Ayla TEKİNAyşe ÖNDÜRÜCÜHüseyin Fırat KAYIRAN2024-07-242024-07-2420231307-9085http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/26465The primary objective of this work is to analyze the critical buckling load of the hybrid composite plate using test data on the effects of different environmental conditions. The artificial neural network (ANN) method was used for analysis. The MATLAB-based program was used to develop the ANN. The buckling data that emerged after being tested was trained on the ANN model. Inputs for ANN modeling; the holding times of the materials, ambient temperatures, environmental conditions, and material orientation angles, and the output parameter is the critical buckling load. In modeling, 80% of forty-two experimental data were taken for training and 20% for validation. The data obtained after training and testing the materials in Artificial Neural Networks were investigated by performing statistical analysis, which is frequently preferred in ANN models, it was seen that the resulting modeling was successfully applied and the results were very close to the real test results. While the average error rate was 1.82% in the training phase, it was 3.41% in the testing phase.engAraştırma Makalesi10.18185/erzifbed.1253159