A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments

© 2019 Computer-Aided Civil and Infrastructure Engineering 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.

A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments Academic Article