Shear strength prediction of FRP reinforced concrete members using generalized regression neural network

Alam, Shah; Gazder, Uneb

doi:10.1007/s00521-019-04107-x

Cited by 34 publications

(4 citation statements)

References 24 publications

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“…ey used 196 laboratory samples and compared the model's accuracy with the JSCE, CSA S806, ACI 440.1R, and BISE building codes. e proposed model exhibited superior accuracy compared to the building codes [20].…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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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“…The first step is to define the factors affecting labour productivity in high rise buildings. The present research study explores the implementation of eight types of data-driven machine learning models, namely ANFIS-GA, ANFIS-PSO, GRNN [23][24], BP-ANN [25][26], ENN [27][28], RT [29][30], SVM [31][32] and GPR [33][34]. The validation process is carried out hinging upon the performance metrics of MAPE, MAE, RMSE, RAE and RRSE.…”

Section: Model Developmentmentioning

confidence: 99%

Intelligent Data-Driven Models for Simulating Formwork Labour Productivity in High Rise Buildings

Elshaboury¹,

Al-Sakkaf²,

Alfalah³

et al. 2022

IJCI

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Forecasting accurate labour productivity is critical in construction project management because construction projects are labour-intensive. This study proposes eight intelligent data-driven models for emulating formwork labour productivity in high rise buildings. These models encompass an adaptive neuro-fuzzy inference system trained using genetic algorithm (ANFIS-GA), an adaptive neuro-fuzzy inference system trained using particle swarm optimization algorithm (ANFIS-PSO), generalized regression neural network (GRNN), back-propagation artificial neural network (BP-ANN), Elman neural network (ENN), regression trees (RT), support vector machines (SVM) and Gaussian process regression (GPR). The models are applied to two high-rise buildings in Montreal, Canada to test their prediction capabilities. The accuracies of the developed data-driven models are investigated using the performance metrics of mean absolute percentage error (MAPE), mean absolute error (MAE), root-mean squared error (RMSE), root relative squared error (RRSE) and relative absolute error (RAE).The assessment metrics show that the GRNN model exhibits better and stable performance than the remainder of the prediction models (MAPE 8.98%, MAE 0.13, RMSE 0.19, RAE 0.45 and RRSE 0.54). It is also derived that the work method and temperature sustain the high influence on labor productivity. It can be anticipated that the developed GRNN model, can be a valuable decision-making tool for forecasting construction labour productivity in construction projects.

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“…Jumaa and Yousif 41 used three AI models called artificial neural network (ANN), gene expression programming (GEP) and nonlinear regression to predict shear capacity of FRP reinforced concrete elements. The study showed that the developed models exhibited an excellent performance as compared with other models Development of generalized regression neural network (GRNN) was conducted to predict shear capacity of FRP reinforced concrete members without stirrups 42 . The developed model was compared with the design codes like ACI 440.1R, CSA S806 and JSCE.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2023

Sci Rep

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Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (Vs) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting Vs of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for Vs prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on Vs of FRP reinforced concrete beam with the potential of applying different computer aid models.

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Shear strength prediction of FRP reinforced concrete members using generalized regression neural network

Cited by 34 publications

References 24 publications

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

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

Intelligent Data-Driven Models for Simulating Formwork Labour Productivity in High Rise Buildings

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

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