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Steel and Composite Structures Volume 43, Number 3, May10 2022 (Special Issue) pages 293-309 DOI: https://doi.org/10.12989/scs.2022.43.3.293 |
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Design models for predicting shear resistance of studs in solid concrete slabs based on symbolic regression with genetic programming |
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Vitaliy V. Degtyarev, Stephen J. Hicks and Jerome F. Hajjar
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| Abstract | ||
| Accurate design models for predicting the shear resistance of headed studs in solid concrete slabs are essential for obtaining economical and safe steel-concrete composite structures. In this study, symbolic regression with genetic programming (GPSR) was applied to experimental data to formulate new descriptive equations for predicting the shear resistance of studs in solid slabs using both normal and lightweight concrete. The obtained GPSR-based nominal resistance equations demonstrated good agreement with the test results. The equations indicate that the stud shear resistance is insensitive to the secant modulus of elasticity of concrete, which has been included in many international standards following the pioneering work of Ollgaard et al. In contrast, it increases when the stud height-to-diameter ratio increases, which is not reflected by the design models in the current international standards. The nominal resistance equations were subsequently refined for use in design from reliability analyses to ensure that the target reliability index required by the Eurocodes was achieved. Resistance factors for the developed equations were also determined following US design practice. The stud shear resistance predicted by the proposed models was compared with the predictions from 13 existing models. The accuracy of the developed models exceeds the accuracy of the existing equations. The proposed models produce predictions that can be used with confidence in design, while providing significantly higher stud resistances for certain combinations of variables than those computed with the existing equations given by many standards. | ||
| Key Words | ||
| genetic programming; headed studs; machine learning; reliability; shear resistance; steel-concrete composite structures; symbolic regression | ||
| Address | ||
| Vitaliy V. Degtyarev:New Millennium Building Systems, Columbia, SC, U.S.A. Stephen J. Hicks:School of Engineering, University of Warwick, Coventry, CV4 7AL, U.K. Jerome F. Hajjar:Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, U.S.A. | ||