RAGN-L: A stacked ensemble learning technique for classification of Fire-Resistant columns
| dc.contributor.author | Çiftçioglu, AÖ | |
| dc.date.accessioned | 2024-07-18T11:46:28Z | |
| dc.date.available | 2024-07-18T11:46:28Z | |
| dc.description.abstract | One of the main challenges in using reinforced concrete materials in structures is to comprehend their fire resistance. The assessment of fire resistance can be performed in a laboratory environment using fire. However, such tests are time-consuming and expensive, and they may not provide a complete assessment of all relevant properties of a particular tested specimen. To that end, the implementation of machine learning (ML) in the investigation of fire-resistant structural performance would be beneficial, as it would also contribute to the reduction of time and cost problems related to traditional techniques. Here, this research proposes a novel ensemble ML approach to classify columns according to their fire resistance characteristics, supporting the application of ML techniques by fire engineers and scientists. The proposed model, named RAGN-L, combines Random Forest, Adaptive Boosting, and Gradient Naive Bayes, and is stacked using the Logistic Regression approach. RAGN-L is evaluated on real-world databases of reinforced concrete columns and concrete-filled steel tube columns, as well as a synthetic database generated by the TVAE deep learning model. The performance of the proposed solution is compared with ten different ML classifiers based on common statistical metrics, accu-racy, precision, recall, and f1-score, and validated using the k-fold cross-validation approach. The developed algorithm outperforms ten different classifiers in all databases, with classification accuracies of 86.6%, and 99.6% for the real-world and synthetic databases of reinforced concrete columns, respectively, and 88.1% for the real-world database of concrete-filled steel tube columns. | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.other | 1873-6793 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/2759 | |
| dc.language.iso | English | |
| dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
| dc.subject | CONCRETE | |
| dc.subject | STRENGTH | |
| dc.subject | PERFORMANCE | |
| dc.subject | BEHAVIOR | |
| dc.title | RAGN-L: A stacked ensemble learning technique for classification of Fire-Resistant columns | |
| dc.type | Article |