The main objective of this paper is to develop machine learning (ML) models for predicting the seismic responses of steel moment frames. For this purpose, four boosting ML techniques-gradient boosting, XGBoost, LightGBM, and CatBoost-were developed in Python. To create an inclusive dataset, 92,400 nonlinear time-history analyses were performed on 1,848 steel moment frames under 50 earthquakes using OpenSeesPy. Geometric configurations, structural properties, and ground motion intensity measures were considered as the inputs for the models. The outputs included maximum global drift ratio (MGDR), maximum interstory drift ratio (MIDR), base shear coefficient (BSC), and maximum floor acceleration (MFA). The study also investigated the effectiveness of the ML models in estimating fragility curves for an 8-story steel frame at different performance levels. Finally, a web application was developed to facilitate the estimation of the peak dynamic responses for steel moment frames. The results show that the LightGBM and CatBoost models demonstrate superior predictive performance, with coefficient of determinations (R2) higher than 0.925. Furthermore, the LightGBM models can estimate the fragility curves with minimal errors (e.g., the relative errors in the median values of the predicted curves are less than 10%).

中文翻译：

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使用增强机器学习技术预测钢矩框架的非线性动力响应并生成地震脆性曲线


本文的主要目标是开发机器学习 （ML） 模型，用于预测钢弯矩框架的地震响应。为此，在 Python 中开发了四种提升 ML 技术——梯度提升、XGBoost、LightGBM 和 CatBoost。为了创建一个包容性数据集，使用 OpenSeesPy 对 50 次地震下的 1,848 个钢力矩框架进行了 92,400 次非线性时程分析。几何配置、结构特性和地震动强度测量被视为模型的输入。输出包括最大全局漂移比 （MGDR）、最大层间漂移比 （MIDR）、基底剪切系数 （BSC） 和最大地板加速度 （MFA）。该研究还调查了 ML 模型在估计不同性能水平下 8 层钢框架的脆弱性曲线方面的有效性。最后，开发了一个 Web 应用程序，以促进钢力矩框架的峰值动态响应的估计。结果表明，LightGBM 和 CatBoost 模型表现出卓越的预测性能，决定系数 （R2） 高于 0.925。此外，LightGBM 模型可以以最小的误差估计脆弱性曲线（例如，预测曲线中位数的相对误差小于 10%）。