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Machine learning models for cracking torque and pre-cracking stiffness of RC beams

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Abstract

The torsional behavior of RC beams is a complex work involving interactions of different design parameters and mechanisms. Considering the limitations and lower accuracy of traditional calculation theories, two machine learning models, including artificial neural network (ANN) model and random forest (RF) model, were applied for the first time to predict the cracking torque and initial or pre-cracking torsional stiffness of RC beams. A comprehensive database consisting 159 experimental results of RC beams with solid or hollow sections was compiled, with input variables including dimension parameters of cross-section, compressive stress of concrete, elastic modulus and strength ratio of reinforcements. The performance of the models was appraised by various statistical estimators and safety ratio, and compared with different theories for cracking torque and initial stiffness. Among all the calculation models, RF model achieved the best overall prediction performance with the highest coefficient of determination (R2 = 0.985 for cracking torque and R2 = 0.978 for initial stiffness) and lowest root-mean-square error (RMSE = 5.867 for cracking torque and RMSE = 3.994 for initial stiffness). However, theories for cracking torque, i.e., plastic theory, Bredt thin-tube theory and skew-bending theory, gave huge underestimation, whereas greatly exaggerated initial stiffness was obtained by elastic theory and simplified soften membrane model for torsion theory. Besides, input variable importance analysis was conducted, revealing that dimension parameters of cross-section were the most critical features to decide prediction performance for pre-cracking torsional performance of RC beams. The achievements of this paper may provide references to the establishment of new predicting model for pre-cracking torsional response of RC beams.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant number 51808258], CSCEC Technical and Development Plan [Grant No. CSCEC-2020-Z-35], and Fundamental Research Funds for the Central Universities [Grant No. 2022QN1031].

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Authors and Affiliations

  1. Jiangsu Key Laboratory Environmental Impact and Structural Safety in Engineering, China University of Mining and Technology, Xuzhou, 211116, Jiangsu Province, China

    Chen Shenggang & Zhang Yingying

  2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, 211116, Jiangsu Province, China

    Chen Shenggang & Zhang Yingying

  3. School of Transportation and Science Engineering, Beihang University, Beijing, 100191, China

    Guo Quanquan

  4. China Construction First Group Corporation Limited, Beijing, 100161, China

    Hu Hexiang

  5. China Academy of Building Research, Beijing City, 100029, China

    Shen Bei

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  1. Chen Shenggang
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Correspondence to Zhang Yingying.

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Shenggang, C., Quanquan, G., Yingying, Z. et al. Machine learning models for cracking torque and pre-cracking stiffness of RC beams. Archiv.Civ.Mech.Eng 23, 6 (2023). https://doi.org/10.1007/s43452-022-00541-2

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  • DOI: https://doi.org/10.1007/s43452-022-00541-2

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