Pengfei Tang, Yecheng Dai, Changheng Lu, Shaowei Hu
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引用次数: 0
Abstract
The structural performance of cold-formed steel (CFS) built-up sections under fire is significant in structural engineering. This study presents an efficient and reliable machine learning (ML) framework for predicting the axial capacity of CFS face-to-face (FTF) built-up channel sections at elevated temperatures. Using finite element (FE) datasets from the existing literature, the axial capacity of CFS-FTF built-up channel sections at elevated temperatures is predicted using different ML models, comprising Support Vector Machine, Radial Basis Neural Network, Artificial Neural Network, Extreme Learning Machine, Convolutional Neural Network and Boosting. Finally, the Boosting prediction was interpreted by the SHapley Additive exPlanations method to determine the significance of input features. The ML-based framework proposed in this study could offer a promising alternative for researchers and engineers to efficiently and effectively predict the axial capacity of CFS-FTF built-up channel sections at elevated temperatures.
期刊介绍:
Structures aims to publish internationally-leading research across the full breadth of structural engineering. Papers for Structures are particularly welcome in which high-quality research will benefit from wide readership of academics and practitioners such that not only high citation rates but also tangible industrial-related pathways to impact are achieved.