Pengfei Tang, Yecheng Dai, Changheng Lu, Shaowei Hu
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引用次数: 0
摘要
在结构工程中,冷弯型钢(CFS)构建型钢在火灾下的结构性能非常重要。本研究提出了一种高效可靠的机器学习(ML)框架,用于预测 CFS 面对面(FTF)结构槽钢截面在高温下的轴向承载力。利用现有文献中的有限元(FE)数据集,采用不同的 ML 模型(包括支持向量机、径向基神经网络、人工神经网络、极限学习机、卷积神经网络和助推)预测 CFS-FTF 搭建槽段在高温下的轴向承载力。最后,通过 SHapley Additive exPlanations 方法对 Boosting 预测进行解释,以确定输入特征的重要性。本研究中提出的基于 ML 的框架可为研究人员和工程师提供一种有前途的替代方法,以高效、有效地预测 CFS-FTF 建筑渠道断面在高温下的轴向承载能力。
A machine learning framework for predicting the axial capacity of cold-formed steel face-to-face built-up channel sections at elevated temperatures
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.