Automated scaffolding safety analysis: strain feature investigation using support vector machines
| Autor: | Sayan Sakhakarmi, Chunhee Cho, Cristian Arteaga, JeeWoong Park |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Scaffold
Computer science business.industry Strain (biology) information science 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Machine learning computer.software_genre 0201 civil engineering Support vector machine Feature (computer vision) 021105 building & construction Artificial intelligence business computer General Environmental Science Civil and Structural Engineering |
| Zdroj: | Canadian Journal of Civil Engineering. 47:921-928 |
| ISSN: | 1208-6029 0315-1468 |
| DOI: | 10.1139/cjce-2019-0150 |
| Popis: | This study developed a methodology that can use real-time strain data for the assessment of scaffolding safety conditions. The researchers identified 23 safety cases of individual member failure with generic global failure for a four-bay, three-story scaffold model and used scaffold member strain values to identify potential failure cases. A computer simulation on the scaffold model generated the strain datasets required for classification with a support vector machine (SVM). The SVM classification demonstrated a stable prediction accuracy after training with a certain number of strain datasets. Furthermore, the 2nd order polynomial kernel function resulted in better prediction compared to other SVM kernel functions. These results imply that the real-time assessment of scaffolding structures is possible with a limited number of training data for machine-learning classification. |
| Databáze: | OpenAIRE |
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