Shear strength of reinforced concrete deep beams – A review with improved model by genetic algorithm and reliability analysis

Shahnewaz, Md; Rteil, Ahmad; Alam, M. Shahria

doi:10.1016/j.istruc.2019.09.006

Cited by 46 publications

(19 citation statements)

References 17 publications

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“…Algorithm 1 represents different steps of a GA algorithm. This algorithm has been used to train an ANN in a number of previous studies [17,32,44,47,57,77].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Kandiri

Sartipi²,

Kioumarsi

2021

Applied Sciences

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Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

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“…Algorithm 1 represents different steps of a GA algorithm. This algorithm has been used to train an ANN in a number of previous studies [17,32,44,47,57,77].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Kandiri

Sartipi²,

Kioumarsi

2021

Applied Sciences

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“…Examples of GA applied to structural engineering include the optimization of reinforced concrete beams [4], [5], steel-concrete composite girders [6], spatial steel frames [7], railway viaducts [8] and bridges [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Optimum design of a composite floor system considering environmental and economic impacts

Arpini

Loureiro

Breda

et al. 2022

Rev. IBRACON Estrut. Mater.

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The composite floor system, composed of steel deck and concrete slab, generates more efficient and economical structures. On the other hand, the design of this type of structure has a high complexity level due to the consideration of several variables. In this respect, the objective of this paper is to present the formulation of the optimization problem for a composite floor system (steel and concrete) considering such environmental as economic impacts. To formulate the optimization problem, the reduction of environmental impact was adopted as an objective function - assuming the CO2 emission and the finance cost as parameters. The restrictions were taken by the limiting states imposed in standard NBR 8800:2008. The computer program was developed via Matlab R2016a and the optimization process was carried out using the Genetic Algorithm toolbox existing in this platform. Two application examples of the formulation at hand are presented: the first from the literature and the second from an existing building - in both situations the influences of different concrete compressive characteristic strengths were analyzed. The results of the optimization problem show a reduction in geometry and, consequently, in its weight. The solution found by the program reduces by up to 17.70% of CO2 emissions and 17.47% of the finance cost. When was applying different concrete compressive strengths, the optimal solution for environmental impact did not get the lowest cost. In general, the steel deck formwork obtained the highest percentage of environmental impact, while the beams and girders, with the same shape configuration, had the highest finance cost. Therefore, it is shown that the optimal design solution to CO2 emissions is not always the better solution for the finance cost.

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“…So using deep beams is an effective solution that could increase the durability of structures [5][6][7]. Due to the large height of deep beams, the primary type of damage is shear damage [8][9][10]. For deep beams, cracks often appear quite early, in the direction of primary compressive stress, perpendicular to the direction of tensile stress.…”

Section: Introductionmentioning

confidence: 99%

“…According to studies [4,[23][24][25], several important parameters have been identified, including compressive strength of concrete, yield strength of longitudinal and transverse reinforcement, the ratio of effective depth to breadth, as well as the main reinforcement ratio. In fact, the relationship between the parameters and the shear capacity of deep beams is nonlinear [9,12,23]. Consequently, building an accurate model that can accurately estimate shear strength based on mathematical equations is challenging [26].…”

Section: Introductionmentioning

confidence: 99%

On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

Nguyen

Thi

et al. 2021

Complexity

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This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers.

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Shear strength of reinforced concrete deep beams – A review with improved model by genetic algorithm and reliability analysis

Cited by 46 publications

References 17 publications

Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Optimum design of a composite floor system considering environmental and economic impacts

On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

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