The effect of hybrid steel fiber on the properties of fresh and hardened self-compacting concrete

Türk

Tanyıldızı

2022

Structural Concrete

In this work, to estimate the compressive, splitting tensile, and flexural strength of self‐compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep‐learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high‐range water‐reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber–reinforced SCC with higher accuracy than SVR.

Section: Resultsmentioning

confidence: 95%

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Türk

Tanyıldızı

2022

Structural Concrete

“…The enhancement in splitting tensile strength could be attributed to the presence of higher content of macro steel fiber (1% by volume) as well as the inclusion of PVA fiber as microfiber. Due to having longer length and higher elastic modulus, macro steel fiber was more effective in the transfer of tensile stress resulting in the prevention of macro cracks 54 . Some researchers 17,18,55 also found that the hooked end of macro steel fiber caused the crack bridging and thus, an enhancement in the splitting tensile strength was obtained.…”

Section: Resultsmentioning

confidence: 99%

Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

Türk

Tanyıldızı

2022

Structural Concrete

Deep auto‐encoders and long short‐term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k‐nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self‐compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty‐eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto‐encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber‐reinforced SCC was superior to the other prediction models.

“…This is because, long steel fibers with hooked end were more effective in the transfer of tensile stress and they had higher elastic modulus with regards to short steel fibers. Tabatabaeian et al [46] and Haddadou et al [47] also proved the effectiveness of HL on the bridging of cracks. On the other hand, adding SS into concrete led to a reduction in splitting tensile strength values for all fiber volume fractions for 7 curing days while for 28 curing days, the splitting tensile strength of SS-0.4 and SS-0.8 increased with 14.28% and 9.52%, respectively, with regards to that of control specimen.…”

Section: Effects Of Fiber Type and Shape On Splitting Tensile Strengthmentioning

confidence: 89%

Effect of Fiber Type, Shape and Volume Fraction on Mechanical and Flexural Properties of Concrete

Başsürücü

Fenerli

Journal of Sustainable Construction Materials and Technologies

et al. 2022

An experimental study was herein presented focusing the effect of different type, shape and volume fraction of fibers on the hardened properties of concrete including compressive, splitting tensile and flexural strengths at 7 and 28 curing days. A control concrete mixture with no fiber was prepared and six fiber reinforced concrete mixtures were designed by using two different types of fibers which were steel fibers with different shapes (short straight and hooked end) and polypropylene fiber with the volume fraction of 0.4% and 0.8%. The load-deflection curves and toughness of the specimens were analyzed based on ASTM C1609. The results showed that the utilization of short straight steel fibers with 0.8% volume fraction was most efficient at improving the compressive strength while the use of 0.8% long hooked end steel fibers provided better splitting tensile and flexural strengths. Besides, the long hooked end steel fibers with the volume fraction of 0.8% contributed to an excellent deflection hardening behavior resulting in higher load deflection capacity and toughness at peak load, L/600 and L/150. On the other hand, with incorporation of polypropylene fiber, all strength values were decreased regardless of the volume fraction and curing days.