PREDICTING THE AXIAL LOAD CAPACITY OF STEEL COLUMNS IN FIRE USING ARTIFICIAL NEURAL NETWORKS
الباحث الأول:
Dr. Ahmed Ajel Ali
الباحثين الآخرين:
Dr. Hayder Mahdi , Dr. Luay M. Abass
المجلة:
JournalAl-Qadisiya Journal for Engineering and Scince
Iraqi Academic Scientific
تاريخ النشر:
None
مختصر البحث:
The ability to reasonably predict the response of steel structures under fire effects is of great
importance in structural fire safety design. This paper presents neural networks prediction of axial
load capacity for steel columns in fire. An algo…
The ability to reasonably predict the response of steel structures under fire effects is of great
importance in structural fire safety design. This paper presents neural networks prediction of axial
load capacity for steel columns in fire. An algorithm of back propagation neural network with the
log-sigmoid activation function is adopted because of its precision and results enhancement of
foretelling. The legitimacy of the technique is tried by contrasting and distributed test information
on steel columns at surrounding and elevated heat. The examinations demonstrate such technique
gives great correlation with test result. Parametric studies have been done to evaluate the impacts of
cross sectional shape, slenderness ratios and eccentricity of loading on the carrying capacity of steel
columns under fire. The slim sections of steel columns with slenderness ratio domain (100-140)
react distinctively by showing an abundantly decreased rate of loss in strength within the
temperature domain (20°C - 300°C). This domain diminishes further with expanding slenderness
ratios, and for middle columns with slenderness ratio domain (40-80), is like that of stumpy
columns however at decreased buckling stress. Be that as it may, in this scope of (L/R) ratios the
lessening in stress with expanding temperature is regular and demonstrates no sudden drop, because
of the collaboration amongst buckling and yielding. On other hand, the eccentricity of loading on
the carrying capacity of steel columns under fire shows that the slender column, (slenderness ratio)
greater than 120, the column demonstrates a diminishing impact of used eccentricity of loadings
with expanding slenderness ratios. This might be as a consequence of more impelled thermal
bowing that is straightforwardly relative to the column length. And the load-eccentricity
characteristics of the intermediate column, (slenderness ratio) domain (20 – 60), are schemed at
increasing temperature gradient. It is fascinating to observe that the eccentricity of the limit of
maximum column load capacity slightly effected with temperature gradient. It is trusted that the
important data gave in this work will be helpful in giving a superior comprehension on the genuine
behavior of steel sections in fire and a great step in improving the method of design.
