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Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups

journal contribution
posted on 2020-01-01, 00:00 authored by J Zhang, Y Sun, G Li, Y Wang, J Sun, Jianxin LiJianxin Li
Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

History

Journal

Engineering with computers

Pagination

1-15

Location

London, Eng.

ISSN

0177-0667

eISSN

1435-5663

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Springer