A new model for predicting the shear strength of RC beams strengthened with externally bonded FRP sheets

Mohammadi, Amin; Barros, Joaquim A. O.; Sena-Cruz, José

doi:10.1016/j.compstruct.2023.117081

Cited by 12 publications

(3 citation statements)

References 66 publications

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“…After the Hanshin earthquake in Japan, rapid and effective strengthening/repair methods were urgently necessary for the bridges and buildings damaged in the earthquake. The externally bonded carbon fiber sheet [4,5] has played an important role in post-earthquake strengthening and repair. Since then, FRPs have gradually been promoted and applied in civil engineering, receiving widespread attention from engineers.…”

Section: Introductionmentioning

confidence: 99%

Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering

Shi

2023

Buildings

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Section: Introductionmentioning

confidence: 99%

Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering

Shi

2023

Buildings

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“…Throughout history, the built environment has experienced significant human casualties and economic losses due to the destructive nature of earthquakes. Researchers have focused on developing innovative strategies to enhance building structural performance while also employing alternative approaches to estimate demands on structures and assess losses in the building stock [1][2][3][4][5][6][7][8]. In recent years, the rapid advancement of computer processors has led to a significant increase in the utilisation of machine learning capabilities across various engineering domains [9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions

Mohammadi,

Karimzadeh,

Yaghmaei-Sabegh

et al. 2023

Buildings

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Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs.

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“…In this study, the authors make use of data-driven approaches, including discriminant analysis and multiple regression analysis, to evaluate relationships between DSs with seismological and structural parameters and to distinguish the most influential parameters on the estimation of DSs. Such approaches are very popular in civil and earthquake engineering research, and they have been used by many researchers [20][21][22][23][24][25][26]. Moreover, the recent advances in uncertainties regarding the sensitivity analysis of global structural parameters (especially for masonry structures) can be accessed in [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

The Sensitivity of Global Structural Parameters for Unreinforced Masonry Buildings Subjected to Simulated Ground Motions

et al. 2023

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This research performs a parametric study based on Equivalent Single Degree of Freedom (ESDOF) models for simplified seismic analysis of unreinforced masonry (URM) structures. This is a necessary action due to the fact that it is not affordable to model and analyze populations of masonry buildings by using detailed continuum-based models during regional seismic damage and loss estimation studies. Hence, this study focuses on the sensitivity of major structural parameters of a selected idealized hysteretic model for URM buildings. The numerical models are subjected to region-specific simulated ground motion time histories generated using validated seismological parameters. The variations in dynamic analysis results are evaluated using statistical tools for major structural and seismological parameters. The results reveal that the strength factor is the most influential structural parameter, whereas magnitude and distance have a significant impact on the response of idealized URM models as seismological parameters. Furthermore, the specific seismic performance exhibiting limited ductility capacity and the narrow margin of safety between the initial state of inelastic behavior and the ultimate (collapse) state for URM buildings is verified by the statistical approaches employed in this study.

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A new model for predicting the shear strength of RC beams strengthened with externally bonded FRP sheets

Cited by 12 publications

References 66 publications

Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering

Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering

Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions

The Sensitivity of Global Structural Parameters for Unreinforced Masonry Buildings Subjected to Simulated Ground Motions

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