Mechanistic-data-driven modeling of multi-material composite columns: Toward intelligent lightweight design

This study examines the axial compressive performance of multi-material composite columns consisting of concrete-filled steel tubes with embedded CFRP-confined timber cores. A data-driven framework integrating theoretical model, finite element simulation and machine learning prediction is established to address the limited accuracy and scalability of conventional dual-material designs. An analytical bearing-capacity model is derived by accounting for steel confinement, CFRP hoop restraint, and timber orthotropy, of which results match FE results well with 5% deviations. Parametric investigations show that increasing steel yield strength and tube thickness would enhance the capacity of the composite columns,…

• NN
  National Natural Science Foundation of China

  Awards: 52278165, 51908085
• ED
  Education Department of Shaanxi Province

  Award: 24JP201
• NS
  Natural Science Basic Research Program of Shaanxi Province

  Award: 2024JC-YBMS-439