ESTIMATION OF CROSS-SECTIONAL CHARACTERISTICS BY MACHINE LEARNING FOR EVALUATION OF ADDITIONAL STRESS DUE TO SHEAR LAG

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  • AOKI Hiroki
    東北大学大学院　工学研究科土木工学専攻
  • SAIKI Isao
    東北大学大学院　工学研究科土木工学専攻
  • OTAKE Yu
    東北大学大学院　工学研究科土木工学専攻
  • MITSUI Ryohei
    東北大学大学院　工学研究科土木工学専攻

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  • せん断遅れによる付加的な応力評価のための機械学習による断面特性推定

Abstract

<p>The distribution of bending stress along the direction perpendicular to the bridge axis on the flange of beams with a wide flange is not uniform due to shear lag. In the design of beams, the additional stress due to the shear lag is considered by reducing the bending rigidity by the effective width. However, it has been known that the shear lag is not caused by bending but by cross-sectional deformation associated with shear deformation. In this context, a beam theory with a degree of freedom of cross-sectional deformation due to shear is proposed to evaluate shear lag effect. While the beam theory considering cross-sectional deformation has been known to estimate shear lag effect accurately, a finite element analysis of representative volume of cross-section is required to obtain a couple of additional cross-sectional parameters. In this study, we propose a method to estimate the additional parameters using LASSO regression and Gaussian process regression. The accuracy of the proposed method is confirmed by a set of test data.</p>

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