Effects of Coarse Aggregate Maximum Size on Synthetic/Steel Fiber Reinforced Concrete Performance with Different Fiber Parameters

Al-Baghdadi, Haider M.; Al-Merib, Faiz H.; Ibrahim, Ayoob A.; Hassan, Rafea F.; Hussein, Husam H.

doi:10.3390/buildings11040158

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…The better performance of 12.5 mm granite concrete may be due to the ease of compaction of smaller-sized aggregate concrete while the optimal behaviour of concrete made with blended aggregates may be attributed to the presence of medium-sized aggregates where some voids are filled in the interfacial transition zone (ITZ) which Wei et al, (2020) reported is responsible for higher strength gain. Also, the higher compressive strength attained with concrete containing smaller granite size aligns with reports of some researches (Umasabor & Osayogie, 2020;Al-baghdadi et al, 2021). Concrete specimens showed reduced residual strength with increment in heating temperature as presented in Figure 3 and Table 3.…”

Section: Compressive Strength Behaviour Before and After Heatingsupporting

confidence: 88%

Short-term Study of the Influence of Aggregate Size on the Fire Resistance of Concrete

Ayodele¹,

Olulope²,

Ayeni³

2023

Nig. J. Technol. Dev.

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This study aims to investigate the effect of aggregate size on the fire resistance of concrete. The binder for this investigation was 42.5R CEM II/A-L Portland limestone cement, and the crushed granite sizes were 20mm, 12.5mm, and a blend (20 mm + 12.5 mm). The concrete was designed with a mix ratio of 1:2:4 (batching by weight) and 0.55w/c. The workability of the concrete was determined using the slump method. The concrete specimens were cured for 7, 14, and 28 days by immersion and later exposed to heat temperatures of 26.8 °C (Room temperature), 60 °C, 120 °C, 180 °C, and 240 °C. The compressive strength of normal concrete (without heating) had the optimum with concrete produced with blended aggregate size and conversely the least with 20mm aggregate size concrete. When subjected to heat, the optimal performance noted for the influence of aggregate size on fire resistance was also with concrete produced with combined blend (20 mm + 12.5 mm) sizes. The p-value (Prob > F) for the whole model test is less than 0.05, indicating that there is a significant relationship between the grain size, temperature, and strength loss. The use of blended sizes of 12.5 mm and 20 mm is therefore recommended for concrete works and especially when fire resistance is a requirement.

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Section: Compressive Strength Behaviour Before and After Heatingsupporting

confidence: 88%

Short-term Study of the Influence of Aggregate Size on the Fire Resistance of Concrete

Ayodele¹,

Olulope²,

Ayeni³

2023

Nig. J. Technol. Dev.

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“…Splitting strength and flexural strength tests were performed, following ASTM C496/C496M-17 [27] and ASTM C78/C78M-18 [28], as shown in Figure 3. As reported in past research [24,[29][30][31], the MSSF reduces the brittleness and increases the ductility of FRC, as listed in Table 3. In this table, brittleness ratios were calculated for both mixes by dividing the compressive to flexural strength.…”

Section: Design and Materials Propertiessupporting

confidence: 60%

“…Ordinary Portland cement was used. Based on previous research findings and recommendations, the MSSF content of 1.5% was added to the concrete mixture [15,24]. This fiber has a length of 13 mm with a nominal tensile strength of more than 2100 MPa provided by the manufacturer (see Figure 2).…”

Section: Design and Materials Propertiesmentioning

confidence: 99%

Experimental Study on Performance of Steel Fiber-Reinforced Concrete V-Shaped Columns

et al. 2021

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Structural engineers have used V-shaped columns based on technical requirements. The inclination of the V-shaped column underlines the individual purpose of the base floor. However, there is no any specification or guidance on the design of V-shaped columns to date. The aim of this study is to investigate the behavior of V-shaped reinforced concrete columns with three angles between columns (30°, 60°, and 90°) in order for the results to be used in the design and analysis of the V-shaped column. The impact of using a 1.5% dosage of micro-straight-steel fiber (MSSF) in the concrete mixture was also studied. The results showed that the V-shaped column with 30°, regardless of the concrete type (with and without MSSFs), exhibited crushing at legends when the sample reached the ultimate load, while no cracks occurred at the legends during the test for the other V-shaped columns. Upon increasing the angle of inclination of the V-shaped columns, the ultimate load capacity was decreased by 24%, 23%, and 20% for V-shaped columns with 30°, 60°, and 90° angles of inclination, respectively. The addition of MSSFs in the concrete significantly improved the ultimate axial load and the bending moment compared to the reference specimens with the normal reinforced concrete (NRC). The steel-fiber-reinforced concrete (SFRC) vertical column specimen demonstrated the highest increase in axial load, and the other SFRC V-shaped and flexural specimens showed a minor increase compared to the NRC specimens.

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“…The SFRC mixes containing hooked ISF and their 28-day CS (tested by 150 mm cubic samples) were collected from the literature 11 , 13 , 21 – 33 . Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy).…”

Section: Methodsmentioning

confidence: 99%

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete

Pakzad

Roshan

Ghalehnovi

2023

Sci Rep

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Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. However, the understanding of ISF’s influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC) incorporating hooked ISF based on the data collected from the open literature. Accordingly, 176 sets of data are collected from different journals and conference papers. Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). Several statistical parameters are also used as metrics to evaluate the performance of implemented models, such as coefficient of determination (R2), mean absolute error (MAE), and mean of squared error (MSE). Among different ML algorithms, convolutional neural network (CNN) with R2 = 0.928, RMSE = 5.043, and MAE = 3.833 shows higher accuracy. On the other hand, K-nearest neighbor (KNN) algorithm with R2 = 0.881, RMSE = 6.477, and MAE = 4.648 results in the weakest performance.

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Effects of Coarse Aggregate Maximum Size on Synthetic/Steel Fiber Reinforced Concrete Performance with Different Fiber Parameters

Cited by 15 publications

References 40 publications

Short-term Study of the Influence of Aggregate Size on the Fire Resistance of Concrete

Short-term Study of the Influence of Aggregate Size on the Fire Resistance of Concrete

Experimental Study on Performance of Steel Fiber-Reinforced Concrete V-Shaped Columns

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete

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