Innovative ANN hysteresis to predict hysteretic performance of composite reinforced concrete beam. (February 2023)

• Record Type: Journal Article
• Authors: Yan, Gongxing; Li, Jie; Ali, Alaa Hussein; Alkhalifah, Tamim; Alturise, Fahad; Ali, H. Elhosiny
• Is Part Of: Advances in engineering software. Volume 176(2023)
• Publication Date: 2023-02
• Subjects: Innovative ANN hysteresis -- Machine learning -- Hysteretic performance -- Reinforced concrete -- Tension tests -- Crack -- Residual stiffness -- Steel fiber-reinforced concrete; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103373
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Estimation of the hysteresis loop of reinforced concrete beams assessed. Application of fibers as a mass enhancement is investigated. SFRC beams represented developed cyclic efficiency in case of deformation. Load-bearing capacity, residual stiffness, cracking and energy dissipation ability discussed. Generating the integrity within the imposed reversal cyclic experiments discussed. Abstract: This article assess the precise estimation of the hysteresis loop of reinforced concrete (RC) beams in distinct failure cases to verify inelastic seismic beam function. Any test failure in RC frame columns is able to produce hysteresis curves in low cyclic repeat load that follows the analysis of the hysteretic behavior of the frame columns. In this case, the application of fibers as a mass enhancement to improve the post-cracking of RC beams, strength, and delay cracking has been investigated. In this research, the hysteretic response of deep and slender SFRC beams enhanced with SF using ten beams under the reversal cyclic load was studied through innovative ANN hysteresis. Shear and flexural strength of SFRC beams were analyzed using a diverse number of fibers with content from 0.1 to 5% per volume, closed stirrups (from 0 to 0.5%), and steel reinforcing bars (0.50% and 1.50%). The innovative artificial neural network hysteresis model has been utilized to define the accuracy prediction of the parameters and determine the hysteresis loop of RC columns failing in different modes. Comparing the experimental findings properly indicated the accuracy of the model to capture the main features of the response, such as the load versus deformation cyclic envelope, SFRC tension softening effect, and the impact of the fibers on the hysteretic energy. The results revealed that SFRC beams represented developed cyclic efficiency in case of deformation, load-bearing capacity, residual stiffness, cracking and energy dissipation ability while generating their integrity within the imposed reversal cyclic experiments.