Prediction of the strength of concrete radiation shielding based on LS-SVM

Xu, Juncai; Ren, Qingwen; Shen, Zhenzhong

doi:10.1016/j.anucene.2015.05.030

Cited by 47 publications

(18 citation statements)

References 19 publications

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“…The feasibility of the LM training algorithm has recently been underlined as an efficient prediction tool in many engineering sectors and is now gaining growing attention in engineering research (Deo and Şahin, 2015;Juncai et al, 2015;Jebur et al, 2018b). These technical papers have reported the outstanding performance of the LM algorithm over the classical artificial neural networks methods.…”

Section: Implementation and Mathematical Background Of The Lm Algorithmmentioning

confidence: 99%

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Jebur

Atherton

Khaddar

et al. 2018

European Journal of Environmental and Civil Engineering

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Section: Implementation and Mathematical Background Of The Lm Algorithmmentioning

confidence: 99%

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Jebur

Atherton

Khaddar

et al. 2018

European Journal of Environmental and Civil Engineering

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“…The analysis in Section 3.1 indicates that the learning and generalization capabilities of the data-driven model greatly depend on the penalty coefficient and kernel function parameter . The grid search algorithm (GSA) is a popular method used to obtain the optimal solution of ( , ) [42]. Therefore, this study adopts the GSA to determine the two parameters for conducting the -fold cross-validation of each set of parameters.…”

Section: Model Parameter Optimizationmentioning

confidence: 99%

Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

Wan

Zhang

Rong

et al. 2016

Scientific Programming

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The differences in supply chains and their competitiveness depend on the differences in supply chain value creation systems. On the basis of the theory of value cocreation, this study investigates the interactive value creation of consumer electronics supply chains from the perspective of the interaction and added value created by the main value creation bodies in supply chains. Least squares support vector machine (LS-SVM) is innovatively introduced into the study on consumer electronics supply chains. A data-driven model is also established, the parameters of the method and kernel functions are optimized and selected, and an LS-SVM algorithm of consumer electronics supply chains is proposed to deal with the limited number of samples. Then, an empirical analysis of the top 10 smartphone supply chains in the Chinese market is conducted, and the LS-SVM model and other forecasting methods are compared. Results suggest that the LS-SVM model achieves a good predictive accuracy. This study also analyzes the value-adding structure of supply chains from the perspective of interaction and enriches the theory of value creation among supply chains. This study is conducive to helping consumer electronics enterprises to conduct market analyses and determine value growth points accurately.

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“…This process leads to setting more compatible parameters of a network [14][15][16][17][18]. Different intelligent models have been used to investigate various characteristics of concrete [19][20][21][22]. In the case of ANNs, Öztaş et al [23] successfully used this tool for predicting the slump and compressive strength of high strength concrete.…”

Section: Introductionmentioning

confidence: 99%

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

et al. 2019

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Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.

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Prediction of the strength of concrete radiation shielding based on LS-SVM

Cited by 47 publications

References 19 publications

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

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