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.

中文翻译：

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预测 CFST 柱极限负载能力的通用 boosting ML 方法

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属性等各种参数如何影响结构的行为至关重要。本研究旨在创建一个综合的 ML 模型，考虑不同截面参数对钢管混凝土 (CFST) 柱极限承载能力 (ULC) 的影响。该模型可帮助工程师做出明智的设计决策。该研究采用了包含 3094 个数据点的大型数据集，这些数据点具有 CFST 柱的不同几何和材料特性。调整输入特征后，使用网格搜索和五重交叉验证对鲁棒的增强 ML 模型（Catboost、LightGBM 和 XGB）进行精心微调。蒙特卡罗模拟用于进一步评估。结果表明，最准确的 XGB 模型具有令人印象深刻的准确性，与专注于单个钢管混凝土柱横截面的现有文献模型相当或更好。然后，使用所选的 XGB 模型通过一维和二维部分依赖图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 塔 ULC 预测的贡献和影响。