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

Nikoo, Mohammad Reza; Aminnejad, Babak; Lork, Alireza

doi:10.1155/2021/5899356

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…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%

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: 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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“…The results revealed that the developed model has more robustness than the classical SVR model and empirical equations. Nikoo et al 46 integrated bat algorithm with ANN to estimate shear behavior of FRB reinforced concrete elements. Based on the statistical assessment and comparison with other optimization algorithms, the study confirmed that the integrated model attained more accurate results than particle swarm optimization (PSO) and genetic algorithm (GA).…”

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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“…With the continuous advancement of computer technology, artificial intelligence (AI) techniques represented by machine learning (ML) models are increasingly applied in the field of civil engineering, particularly in the prediction of the performance of RC beams [21][22][23][24][25][26][27][28][29][30]. Mansour et al [31] utilized the artificial neural network (ANN) model to predict the shear performance of RC beams.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams

Yang

Qiu

et al. 2023

Materials

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Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab’s effective depth (SED), span–depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab’s effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

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

Cited by 13 publications

References 39 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

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

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams

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