Abstract
This paper aims at introducing a new method to determine the buckling coefficient kb of the stiffened plate girders under pure bending using deep learning, one of the most powerful algorithms in machine learning. Firstly, output data kb is generated from eigenvalue buckling analyses based on input data (various geometric dimensions of the girder). This procedure is implemented by using the Abaqus2Matlab toolbox, which allows the transfer of data between Matlab and Abaqus and vice versa. After that, 2,200 training data are used to build the model for predicting kb using deep learning. Finally, 200 test data are used to evaluate the accuracy of the model. The results obtained from this model are also compared with analogous results of previous works with a good agreement.
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Acknowledgments
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2018.327.
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Papazafeiropoulos, G., Vu, QV., Truong, VH., Luong, MC., Pham, VT. (2020). Prediction of buckling coefficient of stiffened plate girders using deep learning algorithm. In: Ha-Minh, C., Dao, D., Benboudjema, F., Derrible, S., Huynh, D., Tang, A. (eds) CIGOS 2019, Innovation for Sustainable Infrastructure. Lecture Notes in Civil Engineering, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-0802-8_183
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DOI: https://doi.org/10.1007/978-981-15-0802-8_183
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