Accounting for the additional stiffness of the steel, the nominal loading capacity Pn for the column in terms of the maximum compressive stress of the concrete fc', the yield stress of the steel fy, the gross cross section area of the column Ag, and the total cross section area of the steel rebar Ast where the first term represents the load carried by the concrete and the second term represents the load carried by the steel.
The spiral acts to provide support in the transverse direction and prevent the column from barreling.
[2] The ACI Building Code Requirements put the following restrictions on amount of spiral reinforcement.
The spacing of the ties is limited in that they must be close enough to prevent barreling failure between them, and far enough apart that they do not interfere with the setting of the concrete.
[4] Columns qualify as being slender when their cross sectional area is very small in proportion to their length.
To see such models and simulations of columns subjected to the cyclic and monotonic loading, refer to the following links:,[5][6][7] Machine learning (ML) is a subfield of artificial intelligence (AI) and an advanced form of data analysis and computation that employs the high elaboration speed and pattern recognition techniques of computers for knowledge output from data.
In supervised learning, the desired output is known by the trainer, where the trainer is the human being that can ascribe physical meaning to the data and characterize it by adding a tag or correcting system errors.
Through this process, the machine develops a predictive model for the connection of this input to a certain output.
Their behavior throughout the loading range is controlled by competing mechanisms of resistance such as flexure, shear, buckling of longitudinal bars when they are subjected to compressive loads and, in the case of lap splices, the lap splice mechanism of the development of reinforcing bars.
Very often, a combination of such mechanisms characterizes the macroscopic behavior of the column, especially in cases of cyclic load reversals.
Various predictive models have been developed in the past to determine both the strength as well as the deformation capacity of the columns, with the uncertainty being at least one order of magnitude greater in terms of deformation capacity rather than strength, as evidenced by comparisons with test results.
Laboratory tests of reinforced concrete (RC) structures have provided one source of data that enables ML methods to identify their failure modes, strength, capacities and constitutive behaviors [8]