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Fatigue Life Prediction of FRP-Strengthened Reinforced Concrete Beams Based on Soft Computing Techniques

MATERIALS 2025年第2期18卷 230-230页

作者： Zhang, Zhimei Wang, Xiaobo Shanghai Univ Sch Mech & Engn Sci Dept Civil Engn Shanghai 200444 Peoples R China

This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams.

关键词： multi-objective genetic algorithm evolutionary polynomial regression gene expression programming fatigue life prediction model fiber-reinforced polymer composite material FRP strengthened beams

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Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

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Asian Journal of Civil Engineering 2024年第1期25卷 183-196页

作者： Albostami, Asad S. Al-Hamd, Rwayda Kh. S. Alzabeebee, Saif Minto, Andrew Keawsawasvong, Suraparb School of Engineering & Construction Oryx Universal College in Partnership with Liverpool John Moores Doha Qatar School of Applied Sciences Abertay University Dundee United Kingdom Department of Roads and Transport Engineering College of Engineering University of Al-Qadisiyah Al Diwaniyah Al-Qadisiyah Iraq Department of Civil Engineering Thammasat School of Engineering Thammasat University Pathumthani 12120 Thailand

Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and gene expression programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the linear regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R ²) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA. © 2023, The Author(s).

关键词： Gene expression programming multi-objective genetic algorithm evolutionary polynomial regression Recycled plastic aggregates Self-compacting concrete Soft computing

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Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

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Journal of Building Pathology and Rehabilitation 2023年第2期8卷 1-15页

作者： Albostami, Asad S. Al-Hamd, Rwayda Kh. S. Alzabeebee, Saif School of Engineering & Construction Oryx Universal College in Partnership with Liverpool John Moores Doha Qatar Civil Engineering Department Faculty of Engineering University of Petra Amman Jordan School of Applied Sciences Abertay University Dundee United Kingdom Department of Roads and Transport Engineering College of Engineering University of Al-Qadisiyah Al-Qadisiyah Al Diwaniyah Iraq

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups. © 2023, The Author(s).

关键词： Gene expression programming multi-objective genetic algorithm evolutionary polynomial regression Recycled aggregate concrete Shear strength of reinforced concrete Soft computing

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