2022
DOI: 10.1007/s40996-022-00893-y
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Application of Random Forest and Multi-layer Perceptron ANNS in Estimating the Axial Compression Capacity of Concrete-Filled Steel Tubes

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Cited by 9 publications
(4 citation statements)
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“…Studies have shown that RF outperforms decision trees with accuracy [37], proving particularly effective where high accuracy is crucial. RF's application extends to concrete-filled steel columns, among other areas [38][39][40].…”
Section: Random Forestmentioning
confidence: 99%
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“…Studies have shown that RF outperforms decision trees with accuracy [37], proving particularly effective where high accuracy is crucial. RF's application extends to concrete-filled steel columns, among other areas [38][39][40].…”
Section: Random Forestmentioning
confidence: 99%
“…Grid search and 10-fold cross-validation were employed by the study to fine-tune the hyperparameters for 5 ML algorithms [38][39][40]43]. Hyperparameters, which are crucial for optimal model performance, are predefined by users and require meticulous optimization [45,46].…”
Section: Hyperparameters Of the ML Modelsmentioning
confidence: 99%
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“…Daneshvar et al [26]; introduced Tools for locating and quantifying damage that are effective and dependable. Arokiaprakash and Selvan [27] Developed a unique learning-based artificial neural network model to forecast the column's axial compression strength. Concrete-filled steel tubular (CFST) columns can withstand axial compression.…”
Section: Introductionmentioning
confidence: 99%