Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques.

ABSTRACT: An innovative form of concrete-encased steel (CES) composite columns incorporating engineered cementitious composites (ECC) confinement (ECC-CES) has recently been introduced, displaying superior performance in failure behavior, ductility, and toughness compared to traditional CES columns. This study presents an innovative approach to predicting the axial capacity of ECC-CES columns using adaptive sampling and machine learning (ML) techniques. This study initially introduces a finite element (FE) modeling for ECC-CES columns, integrating material and geometric nonlinearities to accurately capture the inelastic behavior of ECC and steel through appropriate constitutive material laws. The FE model was validated against experimental data and demonstrated strong predictive accuracy. An adaptiv

SUBMITTER: Megahed K 

PROVIDER: S-EPMC11794466 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature