Modeling of strength of high-performance concrete using artificial neural networks

Yeh, I-Cheng

doi:10.1016/s0008-8846(98)00165-3

Cited by 1,113 publications

(508 citation statements)

References 8 publications

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“…This data was published in Yeh (1988), which can be obtained from http://archive.ics.uci.edu/ml/datasets/Con crete?Compressive?Strength and is used to model the compressive strength of high performance concrete based on features like water-to-cement ratio and the content of other cement ingredients. The basic information and the n h parameter for four algorithms are described as Table 6.…”

Section: Concrete Compressive Strength Data Setmentioning

confidence: 99%

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Yang

Zhang

2015

Cogn Neurodyn

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Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

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Section: Concrete Compressive Strength Data Setmentioning

confidence: 99%

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Yang

Zhang

2015

Cogn Neurodyn

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“…For illustration, we first present some results based on the concrete compressive strength data of Yeh [18]. The dataset has 1,030 observations, with eight quantitative input variables and a response variable, concrete compressive strength.…”

Section: Improvement By Variable Augmentation In Real Data Examplesmentioning

confidence: 99%

“…There can also be benefit if RFs are applied in some problems with a much lower dimension, potentially producing better predictions than other procedures. Using the concrete compressive strength data of Yeh as an example [18], we considered a linear regression model on all Table 2.…”

Section: Improvement By Variable Augmentation In Real Data Examplesmentioning

confidence: 99%

Predictor augmentation in random forests

Nettleton

Nordman

2014

Statistics and Its Interface

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Random forest (RF) methodology is an increasingly popular nonparametric methodology for prediction in both regression and classification problems. We describe a behavior of random forests (RFs) that may be unknown and surprising to many initial users of the methodology: out-of-sample prediction by RFs can be sometimes improved by augmenting the dataset with a new explanatory variable, independent of all variables in the original dataset. We explain this phenomenon with a simulated example, and show how independent variable augmentation can help RFs to decreases prediction variance and improve prediction performance in some cases. We also give real data examples for illustration, argue that this phenomenon is closely connected with overfitting, and suggest potential research for improving RFs.

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“…The strength value of concrete and cement can be forecasted using various computational methods such as neural network [1], gene expression programming [2], support vector machines [3], and genetic programming [4]. However, the concrete mortar itself is non-uniform.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Strength Using Floating Centroids Method

Wang

Yang

Abraham

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Concrete is viewed as the most important cementbased composite material in the field of civil engineering. Its strength is considered the most important among its mechanical properties. Although the value of strength can be directly forecasted, the estimation of strength grade remains particularly important because concrete mortar is non-uniform, and practical preparation and curing cannot be fully simulated under laboratory conditions. In this paper, concrete strength grade was predicted by using the floating centroids method neural network classifier, which removes the fixed-centroid constraint and increases the possibility of finding an optimal neural network. Experimental results show that concrete strength prediction performance is improved by employing the floating centroids method.

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Modeling of strength of high-performance concrete using artificial neural networks

Cited by 1,113 publications

References 8 publications

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Predictor augmentation in random forests

Prediction of Concrete Strength Using Floating Centroids Method

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