Splitting tensile strength of basalt fiber reinforced coral aggregate concrete: Optimized XGBoost models and experimental validation

Sun, Zhen; Li, Yalin; Yang, Yuxi; Su, Li; Xie, Shijie

doi:10.1016/j.conbuildmat.2024.135133

Cited by 26 publications

(3 citation statements)

References 40 publications

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“…The model uses a gradient descent algorithm to minimize the objective function. For a given iteration t, the model updates the predictions based on the negative gradient of the loss function evaluated with the current predictions [31].…”

Section: Xgboost Algorithmmentioning

confidence: 99%

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Myśliwiec,

Kubit,

Szawara

2024

Materials

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This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints’ maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.

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Section: Xgboost Algorithmmentioning

confidence: 99%

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Myśliwiec,

Kubit,

Szawara

2024

Materials

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“…Therefore, before the prediction of students' learning level, data processing methods or data search algorithms can be used to pre-process the initial dataset, in order to effectively improve the prediction accuracy of the model. This reduces forecast errors due to underfitting or overfitting [35,36]. To further analyze the model's ability to predict students' knowledge level, and evaluate the composite model's ability to improve the model, we performed analyses via a confusion matrix.…”

Section: Hyperparameter Adjustment For the Student's Knowledge Level ...mentioning

confidence: 99%

Section: Hyperparameter Adjustment For the Student's Knowledge Level ...mentioning

confidence: 99%

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

Wang,

et al. 2024

Applied Sciences

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By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.

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Novel Strategies for Concrete Restoration: a Deep Dive into Microbially Induced Calcite Precipitation Technology

Gebremedhin,

Eryürük

2024

Iran J Sci Technol Trans Civ Eng

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Splitting tensile strength of basalt fiber reinforced coral aggregate concrete: Optimized XGBoost models and experimental validation

Cited by 26 publications

References 40 publications

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

Novel Strategies for Concrete Restoration: a Deep Dive into Microbially Induced Calcite Precipitation Technology

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