Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns
Machine learning methods are widely used to predict the bearing capacity of concrete-filled steel tubular (CFST) columns. However, in addition to this task, the engineer often faces the inverse problem: to determine what cross-section dimensions of the CFST column are required for given loads. This...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/9/1438 |
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