page loader
Buckling resistance of axially loaded square concrete-filled double steel tubular columns
Authors: Junchang Ci, Mizan Ahmed, Viet-Linh Tran, Hong Jia, Shicai Chen, Tan N. Nguyen
238    0
Steel and Composite Structures
: 43     : 689-706
Publishing year: 6/2022
Thin-walled square concrete-filled double steel tubular (CFDST) columns composed of the inner circular tube filled with concrete can be used to carry the large axial loads or strengthen existing CFST columns in composite constructions. This paper reports an experimental program carried out on short square CFDST columns loaded concentrically. The influences of important column parameters on the post-buckling performance of such columns are investigated. Test results exhibit that the inner circular tube significantly improves the ultimate loads and the ductility of such columns compared to conventional concrete-filled steel tubular (CFST) and double-skin CFST (DCFST) columns with an inner void. A mathematical model developed is used to simulate the ultimate strengths and load-strain curves of such columns loaded axially. Furthermore, the ultimate strengths of such columns are predicted using existing codified design models for conventional CFST columns as well as the formulas proposed by previous researchers and compared against a large database comprising 500 CFDST columns. Lastly, an accurate artificial neural network model is developed for the practical applications of such columns under axial loading.
artificial neural network; axial loading; CFDST columns; post-buckling; short columns
Ensemble machine learning-based models for estimating the transfer length of strands in PSC beamsInnovative formulas for reinforcing bar bonding failure stress of tension lap splice using ANN and TLBOPrediction of the ultimate axial load of circular concrete-filled stainless steel tubular columns using machine learning approachesPatch loading resistance prediction of steel plate girders using a deep artificial neural network and an interior-point algorithmInvestigating the Behavior of Steel Flush Endplate Connections at Elevated Temperatures Using FEM and ANNNovel hybrid WOA-GBM model for patch loading resistance prediction of longitudinally stiffened steel plate girdersA new framework for damage detection of steel frames using burg autoregressive and stacked autoencoder-based deep neural networkRevealing the nonlinear behavior of steel flush endplate connections using ANN-based hybrid modelsRapid prediction of the ultimate moment of flush endplate connections at elevated temperatures through an artificial neural networkComputational analysis of axially loaded thin-walled rectangular concrete-filled stainless steel tubular short columns incorporating local buckling effectsAxial compressive behavior of circular concrete-filled double steel tubular short columnsEvaluation of Seismic Site Amplification Using 1D Site Response Analyses at Ba Dinh Square Area, VietnamMachine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC ColumnsApplication of ANN in predicting ACC of SCFST columnA new empirical formula for prediction of the axial compression capacity of CCFT columnsMoment-rotation-temperature model of semi-rigid cruciform flush endplate connection in firePractical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete