Data and Python Code for Implementing Boosting Machines to Predict Local Buckling Strength of CFS Channels with Staggered Web Perforations

This repository contains the dataset and reproducible Python notebook used to benchmark design-code (DSM) calculations against machine-learning (ML) models for cold-formed steel channel members with and without staggered slots. The data files provide the master experimental dataset and DSM-based comparison sheets for the two specimen families (slotted and solid). The Jupyter notebook loads these files, performs preprocessing, trains/evaluates the ML models, and produces figures/tables comparing model accuracy to DSM.

File inventory

Data.xlsx — Master dataset used for ML training/validation. Contains geometric/material descriptors and the measured target response (bending capacity
𝑀
M, units as in the paper).

data_DSM_slots.xlsx — DSM benchmark calculations for slotted specimens (inputs + DSM-predicted capacities).

data_DSM_slot_1.xlsx — A curated subset of the slotted dataset (used for ablation/cross-checks or quick runs).

data_DSM_solids.xlsx — DSM benchmark calculations for solid (unperforated) specimens.

data_DSM_solid_1.xlsx — A curated subset of the solid dataset (used for ablation/cross-checks or quick runs).

ML paper-Copy1.ipynb — Reproducible Jupyter notebook: data loading, preprocessing, model training (e.g., XGBoost and baselines), metrics, and plots; includes DSM vs ML comparisons.

Notes

Typical feature columns include geometric descriptors and material properties (e.g., web/flange/lip dimensions, thickness, slot geometry descriptors, yield stress 𝑓𝑦 and the measured capacity 𝑀. Exact column names/units are documented in the notebook cells.

All spreadsheets are UTF-8 .xlsx files; the notebook targets Python 3.9+

本仓库收录了用于针对带交错开孔与无开孔冷弯槽钢构件，开展设计规范法（Design Specification Method，DSM）计算与机器学习（Machine Learning，ML）模型基准性能测试的数据集与可复现Python代码笔记本。数据文件包含核心实验数据集，以及两类试件组别（开孔试件与实心试件）的基于DSM的对比工作表。该Jupyter笔记本可加载上述文件，完成数据预处理、机器学习模型的训练与评估，并生成对比模型精度与DSM计算结果的图表与表格。

文件清单
Data.xlsx — 用于机器学习训练与验证的核心数据集，包含几何/材料描述符与实测目标响应（受弯承载力$M$，单位与论文一致）。
data_DSM_slots.xlsx — 开孔试件的DSM基准计算结果（含输入参数与DSM预测承载力）。
data_DSM_slot_1.xlsx — 开孔数据集的精选子集，用于消融实验、交叉验证或快速运行测试。
data_DSM_solids.xlsx — 实心（无穿孔）试件的DSM基准计算结果。
data_DSM_solid_1.xlsx — 实心数据集的精选子集，用于消融实验、交叉验证或快速运行测试。
ML paper-Copy1.ipynb — 可复现的Jupyter笔记本：涵盖数据加载、预处理、模型训练（如XGBoost与基准模型）、评价指标计算与绘图，包含DSM与机器学习模型的对比分析。

说明
典型特征列包含几何描述符与材料属性（如腹板、翼缘、卷边尺寸、板厚、开孔几何描述符、屈服强度（yield stress）$f_y$与实测受弯承载力$M$）。精确的列名与单位详见笔记本单元格内容。
所有电子表格均为UTF-8编码的.xlsx文件；该笔记本适配Python 3.9及以上版本。