Statistical models to predict fresh and hardened properties of self-consolidating concrete

Long, Wu-Jian; Lemieux, Guillaume; Hwang, Soo-Duck; Khayat, Kamal H.

doi:10.1617/s11527-011-9815-9

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…The variations of R 2 values within the −1 and +1 and −2 and +2 modeled regions for the mechanical and visco-elastic property responses are summarized in Table 12 . The R 2 values within the −1 and +1 region are obtained from the previous investigations [ 15 ]. The correlation coefficient R 2 values are shown to decrease when the modeled region varies from the (−1, +1) range to the (−2, +2) range.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 28 SCC mixture combinations were used in the experimental design. It is important to note that the research work presented herein is an extension of the previous experimental plan presented by the authors [ 15 ]. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted.…”

Section: Introductionmentioning

confidence: 98%

“…Material characteristics and mix design of SCC have a marked effect on all aspects of SCC production and placement, and on the fresh and hardened properties of the concrete [ 13 , 14 ]. The proportioning of SCC often involves the adjustment of several mixture parameters to achieve a compromise between the properties in its fresh and hardened states [ 15 ]. While certain design methods and mathematical approaches for SCC have been published [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], a lack of adequate research studies warrants investigation of SCC designated for prestressed applications.…”

Section: Introductionmentioning

confidence: 99%

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Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete

et al. 2015

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In order to model the effect of mixture parameters and material properties on the hardened properties of, prestressed self-compacting concrete (SCC), and also to investigate the extensions of the statistical models, a factorial design was employed to identify the relative significance of these primary parameters and their interactions in terms of the mechanical and visco-elastic properties of SCC. In addition to the 16 fractional factorial mixtures evaluated in the modeled region of −1 to +1, eight axial mixtures were prepared at extreme values of −2 and +2 with the other variables maintained at the central points. Four replicate central mixtures were also evaluated. The effects of five mixture parameters, including binder type, binder content, dosage of viscosity-modifying admixture (VMA), water-cementitious material ratio (w/cm), and sand-to-total aggregate ratio (S/A) on compressive strength, modulus of elasticity, as well as autogenous and drying shrinkage are discussed. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted. A logical design approach would be to use the existing model to predict the optimal design, and then run selected tests to quantify the influence of the new binder on the model.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete

et al. 2015

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“…Yeh (2006) studied the effect of fly ash replacements and different water-cement ratios on early concrete compressive strength and low and high compressive strength. Long et al (2012) observed the impact of material properties and mixture parameters on self-consolidating concrete. Rahim et al (2013) attempted to quantify the effect of fire-type temperatures on concrete compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effect of construction site factors on concrete compressive strength using designed experiments

Santamaría

Valentin

Huerta

2019

IJEDPO

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Both structured and unstructured factors affect concrete compressive strength. Structured factors, e.g., raw materials, affect concrete production. Unstructured factors, e.g., local conditions, affect concrete quality during the construction phase. The effects of structured factors on concrete metrics are well understood, while there is limited understanding on the effects of unstructured factors. A full 2 5 factorial design was conducted to quantify the effect of five construction site factors including crew experience, compaction method, mixing time, curing humidity and curing temperature. A linear regression model was developed considering significant affecting factors. Model adequacy was evaluated through residual plots. The results indicate that compaction method, mixing time, curing humidity and curing temperature affect concrete compressive strength significantly, with curing temperature having the highest percent contribution (50.0%). The final regression model was used to create a decision-support tool that enables construction workers to find operating conditions and take corrective actions to preserve concrete quality.

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“…Key workability characteristics of SCC can be described in terms of filling ability, passing ability, and stability [ 3 , 4 ]. There are two types of stability characteristics: dynamic and static stability [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Performance-Based Specifications of Workability Characteristics of Prestressed, Precast Self-Consolidating Concrete—A North American Prospective

et al. 2014

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Adequate selection of material constituents and test methods are necessary for workability specifications and performance of hardened concrete. An experimental program was performed to evaluate the suitability of various test methods for workability assessment and to propose performance specifications of prestressed concrete. In total, 33 self-consolidating concrete (SCC) mixtures made with various mixture proportioning parameters, including maximum size and type of aggregate, type and content of binder, and w/cm were evaluated. Correlations among various test results used in evaluating the workability responses are established. It is recommended that SCC should have slump flow values of 635–760 mm. To ensure proper filling capacity greater than 80%, such concrete should have a passing ability that corresponds to L-box blocking ratio (h2/h1) ≥ 0.5, J-Ring flow of 570–685 mm, slump flow minus J-Ring flow diameter ≤75 mm. Moreover, Stable SCC should develop a column segregation index lower than 5%, and rate of settlement at 30 min of 0.27%/h for SCC proportioned with 12.5 or 9.5 mm MSA. It is recommended that SCC should have a plastic viscosity of 100–225 Pa·s and 100–400 Pa·s for concrete made with crushed aggregate and gravel, respectively, to ensure proper workability.

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Statistical models to predict fresh and hardened properties of self-consolidating concrete

Cited by 14 publications

References 2 publications

Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete

Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete

Quantifying the effect of construction site factors on concrete compressive strength using designed experiments

Performance-Based Specifications of Workability Characteristics of Prestressed, Precast Self-Consolidating Concrete—A North American Prospective

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