Comprehensive analysis of structural parameters influencing the fundamental period of steel-braced RC buildings using machine learning interpretability

Taimur Rahman, Md. Farhad Momin, Afra Anam Provasha
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Abstract

The accurate prediction of the fundamental period of steel-braced reinforced concrete (RC) buildings is crucial for optimizing seismic design and ensuring structural safety. Traditionally, empirical formulas provided by building codes such as Eurocode 8 and ASCE 7–22 primarily rely on building height to estimate the fundamental period. However, these height-based models often overlook the significant influence of other structural parameters, such as bracing configurations, bracing lengths, and material properties. This study addresses these limitations by offering a comprehensive evaluation of the factors affecting the fundamental period of steel-braced RC buildings, using advanced computational techniques for more precise and interpretable predictions. A dataset comprising 17,280 building models with varied structural configurations was generated using computational simulations. Key parameters, including total building height, bracing type, bracing length, and building dimensions, were systematically varied. The study utilized machine learning techniques and employed SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots as post-hoc interpretability tools to analyze the contributions of structural parameters. Results show that total building height remains the dominant factor, contributing approximately 45% to the predicted fundamental period, while bracing length and bracing type significantly influence the period, reducing it by up to 20%. The inclusion of these parameters improves prediction accuracy and reveals limitations in existing height-based formulas. The study concludes that height alone is insufficient for accurate prediction of the fundamental period in steel-braced RC buildings. Incorporating bracing systems and other structural factors is essential for more reliable seismic design. These findings contribute to the development of more resilient building codes and enhanced seismic performance.

基于机器学习可解释性的钢支撑钢筋混凝土建筑基本工期影响结构参数综合分析
准确预测钢支撑钢筋混凝土结构基本周期对优化抗震设计和保证结构安全具有重要意义。传统上,欧洲规范8和ASCE 7-22等建筑规范提供的经验公式主要依赖于建筑物高度来估计基本周期。然而,这些基于高度的模型往往忽略了其他结构参数的重要影响,如支撑配置、支撑长度和材料性能。本研究通过对影响钢支撑钢筋混凝土建筑基本周期的因素进行全面评估,利用先进的计算技术进行更精确和可解释的预测,从而解决了这些局限性。使用计算模拟生成了包含17,280个不同结构配置的建筑模型的数据集。关键参数,包括建筑总高度,支撑类型,支撑长度和建筑尺寸,被系统地改变。该研究利用机器学习技术,并采用SHapley加性解释(SHAP)和个体条件期望(ICE)图作为事后可解释性工具来分析结构参数的贡献。结果表明,建筑总高度仍然是主导因素,对预测基本周期的贡献约为45%,而支撑长度和支撑类型对预测基本周期的影响显著,最多可减少20%。这些参数的加入提高了预测精度,并揭示了现有基于高度的公式的局限性。研究得出结论,仅高度不足以准确预测钢支撑钢筋混凝土建筑的基本周期。考虑支撑系统和其他结构因素对于更可靠的抗震设计是必不可少的。这些发现有助于制定更有弹性的建筑规范和提高抗震性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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