Artificial neural network model for predicting the local compression capacity of stirrups-confined concrete

Abstract

The local compression capacity is a key mechanical property for several constructions, such as the post-tensioned prestressed concrete. Due to the disordered stress distribution caused by the introduction of the concentrated load, it is a huge challenge to predict accurately the local compression capacity of stirrups-confined concrete to avoid the whole structure damage caused by local failure. This study explored a new approach to obtain the local compression capacity for stirrups-confined concrete using artificial neural networks (ANN). The ANN model was trained by a reliable database consisting of 180 samples from previous literature, which had a large application range, covering concrete strengths 15–112 MPa while the stirrup yield strengths 230–660 MPa. The main parameters, including the concrete strength, the local area aspect ratio, the core area aspect ratio, the ratio of duct diameter to the section width, the yield strength of stirrups, and the volumetric ratios of stirrups, were selected as input variables. To evaluate the proposed ANN model, the k-fold cross-validation approach was adopted to determine the generalization and reliability, and the sensitivity analysis was conducted to investigate the importance of the input parameters. Moreover, an empirical ANN equation was generated based on the proposed ANN model for design. Finally, the ANN model and the ANN equation were evaluated and verified against experimental data and existing models. The findings indicated that the ANN approach was highly applicable and reliable for estimating the local compression capacity of stirrups-confined concrete.