A new DEM calibration method for wet and stick materials based on the BP neural network

Liu, Zhiyuan; Yuan, Jianming; Shen, Jiahe; Hu, Yan; Chen, Silong

doi:10.1016/j.powtec.2024.120228

Powder Technology

2024

DOI: 10.1016/j.powtec.2024.120228

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A new DEM calibration method for wet and stick materials based on the BP neural network

Zhiyuan Liu,

Jianming Yuan,

Jiahe Shen

et al.

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2024

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Cited by 4 publications

References 32 publications

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Predictive slope stability early warning model based on CatBoost

Cai,

Yuan,

Zhou

2024

Sci Rep

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A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77058-6.

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Predictive slope stability early warning model based on CatBoost

Cai,

Yuan,

Zhou

2024

Sci Rep

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Research on enterprise economic efficiency assessment and management decision-making based on big data analysis

Du,

Sun

2024

Applied Mathematics and Nonlinear Sciences

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Enterprise economic efficiency is an important indicator for measuring the input and output of enterprises, and it has a certain guiding role in the formulation of enterprise management decisions. This paper constructs an enterprise economic efficiency assessment model using big data analysis and explores the application of big data analysis in enterprise management decision-making. First, we extract the principal component factors for measuring enterprise economic efficiency indexes using principal component analysis, and then construct the comprehensive evaluation function of enterprise economic efficiency based on this. The particle swarm algorithm is used to optimize the BP neural network, and the PSO-BP neural network model is constructed to intelligently evaluate the economic efficiency of the enterprise. This paper extracts three principal components of enterprise economic efficiency, namely “enterprise solvency and profitability,” “enterprise operating ability,” and “net asset growth ability,” and achieves a cumulative variance explanation rate of 84.431%. The PSO-BP neural network model and the standard BP neural network model satisfy the error criterion of MAPE<0.001 when they are trained about 170 times and 820 times, respectively, and the number of operations of the former is only about 20.73% of that of the latter, which greatly shortens the running time of the model and indicates that the PSO-BP-based enterprise economic efficiency assessment model in this paper is practical. Practicality. This paper serves as a reference for the application of big data analysis in enterprise economic benefit assessment and management decision formulation.

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Research on Modified Thermal Barrier Coatings Against CMAS Corrosion Driven by Mechanism–Data Hybrid Model

Ye,

Wu,

et al. 2024

Coatings

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With the development of high-efficiency gas turbine engines and increasing inlet temperatures, the performance of thermal barrier coatings (TBCs) for hot-section components has been more severely challenged. The doping of multi-element rare earth elements significantly improves the thermodynamic properties and chemical compatibility of thermal barrier coatings so that the application performance of coatings in high-temperature environments is enhanced considerably. In this work, the doped coatings prepared by REYSZ (RE = La, Sm, Nd) were investigated and characterized in terms of crystal structure, elastic properties, and thermal–mechanical properties based on the first-principles approach, combined with various empirical and semi-empirical formulations, and a predictive model for resistance to CMAS corrosion based on machine learning approaches. The results showed that the tetragonal phase REYSZ material was mechanically stable, had a large strain damage tolerance, and was not easy to fracture under applied loads and thermal shocks. In terms of CMAS corrosion resistance, the NdYSZ interfacial model had a lower surface energy (3.130 J/m2) and Griffith fracture energy (6.934 J/m2) compared with the conventional YSZ model, and Nd2O3 had the potential to improve the CMAS corrosion resistance of zirconia-based material for thermal barrier coatings. By evaluating the machine learning prediction models, the regression coefficients of the two algorithms were 0.9627 and 0.9740, and both these two prediction models showed high prediction accuracy and strong robustness. Ultimately, this work presented a novel mechanism–data hybrid method, which would facilitate the efficient development of TBC new materials for anti-CMAS corrosion.

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A new DEM calibration method for wet and stick materials based on the BP neural network

Cited by 4 publications

References 32 publications

Predictive slope stability early warning model based on CatBoost

Predictive slope stability early warning model based on CatBoost

Research on enterprise economic efficiency assessment and management decision-making based on big data analysis

Research on Modified Thermal Barrier Coatings Against CMAS Corrosion Driven by Mechanism–Data Hybrid Model

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