Data-driven nonmodel seismic assessment of eccentrically braced frames with soil-structure interaction

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Mahshad Jamdar , Kiarash M. Dolatshahi , Omid Yazdanpanah
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Abstract

This study presents a nonmodel-based machine learning framework for estimating engineering demand parameters (EDPs) of eccentrically braced frames with soil-structure interaction effects. The objective is to estimate residual and peak story drift ratio, peak floor acceleration, and develop fragility curves using traditional regression equations and advanced machine-learning techniques. Correction coefficients are developed to improve prediction accuracy by accounting for soil-structure interaction. A comprehensive database, including incremental dynamic analysis results of 4- and 8-story frames, is developed, consisting of 109,841 data points. The database includes fixed-base models and models with various soil-structure interaction values, subjected to 44 far-field ground motions. Four scenarios are introduced considering various input variables to compare the impact of soil-structure interaction. Findings reveal the effects of soil-structure interaction features on the performance of machine learning algorithms, increasing by up to 17.61% of the coefficient of determination. Utilizing the predicted story drift ratio, two types of fragility curves indicate more precise predictions, emphasizing the impact of soil-structure interaction effects at lower damage levels. A graphical user interface has been developed to predict fragility curves based on various inputs to promote the practical use of machine learning in engineering. Two new 4-story frames are used as case studies, subjected to unseen ground motions to assess the application of trained machine learning algorithms. Prediction errors in input-output scenarios considering soil-structure interaction range from 3% to 18% for new frames. The proposed approach for predicting EDPs is further acknowledged by evaluating a real instrumented five-story steel frame office building.
土-结构相互作用偏心支撑框架的数据驱动非模型抗震评估
本研究提出了一种基于非模型的机器学习框架,用于估算具有土-结构相互作用效应的偏心支撑框架的工程需求参数(EDP)。目的是使用传统回归方程和先进的机器学习技术估算残余漂移率和峰值层漂移率、峰值楼层加速度并绘制脆性曲线。通过考虑土壤与结构的相互作用,开发了校正系数以提高预测精度。开发了一个综合数据库,其中包括 4 层和 8 层框架的增量动态分析结果,由 109,841 个数据点组成。数据库包括固定基座模型和具有不同土-结构相互作用值的模型,受 44 种远场地震动影响。考虑到不同的输入变量,引入了四种情景,以比较土壤-结构相互作用的影响。研究结果表明,土-结构相互作用特征对机器学习算法的性能有影响,决定系数最多可增加 17.61%。利用预测的楼层漂移率,两种类型的脆性曲线显示了更精确的预测结果,强调了在较低破坏水平下土层与结构相互作用效应的影响。为了促进机器学习在工程中的实际应用,我们开发了一个图形用户界面,用于根据各种输入预测脆性曲线。使用两个新的 4 层框架作为案例研究,通过未见的地面运动来评估训练有素的机器学习算法的应用情况。在考虑土壤与结构相互作用的输入-输出情景中,新框架的预测误差在 3% 到 18% 之间。通过对一栋真实的五层钢结构办公楼进行评估,我们进一步确认了所提出的 EDP 预测方法。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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