Evaluating structural response of concrete-filled steel tubular columns through machine learning

Reliability considerations of modern design codes for CFST columns

“…For geometric properties, only the uncertainties in the cross-section of columns (i.e., thickness t of steel tubes, diameter D of circular sections or width B and height H of rectangular sections) are considered in this study. The uncertainty in the column length L is ignored because it is not sensitive to the reliability index of columns [42,59,60].…”

A Framework for Intelligent Fire Detection and Evacuation System

“…Predictions of fire behaviour in two connected compartments, using ANN, were extended to predict fire behaviour in multi-compartments [8]. Because of the cost prohibitive aspect of generating large data set from experiments, CFD and zone models such as CFAST are commonly used to develop data bases of fire dynamics related parameters sensors to train ANNs [6].…”

“…Researchers relied on zone models and Computational Fluid Dynamics (CFD) to generate training data on fire dynamics [5,6]. Structurally, Machine Learning (ML), genetic algorithm and gene expression programming have been used to predict capacities of structural elements [7,8].…”

Estimating the Axial Compression Capacity of Concrete-Filled Double-Skin Tubular Columns with Metallic and Non-Metallic Composite Materials

“…Generally, the CFDST columns have two layers (outer and inner skin) of concrete-filled tubes, unlike the normal single-skin tubular columns as indicated in Figure 1 . In CFDST, the steel acts as the base for the tubular columns and has offered the construction industry better outcomes in terms of core strength (axial compression), tensile strength, ductility, load-bearing capacity [ 1 ], structural strength, yield strength, toughness [ 2 ], heavy vehicle load-capacity, elastoplastic capacity [ 3 ], heavy-wind, wearing and erosion, etc. [ 4 ], mainly in the countries such as China, USA, Japan and India [ 5 ].…”