Applying the Ensemble and Metaheuristic Algorithm to Predict the Flexural Characteristics of Ice
Chengxi Lu, Xiangyu Han
TL;DR
This paper introduces a new AI-based framework combining ensemble and metaheuristic algorithms to accurately predict the flexural strength of ice, which is important for assessing ice structure stability in a warming climate.
Contribution
The novel contribution is the integration of ensemble learning with the Elitist Ant System to improve prediction accuracy of ice flexural properties.
Findings
AdaBoost outperformed other models with an R2 of 0.736 in predicting ice flexural strength.
The Elitist Ant System optimized model parameters efficiently within ten iterations.
Testing method and specimen geometry were identified as the most influential factors in flexural strength prediction.
Abstract
The stability of ice structures in cold regions and polar environments has been increasingly challenged by global warming and climate change, making the accurate estimation of ice flexural properties essential. However, the flexural failure process of ice is highly complex, and the calculated flexural properties are influenced by multiple factors. Hence, several data-driven artificial intelligence models were developed to predict flexural strength, using classification and regression tree (CART), AdaBoost, and Random Forest methods, while the Elitist Ant System (EAS) was applied to optimize model parameters. The EAS procedure converged rapidly within ten iterations and effectively enhanced overall model performance. Compared with the single CART model, ensemble approaches exhibited higher prediction accuracy and better generalization, with AdaBoost achieving the best performance (R2 =…
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Taxonomy
TopicsArctic and Antarctic ice dynamics · Cryospheric studies and observations · Icing and De-icing Technologies