Modeling slump flow of concrete using second-order regressions and artificial neural networks

Yeh, I-Cheng

doi:10.1016/j.cemconcomp.2007.02.001

Cited by 265 publications

(103 citation statements)

References 9 publications

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“…Empirical Comparison: Case Study. In this section, we illustrate the behavior of FastPCS on a real data problem from the field of engineering: the Concrete Slump Test Data set [12]. This dataset includes 103 data-points, each corresponding to a different type of concrete.…”

Section: Simulation Results (B)mentioning

confidence: 99%

Finding multivariate outliers with FastPCS

Vakili¹,

Schmitt²

2014

Computational Statistics & Data Analysis

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The Projection Congruent Subset (PCS) is a new method for finding multivariate outliers. Like many other outlier detection procedures, PCS searches for a subset which minimizes a criterion. The difference is that the new criterion was designed to be insensitive to the outliers. PCS is supported by FastPCS, a fast and affine equivariant algorithm which is also detailed. Both an extensive simulation study and a real data application from the field of engineering show that FastPCS performs better than its competitors.

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Section: Simulation Results (B)mentioning

confidence: 99%

Finding multivariate outliers with FastPCS

Vakili¹,

Schmitt²

2014

Computational Statistics & Data Analysis

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“…Small (N ∼ 150) Concrete slump (CS), (Yeh 2007), (103,7) Uniform sampling (US), (Kartal 2007), (160,10) Medium (N ∼ 500) PM10 (Aldrin 2006), (500,7) Forest fires (FF) (Cortez and Morais 2007), (517,11) While validating the models, 3-fold CV is used as described in Sect. 2.4.…”

Section: Sample Size (N)mentioning

confidence: 99%

A computational approach to nonparametric regression: bootstrapping CMARS method

2015

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Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a special case of nonparametric regression, conic multivariate adaptive regression splines (CMARS), a statistical machine learning algorithm. CMARS is the modified version of the well-known nonparametric regression model, multivariate adaptive regression splines (MARS), which uses conic quadratic optimization. CMARS is at least as complex as MARS even though it performs better with respect to several criteria. To achieve a better performance of CMARS with a less complex model, three different bootstrapping regression methods, namely, random-X, fixed-X and wild bootstrap are applied on four data sets with different size and scale. Then, the performances of the models are compared using various criteria including accuracy, precision, complexity, stability, robustness and computational efficiency. The results imply that bootstrap methods give more precise parameter estimates although they are computationally inefficient and that among all, random-X resampling produces better models, particularly for medium size and scale data sets.

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“…The workability of concrete-defined as the ease with which fresh concrete can be mixed, transported, and subsequently placed and compacted-is therefore of prime concern during the design mix of RMC. The workability of concrete is a function of the rheological properties of the cement paste, internal friction between the aggregate particles, and the external friction between the concrete and the surface of the framework (Yeh, 2007). The complexities of the mentioned associations have made it difficult to quantitatively measure the workability of concrete.…”

Section: Introductionmentioning

confidence: 99%

Modeling Slump of Ready Mix Concrete using Artificial Neural Network

Chandwani¹,

Agrawal²,

Nagar³

et al. 2015

IJTech

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With rapid growth in the construction industry, Ready Mix Concrete (RMC) is playing a key role in offering high-quality customized concrete to contractors and builders. The workability of concrete involves early stage operations of concrete: placing, compaction, and finishing. Since RMC is manufactured at a plant and transported to the construction site, loss of workability is of prime concern due to the considerable time interval between mixing and placing of concrete. Workability of concrete is measured using a slump test to evaluate the life of the concrete during its transportation phase and the uniformity of the concrete from batch to batch. The proportions of cement, fly ash, coarse aggregates, fine aggregates, water, and admixtures in the concrete govern its workability or slump value. In this study, an Artificial Neural Networks (ANNs) learning from past examples gathered from a RMC plant were used to model the functional relationship between the input parameters and the slump value of concrete. The ANN model provided promising results compared to first-order and second-order regression techniques for modeling the unknown and complex relationships exhibited by the design mix proportions and the slump of concrete. The neural network synaptic weights that control the learning mechanism of ANN were further used to compute the percentage of relative importance of each constituent of RMC on the slump value, providing insight into the composite nature of concrete. The technique presented in the study will enable technical staff to quickly estimate the slump of RMC based on its design mix constituents without having to perform multiple design mix trials in order to achieve a customized slump value.

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Modeling slump flow of concrete using second-order regressions and artificial neural networks

Cited by 265 publications

References 9 publications

Finding multivariate outliers with FastPCS

Finding multivariate outliers with FastPCS

A computational approach to nonparametric regression: bootstrapping CMARS method

Modeling Slump of Ready Mix Concrete using Artificial Neural Network

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