Shear Strength Determination in RC Beams Using ANN Trained with Tabu Search Training Algorithm

Shahbazian, Alireza; Rabiefar, Hamidreza; Aminnejad, Babak

doi:10.1155/2021/1639214

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…These can be performed through nature-inspired behaviors and learning experiences of population particles. PSO was found to be a robust integrated technique with SVR and ANN to model the shear strength of concrete [36,41,42]. PSO algorithm may be summarized as follows:…”

Section: Optimization Methodsmentioning

confidence: 99%

Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models

et al. 2022

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This study looks to propose a hybrid soft computing approach that can be used to accurately estimate the shear strength of reinforced concrete (RC) deep beams. Support vector regression (SVR) is integrated with three novel metaheuristic optimization algorithms: African Vultures optimization algorithm (AVOA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO). The proposed models, SVR-AVOA, -PSO, and -HHO, are designed and compared to reference existing models. Multi variables are used and evaluated to model and evaluate the deep beam’s shear strength, and the sensitivity of the selected variables in modeling the shear strength is assessed. The results indicate that the SVR-AVOA outperforms other proposed and existing models for the shear strength prediction. The mean absolute error of SVR-AVOA, SVR-PSO, and SVR-HHO are 43.17 kN, 44.09 kN, and 106.95 kN, respectively. The SVR-AVOA can be used as a soft computing technique to estimate the shear strength of the RC deep beam with a maximum error of ±3.39%. Furthermore, the sensitivity analysis shows that the deep beam’s key parameters (shear span to depth ratio, web reinforcement’s yield strength, concrete compressive strength, stirrups spacing, and the main longitudinal bars reinforcement ratio) are efficiently impacted in the shear strength detection of RC deep beam.

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Section: Optimization Methodsmentioning

confidence: 99%

Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models

et al. 2022

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“…Machine learning has been utilized to predict the performance of materials or structures in civil engineering [128][129][130][131][132][133][134][135][136]. Due to the high uncertainty, the fatigue loading of concrete is a random process affected by many factors, such as loading period, specimen size, and environment.…”

Section: Machine Learningmentioning

confidence: 99%

Analytical Models of Concrete Fatigue: A State-of-the-Art Review

Wei¹,

Makhloof²,

Ren³

2023

Computer Modeling in Engineering &Amp; Sciences

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Fatigue failure phenomena of the concrete structures under long-term low amplitude loading have attracted more attention. Some structures, such as wind power towers, offshore platforms, and high-speed railways, may resist millions of cycles loading during their intended lives. Over the past century, analytical methods for concrete fatigue are emerging. It is concluded that models for the concrete fatigue calculation can fall into four categories: the empirical model relying on fatigue tests, fatigue crack growth model in fracture mechanics, fatigue damage evolution model based on damage mechanics and advanced machine learning model. In this paper, a detailed review of fatigue computing methodology for concrete is presented, and the characteristics of different types of fatigue models have been stated and discussed.

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“…The coefficient of determination of their model for the testing set was 0.92. Shahbazian et al [25] calibrated the weights and biases of an artificial neural network (ANN) for forecasting the shear strength of reinforced concrete beams using the Tabu search training algorithm and 248 experimental results. For testing data, their model had an R 2 value of 0.94.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Barkhordari

Feng

Tehranizadeh

2022

Period. Polytech. Civil Eng.

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Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength.

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Shear Strength Determination in RC Beams Using ANN Trained with Tabu Search Training Algorithm

Cited by 5 publications

References 53 publications

Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models

Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models

Analytical Models of Concrete Fatigue: A State-of-the-Art Review

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

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