Application of back propagation neural network to the modeling of slump and compressive strength of composite geopolymers

Kuang, Fenglan; Long, Zhilin; Kuang, Dumin; Liu, Xiaowei; Guo, Ruiqi

doi:10.1016/j.commatsci.2022.111241

Cited by 32 publications

(10 citation statements)

References 40 publications

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“…Then the optimized weights and thresholds are subjected to BPNN network training. The BPNN model training process is referred to in papers 48,49 …”

Section: Ga‐bpnn Modelmentioning

confidence: 99%

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Numerical study and prediction of water transfer in gas diffusion layer of proton exchange membrane fuel cells under vibrating conditions

Chen

2022

Intl J of Energy Research

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Summary Proton exchange membrane fuel cells (PEMFCs) operate with vibrations that have a significant effect on the transfer of water and gas in the gas diffusion layer (GDL), as well as the conduction of electrons. However, the available research on the effects of vibration on PEMFC is sparse. The main purpose of this study is to investigate the transfer characteristics of water in GDL under different vibration conditions and to optimize the water content of GDL using computational fluid dynamics (CFD) and machine learning method. The results show that the transfer of water in the GDL can be divided into two stages: water accumulation and drainage. In the accumulation stage, vibration has only a small effect on saturation; during the drainage stage, vibration causes saturation to fluctuate regularly. In addition, vibration has a large effect on the water content of GDL. Vibrations with a high frequency and a large amplitude will accelerate the accumulation of water in the GDL. Moreover, horizontal vibration will cause more flow paths, while vertical vibration will generate a relatively stable flow path. In addition, the established genetic algorithm‐back propagation neural network (GA‐BPNN) data‐driven model has good predictive performance for the saturation of water in the GDL. Compared to the physical simulation approach, the data‐driven model has faster computational efficiency and less computational cost and can be used to optimize the water content of GDL.

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“…Then the optimized weights and thresholds are subjected to BPNN network training. The BPNN model training process is referred to in papers 48,49 …”

Section: Ga‐bpnn Modelmentioning

confidence: 99%

“…The BPNN model training process is referred to in papers. 48,49 The prediction accuracy of the model is judged by the root mean square error (RMSE) and the coefficient of determination (R 2 ), which are given by Equations ( 9) and (10), respectively.…”

Section: Numerical Discretizationmentioning

confidence: 99%

Numerical study and prediction of water transfer in gas diffusion layer of proton exchange membrane fuel cells under vibrating conditions

Chen

2022

Intl J of Energy Research

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“…Widely used statistical models included the back-propagation (BP) neural network, the random forest algorithm, the bayesian model, and the cart decision tree algorithm [18][19][20]. The BP neural network model was widely used, had good self-adaptive ability and memory function, could learn and store a large number of input-output mode mapping relationships, and had clear advantages in predicting nonlinear problems [21,22]. The BP neural network was applied in this study.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Early Warning Method for Algal Bloom Risk Based on a Neural Network and a Two-Dimensional Hydrodynamic Model

Luo,

Gu,

Gao

et al. 2024

Pol. J. Environ. Stud.

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Eutrophication of lakes and reservoirs and the occurrence of cyanobacteria blooms are two of the major environmental problems facing the whole world. However, if cyanobacteria bloom outbreaks can be predicted in advance, there is enough time to implement various measures to reduce ecological harm and health risks and greatly reduce economic losses. Yuqiao Reservoir was a typical shallow-water lake reservoir that was also faced with a greater risk of cyanobacteria bloom outbreaks in summer and autumn. In this study, a BP neural network model and a two-dimensional hydrodynamic numerical model were constructed based on meteorological, hydrological, water quality, and terrain data for Yuqiao Reservoir. The neural network model was used to simulate the biogeneration and extinction processes of cyanobacteria bloom biomass, while the two-dimensional hydrodynamic numerical model was used to simulate the migration and accumulation processes of cyanobacteria. The two models were coupled by an algal attenuation coefficient for the shortterm warning of algal bloom. The results of the Nash coefficient evaluation showed that the coupled model had good overall efficiency and could effectively simulate the algal density both in time and space. The results of scenario analysis showed that the change in flow field in a shallow water reservoir had a great influence on algae migration and accumulation, and water transfer, wind speed, and direction all affected the flow field distribution and then the formation of bloom. The model application results showed that the short-term bloom prediction method constructed in this study had advantages in forecasting accuracy and displayed an effect.

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“…Artificial neural networks have become dominant with the development of computational tools and can solve highly nonlinear problems [13]. Many studies have confirmed that artificial neural networks can help predict the slump value [12,[14][15][16] and compressive strength of concrete [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Predicting Concrete Slump using Fly Ash and Stone Powder in Central Vietnam

2024

Teh. vjesn.

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Fly ash and stone powder, which are abundant wastes in Central Vietnam, are viable alternatives to cement and sand in concrete production. However, this replacement may worsen the compressive strength and slump of concrete. This study deals with the prediction of the slump and compressive strength of concrete using fly ash and stone powder in Central Vietnam as cement and sand substitute materials, respectively. First, the Ishikawa diagram was used to analyze the factors affecting the concrete workability and compressive strength, in combination with the method of design of experiment to determine the required number of testing specimens. A total of 72 concrete mixtures with slump of 3 -12 cm and compressive strengths 10 MPa -60 MPa were designed. Subsequently, regression and artificial neural network methods were used to predict the concrete slump and compressive strength. The results demonstrated the high accuracy of the artificial neural network model. In addition, the above models allowed us to determine the proportion of concrete ingredients that met the slump and compressive strength requirements.

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Application of back propagation neural network to the modeling of slump and compressive strength of composite geopolymers

Cited by 32 publications

References 40 publications

Numerical study and prediction of water transfer in gas diffusion layer of proton exchange membrane fuel cells under vibrating conditions

Numerical study and prediction of water transfer in gas diffusion layer of proton exchange membrane fuel cells under vibrating conditions

Short-Term Early Warning Method for Algal Bloom Risk Based on a Neural Network and a Two-Dimensional Hydrodynamic Model

Predicting Concrete Slump using Fly Ash and Stone Powder in Central Vietnam

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