A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments
A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments
复制标题
用于地震脆弱性评估中漂移能力广义预测的局部加权机器学习模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Paal
中科院分区：
文献类型：
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作者：
Huan Luo;S. Paal
钢筋混凝土柱的位移承载力是衡量框架结构地震易损性的重要指标；然而，准确地预测这个值是具有挑战性的，因为非线性行为会因列类型的不同而有很大的不同。本文提出了一种新的局部机器学习（ML）模型，称为局部加权最小二乘支持向量机回归（LWLS‐SVMR），它集成了LS‐SVMR和局部加权训练标准，以增强和推广RC柱漂移能力的预测，无论类型如何。一个包含160个圆形RC柱的数据库，涵盖弯曲、剪切和弯曲-剪切临界试件，用于训练和测试拟议的LWLS - SVMR。通过与现有的流行的全局和局部学习方法以及传统的经验方程进行比较，验证了所提出的LWLS‐SVMR，结果表明，所提出的LWLS‐SVMR优于所有其他方法，因此，是一种有前途的人工智能技术，可以增强对RC挠曲，剪切和挠曲-剪切临界柱的漂移能力预测。LWLS - SVMR显示出的能力可能使它成为一种以广谱方式预测复杂非线性行为的可行方法。
Drift capacity of reinforced concrete (RC) columns is an important indicator to quantify the seismic vulnerability of RC frame buildings; however, it is challenging to accurately predict this value as the nonlinear behavior can vary greatly by column type. This article proposes a novel, local machine learning (ML) model, called locally weighted least squares support vector machines for regression (LWLS‐SVMR), which integrates LS‐SVMR and locally weighted training criteria to enhance and generalize the prediction of the drift capacity of RC columns, regardless of the type. A database of 160 circular RC columns covering flexure‐, shear‐, and flexure–shear‐critical specimens was developed to train and test the proposed LWLS‐SVMR. The proposed LWLS‐SVMR was validated by comparison with popular existing global and local learning approaches as well as a traditional empirical equation, and the results demonstrated that the proposed LWLS‐SVMR is superior to all other approaches and thus, is a promising artificial intelligence technique for enhancing the prediction of drift capacity, universally across RC flexure‐, shear‐, and flexure–shear‐critical columns. The LWLS‐SVMR exhibits capabilities which may yield it a feasible approach to predict complex, nonlinear behavior in a broad‐spectrum manner.