Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique

Boukhatem, Bakhta; Kenai, Said; Hamou, A.; Ziou, Dj.; Ghrici, Mohamed

doi:10.12989/cac.2012.10.6.557

Cited by 37 publications

(18 citation statements)

References 15 publications

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“…It was also successfully applied as a technique for reducing the dimensionality of ANN inputs in a variety of engineering applications [10][11][12][13]. Mathematically, PCA is an orthogonal projection technique that projects multidimensional observations represented in a subspace of dimension m (m is the number of observed variables) in a subspace of lower dimension (L < m) by maximizing the variance of the projections.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Neural networks and principle component analysis approaches to predict pile capacity in sand

Benali

Nechnech

Boukhatem³

et al. 2018

MATEC Web Conf.

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Abstract. Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Neural networks and principle component analysis approaches to predict pile capacity in sand

Benali

Nechnech

Boukhatem³

et al. 2018

MATEC Web Conf.

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show abstract

“…It was also successfully applied as a technique for reducing the dimensionality of ANN inputs in a variety of engineering applications (e.g., Harkat 2003;Kuniar, Waszczyszyn 2006;Shin et al 2008;Boukhatem et al 2012). Mathematically, PCA is an orthogonal projection technique that projects multidimensional observations represented in a subspace of dimension m (m is the number of observed variables) in a subspace of lower dimension (L < m) by maximizing the variance of the projections.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…This represents a very important step before designing a model. The Principal Component Analysis (PCA) is then used and considered as a statistical tool for the elimination of correlations between the data as well as reducing the representation size of these data or data compression (Bellamine, Elkamel 2008;Boukhatem et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Benali

Boukhatem

Hussien

et al. 2017

Journal of Civil Engineering and Management

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Abstract. This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of uncemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on largeand low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabilities and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches.

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“…PCA is a statistical analysis method that turns multiple variables into a few independent comprehensive variables. It is used to reduce data dimensionality [14]. Let…”

Section: A Principal Component Analysis and Normalizationmentioning

confidence: 99%

Analog Circuit Soft Fault Diagnosis based on PCA and PSO-SVM

Wang¹,

Sun²,

Wang³

2013

JNW

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Regarding to the complexity and diversity of analog circuit fault, a principal component analysis(PCA) and particle swarm optimization(PSO) support vector machine(SVM) analog circuit fault diagnosis method is proposed. It uses principal component analysis and data normalization as preprocessing, then reduced dimension fault feature is putted into support vector machine to diagnosis, and particle swarm optimization is used to optimize the penalty parameters and the kernel parameters of SVM, that improve the recognition rate of the fault diagnosis. The simulation results show that the proposed diagnosis model can perform analog circuit fault diagnosis effectively, and has higher fault diagnosis rates.

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Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique

Cited by 37 publications

References 15 publications

Neural networks and principle component analysis approaches to predict pile capacity in sand

Neural networks and principle component analysis approaches to predict pile capacity in sand

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Analog Circuit Soft Fault Diagnosis based on PCA and PSO-SVM

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