Artificial Neural Network for Strength Prediction of Fibers’ Self-compacting Concrete

Prasad, M. L. V.; Saha, Prasenjit; Tarafder, Nilanjan

doi:10.1007/978-981-13-1592-3_2

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Furthermore, the ANN model also predicted the compressive strength, splitting tensile strength, and flexural strength with 100%, 94%, and 94%, respectively. Meesaraganda et al 14 devised the ANN for mechanical properties of fibers SCC. The proposed model estimated the compressive strength with 93.80% accuracy.…”

Section: Prediction Results Of the Selected Modelsmentioning

confidence: 99%

“…11 There have been articles in the literature about estimating the strength of concrete containing fiber. [12][13][14] In these articles, deep-learning (DL) model was not used to estimate the mechanical properties of SCC having single fiber and binary, ternary, and quaternary fiber hybridization. The DL has been generally used in crack detection in civil engineering in recent years.…”

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confidence: 99%

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

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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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.

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Section: Prediction Results Of the Selected Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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show abstract

“…ANNs are a type of soft computing approach inspired by the behaviour of the human nervous system. It is widely employed in civil engineering research and technology [86], [87], [88], [89], [90], [91]. Neural network architecture comprises several major components, including inputs, weights, a sum function, an activation function, and outputs.…”

Section: Artificial Neuronal Network (Anns)mentioning

confidence: 99%

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Uddin

et al. 2022

Innov. Infrastruct. Solut.

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Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using AI Approach

Sandhya¹,

Rao²

2022

Lecture Notes in Civil Engineering

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Artificial Neural Network for Strength Prediction of Fibers’ Self-compacting Concrete

Cited by 11 publications

References 21 publications

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

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

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using AI Approach

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