Modeling of Seismic Energy Dissipation of Rocking Foundations Using Nonparametric Machine Learning Algorithms
Modeling of Seismic Energy Dissipation of Rocking Foundations Using Nonparametric Machine Learning Algorithms
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使用非参数机器学习算法对摇摆基础的地震能量耗散进行建模
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sivapalan Gajan
中科院分区：
文献类型：
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作者：
Sivapalan Gajan
本研究的目的是使用多种机器学习 (ML) 算法和来自摇摆基础数据库的实验数据，开发数据驱动的预测模型，用于地震加载期间摇摆浅基础的地震能量耗散。考虑三种非线性、非参数 ML 算法：k 最近邻回归 (KNN)、支持向量回归 (SVR) 和决策树回归 (DTR)。机器学习算法的输入特征包括摇摆系统的临界接触面积比、长细比和摇摆系数，以及地震运动的峰值地面加速度和阿里亚斯强度。随机分割的一对训练和测试数据集用于模型的初始评估和超参数调整。重复 k 折交叉验证技术用于使用平均绝对百分比误差进一步评估 ML 模型在偏差和方差方面的性能。结果发现，所有三种 ML 模型的性能都优于多元线性回归模型，并且 KNN 和 SVR 模型始终优于 DTR 模型。平均而言，KNN 模型的准确率比 SVR 模型高约 16%，而 SVR 模型的方差比 KNN 模型小约 27%，这使得它们都是对所考虑问题进行建模的绝佳候选者。
The objective of this study is to develop data-driven predictive models for seismic energy dissipation of rocking shallow foundations during earthquake loading using multiple machine learning (ML) algorithms and experimental data from a rocking foundations database. Three nonlinear, nonparametric ML algorithms are considered: k-nearest neighbors regression (KNN), support vector regression (SVR) and decision tree regression (DTR). The input features to ML algorithms include critical contact area ratio, slenderness ratio and rocking coefficient of rocking system, and peak ground acceleration and Arias intensity of earthquake motion. A randomly split pair of training and testing datasets is used for initial evaluation of the models and hyperparameter tuning. Repeated k-fold cross validation technique is used to further evaluate the performance of ML models in terms of bias and variance using mean absolute percentage error. It is found that all three ML models perform better than multivariate linear regression model, and that both KNN and SVR models consistently outperform DTR model. On average, the accuracy of KNN model is about 16% higher than that of SVR model, while the variance of SVR model is about 27% smaller than that of KNN model, making them both excellent candidates for modeling the problem considered.