Comparison of Fresh and Hardened Properties of Self-Compacting Concrete Mixture From Different Aspect Ratio of Steel Fiber View Point

Ouedraogo, Hassane Amidou; Özen, Süleyman; Kobya, Veysel; Sağıroğlu, Serkan; Mardani‐Aghabaglou, Ali

doi:10.3992/jgb.16.1.115

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…To train a ML model effectively and accurately, a sufficient amount of data is essential. The dataset used in this study was collected from 15 published papers [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] and a total of 161 groups of data were employed to build ML models. To eliminate the influence of fibre type, the dataset was limited to the data from SCC reinforced with 2D hookedend steel fibres only.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms

Cui

Kulasegaram

2023

International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Con

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With the development of big data processing technology and the continuous improvement of computer operation ability, machine learning has achieved remarkable results in recent years. Applying machine learning to solve engineering problems is gaining more attention from researchers. Steel fibre-reinforced self-compacting concrete (SFRSCC) is a new type of composite material prepared by combining the advantages of the high fluidity of self-compacting concrete (SCC) and the high toughness of steel fibre-reinforced concrete. However, the performance of SFRSCC is influenced by many factors such as water-binder ratio, mineral powder content and steel fibre content. This study aims to predict the mechanical properties of SFRSCC mixes based on datasets collected from the literature. In the presented work, the machine learning algorithms are employed to investigate the effect of SCC compositions and steel fibre on the performance of SFRSCC. The models used for the prediction are support vector regression (SVR) and artificial neural network (ANN). In both models, input variables are set to be water to binder ratio, sand to aggregate ratio, maximum size of coarse aggregate, amount of other mix components (e.g., superplasticizers, limestone powder, fly ash), volume fraction and aspect ratio of steel fibre, and curing age. The output variables are flexural strength and compressive strength of SFRSCC specimens. The performances of machine learning models are evaluated by comparing the predicted results with experimental results obtained from the literature. Furthermore, a comparative study is performed to select the best-proposed model with better accuracy.

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Section: Dataset Descriptionmentioning

confidence: 99%

Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms

Cui

Kulasegaram

2023

International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Con

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“…Along with the effects of external stresses, these cracks grow and affect the mechanical properties and stress-strain behavior of the concrete negatively. One of the most preferred methods to improve these properties in concrete is the addition of steel fiber in concrete mixtures (Alex & Arunachalam, 2019;Ouedraogo et al, 2021). These randomly distributed fibers increase the occupancy and limit the cracks at different stages of the concrete, preventing their growth and distribution of the internal stresses.…”

Section: Introductionmentioning

confidence: 99%

Specimen size and shape effects on strength of concrete in the absence and presence of steel fibers

Mardani‐Aghabaglou

Bayqra

Nobakhtjoo

2021

rdlc

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In this research, the effects of size and shape on compressive and splitting tensile strength of fibrous and non-fibrous concrete specimens with different characteristic strength were investigated. With this aim, both fibrous and non-fibrous 10 different concrete mixtures with 0.3, 0.4, 0.5, 0.6 and 0. 7 Water/Cement ratio were prepared. In the fibrous mixture specimens, the total amount of steel fibers to 1% by total volume. In the entire specimens, 42.5 R type Portland cement were used as bonding elements while crushed lime stones in 3 sizes were added to the mixtures. Furthermore, to evaluate the influence of size and shapes over specimens’ strength, for each concrete mixtures two 10 cm and 15 cm cubic specimen beside two 10×20 cm and 15×30 cm cylindrical specimens were prepared as well. The prepared specimens were subjected to compressive and splitting tensile tests. The results showed that, regardless of the fiber amount and specimens’ shapes, the decrease in specimens’ size resulted in higher strength. But in the high ratio of Water/Cement and fibrous mixtures, the mentioned behavior was not observed.

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Effect of fiber type, size, and utilization rate on mechanical and thermal properties of lightweight concrete facade panels

Beytekin,

Kaya,

Mardani

et al. 2024

Structural Concrete

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It was understood that various studies were carried out on the strength, permeability, durability, increasing the thermal performance of lightweight concrete facade panels, and sustainable and energy‐efficient concepts. It was observed that fiber was added to the mixture to improve the properties in question. However, it was determined that contradictory results were obtained due to the large number of active parameters and whether the fiber was distributed homogeneously in the matrix. In this study, the effects of fiber type, length and usage rate on the strength, energy absorption capacity, elasticity modulus, water absorption, and thermal performance of lightweight concrete mixtures were investigated. For this purpose, three different types of fibers of different lengths: polypropylene (3, 6, and 12 mm), glass (13 and 25 mm) and polyamide (6 and 12 mm) were used at 0%, 0.25%, 0.50%, and 0.75% of the total volume. It was determined that the mixture containing 0.25% polypropylene fiber with a length of 3 mm exhibited the best performance in terms of both mechanical and thermal properties. In terms of these features, it was understood that the mixtures with 12 mm polypropylene with a usage rate of 0.75% and 6 mm polyamide fibers with a usage rate of 0.50% had the weakest performance.

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Comparison of Fresh and Hardened Properties of Self-Compacting Concrete Mixture From Different Aspect Ratio of Steel Fiber View Point

Cited by 4 publications

References 14 publications

Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms

Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms

Specimen size and shape effects on strength of concrete in the absence and presence of steel fibers

Effect of fiber type, size, and utilization rate on mechanical and thermal properties of lightweight concrete facade panels

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