Abstract: In this study, an experiment was executed related to
the strength of wooden materials which have been commonly used
both in the past and present against pressure and whether fire
retardant materials used against fire have any effects or not. Totally
81 samples which included 3 different wood species, 3 different
sizes, 2 different fire retardants and 2 unprocessed samples were
prepared. Compressive pressure tests were applied to the prepared
samples, their variance analyses were executed in accordance with
the obtained results and it was aimed to determine the most
convenient wooden materials and fire-retardant coating material. It
was also determined that the species of wood and the species of
coating caused the decrease and/or increase in the resistance against
pressure.
Abstract: Artificial intelligence applications are commonly used
in industry in many fields in parallel with the developments in the
computer technology. In this study, a fire room was prepared for the
resistance of wooden construction elements and with the mechanism
here, the experiments of polished materials were carried out. By
utilizing from the experimental data, an artificial neural network
(ANN) was modelled in order to evaluate the final cross sections of
the wooden samples remaining from the fire. In modelling,
experimental data obtained from the fire room were used. In the
developed system, the first weight of samples (ws-gr), preliminary
cross-section (pcs-mm2), fire time (ft-minute), and fire temperature
(t-oC) as input parameters and final cross-section (fcs-mm2) as output
parameter were taken. When the results obtained from ANN and
experimental data are compared after making statistical analyses, the
data of two groups are determined to be coherent and seen to have no
meaning difference between them. As a result, it is seen that ANN
can be safely used in determining cross sections of wooden materials
after fire and it prevents many disadvantages.