Universal boosting ML approaches to predict the ultimate load capacity of CFST columns

Thuy-Anh Nguyen, Khuong Le Nguyen, Hai-Bang Ly
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Abstract

Establishing a universal machine learning (ML) model in structural engineering is vital for understanding how various parameters, like geometry and material properties, influence a structure's behavior. This study aims to create a comprehensive ML model that considers the impact of different cross-sectional parameters on the ultimate load capacity (ULC) of concrete-filled steel tube (CFST) columns. This model assists engineers in making informed design decisions. The study employs a large dataset of 3094 data points with diverse geometric and material properties of CFST columns. After adjusting input features, robust boosting ML models (Catboost, LightGBM, and XGB) are meticulously fine-tuned using grid search and fivefold cross-validation. Monte Carlo simulation is used for further assessment. The results demonstrate that the most accurate XGB model delivers impressive accuracy, comparable to or better than existing literature models that focused on a single CFST column cross-section. The chosen XGB model is then utilized for feature importance analysis, local performance assessment, and sensitivity analysis through 1-D and 2-D partial dependence plots. These analyses help assess the input's contribution and effect on ULC prediction for CFST columns.
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通用增强ML方法预测CFST柱的极限承载能力
在结构工程中建立通用机器学习(ML)模型对于理解几何形状和材料特性等各种参数如何影响结构的行为至关重要。本研究旨在建立一个综合的ML模型,考虑不同截面参数对钢管混凝土(CFST)柱的极限承载能力(ULC)的影响。这个模型帮助工程师做出明智的设计决策。本研究采用了包含3094个数据点的大型数据集,其中包含了不同的CFST柱的几何和材料特性。在调整输入特征后,稳健的增强ML模型(Catboost, LightGBM和XGB)使用网格搜索和五倍交叉验证进行精心微调。蒙特卡罗模拟用于进一步评估。结果表明,最精确的XGB模型提供了令人印象深刻的准确性,与现有的专注于单个CFST柱截面的文献模型相当或更好。然后通过1-D和2-D部分依赖图,利用所选的XGB模型进行特征重要性分析、局部性能评估和敏感性分析。这些分析有助于评估输入对CFST柱ULC预测的贡献和影响。
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