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

abstract

  • © 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.

altmetric score

  • 0.75

author list (cited authors)

  • Luo, H., & Paal, S. G.

citation count

  • 11

publication date

  • November 2019

publisher