Characterization of Rheological and Mechanical Properties of Self-Compacting Concrete with Indian Standard Gradation and Particle Packing Gradations

Tantri, Adithya; Shenoy, Adithya; Nayak, Gopinatha

doi:10.1007/978-981-15-8293-6_6

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Bailey aggregate grading technique (BAGT) is a particle grading tool, which was initially proposed for asphalt mixes [32][33][34]. However, this technique was found to be most suitable for self-compacting concrete (SCC) where it was purely based on the particle orientation factor, which was necessary where it would be expected that aggregate must self-orient itself to achieve the densified compacted state [35,36]. Further BAGT was found to be more suitable for recycled-based SCC [37].…”

Section: Introductionmentioning

confidence: 99%

Implementation assessment of calcined and uncalcined cashew nut-shell ash with total recycled concrete aggregate in self-compacting concrete employing Bailey grading technique

Tantri

Nayak

Shenoy

et al. 2022

Innov. Infrastruct. Solut.

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The present study concentrates on the performance evaluation of calcined and uncalcined cashew nut-shell ash (UCCNA and CCNA) with treated total recycled concrete aggregate (TRCA) in self-compacting concrete. The achievement of sustainable self-compacting concrete (SCC) is possible by the implication of four stages, which includes TRCA treatment process, gradation selection process through Bailey aggregate grading technique, by considering TRCA replacement percentage with an increment of 25% and up to 100% and by considering UCCNA or CCNA replacement with an increment of 5% and up to 20%. Hardened and fresh properties of SCC have been performed and analyzed based on the compliance requirements of SCC. In addition finding results through microstructure assessment was in line with the findings of the hardened and fresh properties of SCC. In addition, quality and dynamic instability assessments of SCC were analyzed through ultrasonic pulse velocity and drying shrinkage aspects. Besides CO2, the emission rate and the efficiency rate of SCC, composites were analyzed in detail. Overall findings revealed that CCNA-based SCC mixes performed effectively than UCCNA-based SCC; specifically, incorporation of 75% of TRCA with 15% CCNA was found to be optimal. But with regard to shrinkage performance UCCNA found to be better by imputing less shrinkage compared to CCNA-based SCC mixes. Further with regard to efficiency rate of SCC composites revealed the gain of maximum efficiency of about 0.156 MPa/kg CO2/m3 and 0.160 MPa/kg CO2/m3 for 15% and 20% CCNA-based SCC mixes.

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Section: Introductionmentioning

confidence: 99%

Implementation assessment of calcined and uncalcined cashew nut-shell ash with total recycled concrete aggregate in self-compacting concrete employing Bailey grading technique

Tantri

Nayak

Shenoy

et al. 2022

Innov. Infrastruct. Solut.

Self Cite

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“…Ensemble ML uses multiple learning algorithm to obtain better predictive performance as compared to any constituent learning algorithm alone. Therefore, individual and ensemble ML techniques have been compared in this study to identify the superlative model for predicting the hardened properties of concrete (Chou and Pham, 2013;Pouriyeh et al, 2017;Salami et al, 2021;Tantri et al, 2021aTantri et al, , 2021bTantri et al, , 2021cPrayuda et al, 2020;Prasad et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The datasets studied by most of the pervious researchers typically include laboratory data involving concrete produced under controlled conditions. While the concrete produced on site or ready-mix-concrete (RMC) plants is associated with many uncertainties including material and environment factors, most of the times, large variations are found in the compressive strength of same concrete mix, used for in-situ applications, RMC plants and lab-Crete (Tantri et al, 2021a(Tantri et al, , 2021b(Tantri et al, , 2021c. The use of real datasets from the industry are likely to contain more supplementary unaccountable differences, and their effects on the accuracy of concrete strength artificial prediction models still remain unknown (Ben Chaabene et al, 2020;DeRousseau et al, 2019;Balendran et al, 2002;Balendran and Pang, 1995;Ganesan, Rajagopal and Thangavel, 2007).…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data

Kamath

Prashanth

Kumar

et al. 2022

JEDT

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Purpose The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets. Design/methodology/approach In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models. Findings The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable. Originality/value The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

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“…Non-biodegradable wastes which can be recycled or reused as a value-added material in any construction application is a globally oriented challenge [1][2][3][4][5][6][7] . Specifically, after plastic, glass is the major non-biodegradable waste.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of hybrid glass wastes incorporated concrete

U¹,

Bhandary²,

Maddodi³

et al. 2022

Eng. Sci.

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Characterization of Rheological and Mechanical Properties of Self-Compacting Concrete with Indian Standard Gradation and Particle Packing Gradations

Cited by 5 publications

References 2 publications

Implementation assessment of calcined and uncalcined cashew nut-shell ash with total recycled concrete aggregate in self-compacting concrete employing Bailey grading technique

Implementation assessment of calcined and uncalcined cashew nut-shell ash with total recycled concrete aggregate in self-compacting concrete employing Bailey grading technique

Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data

Performance evaluation of hybrid glass wastes incorporated concrete

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