Prediction of the effective moment of inertia for concrete beams reinforced with FRP bars using an evolutionary algorithm

Arabshahi, Alireza; Tavakol, Masoumeh; Sabzi, Javad; Gharaei-Moghaddam, Nima

doi:10.1016/j.istruc.2021.11.011

Cited by 16 publications

(4 citation statements)

References 77 publications

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“…To determine the following equation (18)(19)(20)(21)(22), according to 40 curves related to each correction factor, a line with the least squared error was drawn and the corresponding equations were presented: 0.37 0.63 0.4…”

Section:    mentioning

confidence: 99%

“…Wang et al [19] investigated the flexural behavior of five hybrid BFRP and steel bars RC beams subject to four-point bending tests. Arabshahi et al [20] proposed an equation for the effective moment of inertia in concrete beams reinforced with FRP bars. Lyu et al [21] studied the usage of back-propagation Neural Network (NN) and Genetic Algorithm (GA) for the predicting of torsional strength RC beams.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

et al. 2023

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The application of fiber-reinforced polymer bars is rapidly rising in concrete structures because of corrosion resistance and high tensile strength. By contrast, concrete structures reinforced with Fiber-Reinforced Polymer (FRP) bars illustrate less ductility and brittle failure without warning than reinforced concrete structures with conventional steel bars. Hybrid concrete structures with the combination of FRP and steel bars can simple beams to calculate the effective moment of inertia for hybrid reinforced concrete beams. The result shows that both soft computing models are highly precise compared to experimental data.

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Section:    mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

et al. 2023

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“…Fiber-reinforced polymers (FRP) have gained popularity in construction as a viable replacement for steel rebars in reinforced concrete (RC) members due to their corrosion resistance, higher strength, lightweight, and ease of fabrication [1]. Extensive research works have been carried out in the last two decades to investigate the performance of the different types of FRP reinforcement [2][3][4][5][6][7][8][9][10]. Some of the studies in this area were experimental in nature [11][12][13][14][15][16][17][18][19], whereas others aimed to develop more accurate, novel methods of prediction for structural performance using various techniques of artificial intelligence and optimization [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

et al. 2022

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Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.

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“…The latest research are still mainly focused on cracking behavior of RC members and estimating the effective moment of inertia. Consequently, sophisticated methods have been developed (Ammash et al, 2018;Arabshahi et al, 2022). In these approaches, even shear deformations were considered and taken into account (Kim et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Immediate Deflections for RC Beams Using Stress-varying Modulus of Elasticity

Özbek

2022

Gazi University Journal of Science Part A: Engineering and Innovation

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This paper discusses the immediate deflection calculation of reinforced concrete beams and their consistency with the experimental results. For this purpose, a total of six T-beams with low, medium, and high reinforcement ratios were tested and then, deflection behavior was compared with the well-known Branson (1965) and Bischoff (2005) approaches. Although both approaches could yield close results for the low reinforcement at service loads by using a constant modulus of elasticity, they underestimated the deflections of medium and highly reinforced beams. Thus, the nonlinear behavior of concrete that changes with stress was also considered in the subsequent analyzes. As a result, the developed new approach could predict the experimental deformations very accurately, especially at the level of service loads.

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Prediction of the effective moment of inertia for concrete beams reinforced with FRP bars using an evolutionary algorithm

Cited by 16 publications

References 77 publications

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

Prediction of Immediate Deflections for RC Beams Using Stress-varying Modulus of Elasticity

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