Genetic algorithm model for shear capacity of RC beams reinforced with externally bonded FRP

Nehdi, Moncef L.; Nikopour, H.

doi:10.1617/s11527-010-9697-2

Cited by 21 publications

(6 citation statements)

References 27 publications

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“…( 3) indicates that determining the effective FRP strain is crucial for predicting the shear contribution of the external FRP reinforcement. The crack opening along the shear crack, local FRP debonding on both sides of the shear crack, the development length of FRP, which depends on the bond at the FRP-concrete interface, and the axial rigidity of FRP all play a role in the estimation of this strain [16]. The effective strain, 𝜖𝜖 𝑓𝑓𝑓𝑓 is therefore calibrated using a function of 𝜌𝜌 𝑓𝑓 𝐸𝐸 𝑓𝑓 𝑓𝑓′ 𝑐𝑐 2/3 which indicated a power function as shown in Figure 1 (inclusive of first and third quartiles).…”

Section: Significance Of the Databasementioning

confidence: 99%

“…Based on the equilibrium of forces in a cross-section of a beam at failure, Pellegrino and Moderna [14] have obtained a theoretical equation for effective FRP strain. Nehdi et al [15], [16], and Kara [17] have taken a genetic algorithm approach whereas Hosseini and others [18], [19], [20], [21] have adopted machine learning and neural networks. Anvari et al [22] have also used an evolutionary machine learning approach, named genetic expression programming.…”

Section: Introductionmentioning

confidence: 99%

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Shear Design Equations for EB FRP-RC Beams Based on Data Fitting Approach

Haggalla,

Bae

2024

World Congress on Civil, Structural, and Environmental Engineering

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Based on a data fitting method applied to 490 experimental test data that are publicly available in the literature, this study provides simplistic and straightforward equations to determine the shear capacity of FRP bonded-RC beams. Complete wrap, U-wrap, and side wrap schemes pertaining to Carbon Fiber Reinforced Polymer (CFRP) were analyzed separately. Current design codes follow a customary approach where the nominal shear capacity is calculated by simply accumulating the shear contribution of concrete, transverse reinforcement, and FRP. The interaction between concrete, transverse reinforcement, and FRP is usually not taken into consideration. While the modulus of elasticity of FRP, longitudinal steel ratio, transverse steel ratio, and FRP ratio all have an inverse interaction with the effective strain of FRP, the concrete's compressive strength is positively linked with the effective FRP strain, i.e., when the concrete compressive strength increases, effective FRP increases. This investigation further showed that as transverse and longitudinal reinforcement are increased, the influence of FRP on shear contribution decreases. ACI 440.2R-17 and, CSA S806-02 are among the regularly used shear design methodologies in North America and they were used to compare the performance of the proposed equations. The obtained results show that the proposed equations predict the experimental results more accurately than ACI 440.2R-17 and CSA S806-02.

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Section: Significance Of the Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shear Design Equations for EB FRP-RC Beams Based on Data Fitting Approach

Haggalla,

Bae

2024

World Congress on Civil, Structural, and Environmental Engineering

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“…ACI-440.1R-15 [30], CAN/CSA-S806-12 [31], BISE-99 [32], and Nehdi et al (optimized equation method) provide the shear design requirements for FRE-reinforced concrete beams without stirrups.) [33,34]. Tus, they were used on experimental test data.…”

Section: Comparison With Existingmentioning

confidence: 99%

Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams

Nikoo

Aminnejad

Lork

2023

Advances in Civil Engineering

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The shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the literature to predict the shear strength of 139 training samples and 59 testing samples. Additionally, the ANN structure is optimized with the firefly algorithm. The FA-ANN model is also compared to ACI-440, CSA-S806, and BISE-99 codes, and the optimized model by Nehdi et al. Findings show that regarding the shear strength of FRP-reinforced concrete beams, the firefly algorithm-optimized model performs better than the other four models. Concerning accuracy, the coefficient of correlation, R2, was calculated as 0.961, while the average absolute error (AAE) is 0.22 for the shear strength of FRP-reinforced beams.

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“…Also, Nehdi et al utilized the GA and developed a set of simple equations to determine the shear strength of the FRP-reinforced concrete beams. e results of examining 212 experimental samples revealed that the shear span-to-depth ratio influences shear behavior in the bonded-FRP-reinforced concrete beams [4]. In addition, various other scholars, including Ricardo Perera et al and Shahnewaz et al, conducted studies on GA to determine the shear strength equations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Shear Strength in FRP‐Reinforced Concrete Beams Using Bat Algorithm‐Based Artificial Neural Network

Nikoo

Aminnejad

Lork

2021

Advances in Materials Science and Engineering

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In this article, 140 samples with different characteristics were collected from the literature. The Feed Forward network is used in this research. The parameters f’c (MPa), ρf (%), Ef (GPa), a/d, bw (mm), d (mm), and VMA are selected as inputs to determine the shear strength in FRP-reinforced concrete beams. The structure of the artificial neural network (ANN) is also optimized using the bat algorithm. ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. The results confirm that the bat algorithm-optimized ANN is more capable, flexible, and provides superior precision than the other three models in determining the shear strength of the FRP-reinforced concrete beams.

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Genetic algorithm model for shear capacity of RC beams reinforced with externally bonded FRP

Cited by 21 publications

References 27 publications

Shear Design Equations for EB FRP-RC Beams Based on Data Fitting Approach

Shear Design Equations for EB FRP-RC Beams Based on Data Fitting Approach

Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams

Predicting Shear Strength in FRP‐Reinforced Concrete Beams Using Bat Algorithm‐Based Artificial Neural Network

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