TY - JOUR ID - 50314 TI - Efficiency of Neural Networks for Estimating the Patch Load Resistance of Plate Girders with a Focus on Uncertainties in Material and Geometrical Properties JO - Civil Engineering Infrastructures Journal JA - CEIJ LA - en SN - 2322-2093 AU - Shahabian, Farzad AU - Elachachi, Sidi Mohammed AU - Breysse, Denys AD - Associate Professor, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. AD - Professor, University of Bordeaux1, I2M-GCE, 33405 Talence, France. Y1 - 2014 PY - 2014 VL - 47 IS - 1 SP - 29 EP - 42 KW - neural networks KW - Patch Loading KW - Plate Girder KW - Sensitivity analysis KW - Variability DO - 10.7508/ceij.2014.01.003 N2 - In this paper, a sensitivity analysis of artificial neural networks (NNs) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. To evaluate the accuracy of the proposed NN model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. The empirical models are calibrated, for improving the formulae, with experimental data set which was collected from the corresponding literature. NNs models are later trained and validated through using the existing experimental data. In this process several NNs architectures are taken into account. A set of good NNs models are selected and then analyzed regarding their robustness when confronted with the test data set and regarding their ability to reproduce the effect of uncertainty on the data. A sensitivity analysis is conducted herein in order to investigate the effect of variability in material and geometrical properties of plate girders. Thereafter, several estimates measuring the efficiency and the quality of the NN model and the calibrated models are obtained and discussed. UR - https://ceij.ut.ac.ir/article_50314.html L1 - https://ceij.ut.ac.ir/article_50314_afd2c195149544066539f3c4860e2dc8.pdf ER -