among the six models, the XGBoost model shows the best performance with an accuracy of 97.2% and F1 score of 0.933. Parametric analyses are performed using the XGBoost model to study the influences of various parameters on the minimal dosage of fibers to prevent spalling. According to the analysis, PP fibers play a primary role in preventing explosive spalling of UHPC, and limiting the silica fume content reduces the minimal PP fiber dosage for spalling prevention. As the silica fume/binder ratio decreases from 0.25 to 0.05, the minimal PP fiber dosage decreases from 3.5 to 0.5 kg/m3.

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Acknowledgements

The work is funded by the Innovation and Technology Support Programme of Innovation and Technology Fund of Hong Kong (Grant no: ITS/412/18). Any opinions, findings, conclusions or recommendations expressed in this material/event (or by members of the project team) do not reflect the views of the Government of the Hong Kong Special Administrative Region, the Innovation and Technology Commission or the Panel of Assessors for the Innovation and Technology Support Programme of the Innovation and Technology Fund.

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Authors and Affiliations

  1. Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China

    Jin-Cheng Liu, Zhijian Chen & Hailong Ye

  2. School of Civil Engineering, Wuhan University, No. 8 Dong Hu South Road, Wuhan, 430072, China

    Le Huang

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  1. Jin-Cheng Liu
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Correspondence to Hailong Ye.

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Appendices

Appendices

1.1 Appendix A

See Fig. 17.

Fig. 17
figure 17

Hybrid fiber-reinforced UHPC specimens after exposure to high temperature (UHPC1-2–4.5% means that UHPC1 specimens with moisture content of 4.5% were heated to 600 °C at 2 °C/min. The same naming convention applies to the rest titles in the appendixes.)

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1.2 Appendix B

See Table 5 and Fig. 18.

Table 5 Mix proportions of UHPC with PP fibers
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Fig. 18
figure 18

PP fiber-reinforced UHPC specimens after exposure to high temperature

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1.3 Appendix C

See Table 6 and Fig. 19.

Table 6 Mix proportions of UHPC with steel fibers*
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Fig. 19
figure 19

Steel fiber-reinforced UHPC specimens after exposure to high temperature

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Liu, JC., Huang, L., Chen, Z. et al. A comparative study of artificial intelligent methods for explosive spalling diagnosis of hybrid fiber-reinforced ultra-high-performance concrete. Int J Civ Eng 20, 639–660 (2022). https://doi.org/10.1007/s40999-021-00689-7

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  • DOI: https://doi.org/10.1007/s40999-021-00689-7

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