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

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.