Effects of eccentric loading on performance of concrete columns reinforced with glass fiber-reinforced polymer bars

Shakouri Mahmoudabadi, Nasim; Bahrami, Alireza; Saghir, Saba; Ahmad, Afaq; Iqbal, Muhammad; Elchalakani, Mohamed; Özkılıç, Yasin Onuralp

doi:10.1038/s41598-023-47609-4

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…The learning algorithms optimize these parameters to minimize the difference between the network's predicted outputs and the desired outputs, which is the process known as training. The weights are changed based on the difference between the target value and the predicted value [43], [44], [45]. In addition to the optimization algorithm, other factors can affect the performance of a neural network, such as the architecture of the network and the amount and quality of the training data [46].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Wahab,

Suleiman,

Shabbir

et al. 2024

JoCEF

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This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on eight parameters, including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (f_co^'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain (?_co), confined concrete strain (?_cc) and the ultimate compressive strength of confined concrete (f_cc^'). Three metaheuristic models are implemented, including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error, and their predicted results are validated against experimental studies and finite element analysis. The study shows that the hybrid PSO model predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale, time-consuming experimental tests that make the process quick and economical.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Wahab,

Suleiman,

Shabbir

et al. 2024

JoCEF

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show abstract

“…Becoming an effective construction material, numerous studies have been dedicated to assessing the capacity of composite structures. For instance, Shakouri Mahmoudabadi et al 23 investigated the behavior of concrete columns fortified with glass fiber-reinforced polymer bars subjected to eccentric loading using experimental and finite element analysis models. They observed that the loading capacity of the specimens declines as the eccentricity rises.…”

Section: Introductionmentioning

confidence: 99%

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Liu,

Liang

2024

Sci Rep

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Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (Pu) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the Pu. In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the Pu pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in Pu prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the Pu of concrete-filled steel tube columns.

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“…Fibers in concrete serve to enhance its mechanical properties, control cracking, and improve durability. They are essential for creating more resilient and robust structures [35][36][37]. Shape memory alloy (SMA) is a type of metal alloy that has different mechanical features that are not found in steel used for construction.…”

Section: Introductionmentioning

confidence: 99%

Improving the Mechanical Properties of Concrete Mixtures by Shape Memory Alloy Fibers and Silica Fume

Xie,

Far,

Mortazavi

et al. 2024

Buildings

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Concrete, as one of the most widely applied materials in buildings, has high environmental impacts. Researchers are continually seeking solutions to mitigate these environmental issues while enhancing the mechanical strength and durability of concrete. However, there is a lack of studies on the effect of combining silica fume (SF) as pozzolanic materials and shape memory alloy (SMA) fibers on the mechanical properties of concrete. Moreover, there is very limited research on the influence of these materials on concrete mixtures after primary failure cracks using the secondary compressive strength test. In this research, 0.1, 0.2, and 0.3% SMA and 5, 7.5, and 10% SF were applied and then subjected to compressive strength, splitting tensile strength, flexural strength, secondary compressive strength, and ultrasonic pulse velocity tests. According to the results, 10% SF is more economical, which increases the compressive, splitting tensile, and flexural strength by 14%, 7%, and 10%, respectively. Also, using 0.3% SMA improves the compressive, splitting tensile, and flexural strength by 2%, 5%, and 8%, respectively. Furthermore, SMA has the ability to reduce the secondary compressive strength compared to other samples, indicating the quality of this material in controlling stress after cracking. Finally, it was indicated that the combined use of these two materials increases the strength parameters.

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Effects of eccentric loading on performance of concrete columns reinforced with glass fiber-reinforced polymer bars

Cited by 10 publications

References 46 publications

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Improving the Mechanical Properties of Concrete Mixtures by Shape Memory Alloy Fibers and Silica Fume

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