A. Mukherjee et al., PREDICTION OF BUCKLING LOAD OF COLUMNS USING ARTIFICIAL NEURAL NETWORKS, Journal of structural engineering, 122(11), 1996, pp. 1385-1387
Citations number
10
Categorie Soggetti
Engineering, Civil","Construcion & Building Technology
A number of investigators have proposed semiempirical formulas for the
critical buckling load of slender columns. The departure from the ass
umptions of the elastic-plastic theory makes the task of incorporating
all the features of real-life columns into a single formula very diff
icult. As a result, semiempirical formulas, adopted for design specifi
cations often follow a lower bound to experimental observations to inc
lude a variety of column types. Therefore, a significant portion of th
e actual column strength remains unutilized, when such a lower bound i
s adopted in the design of axially compressed members. This technical
note reports development of a tool for the prediction of buckling load
of columns, which requires minimum assumptions using neural computing
techniques. This concept can be extended to include a variety of colu
mn types in a single model for the buckling load of columns. This conc
ept can also be further extended for reliability analysis as the netwo
rk can also predict the standard deviation in the column strength.