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Steel and Composite Structures Volume 45, Number 2, October25 2022 , pages 159-173 DOI: https://doi.org/10.12989/scs.2022.45.2.159 |
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Patch loading resistance prediction of steel plate girders using a deep artificial neural network and an interior-point algorith |
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Sy Hung Mai, Viet-Linh Tran, Duy-Duan Nguyen, Viet Tiep Nguyen and Duc-Kien Thai
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| Abstract | ||
| This paper proposes a hybrid machine-learning model, which is called DANN-IP, that combines a deep artificial neural network (DANN) and an interior-point (IP) algorithm in order to improve the prediction capacity on the patch loading resistance of steel plate girders. For this purpose, 394 steel plate girders that were subjected to patch loading were tested in order to construct the DANN-IP model. Firstly, several DANN models were developed in order to establish the relationship between the patch loading resistance and the web panel length, the web height, the web thickness, the flange width, the flange thickness, the applied load length, the web yield strength, and the flange yield strength of steel plate girders. Accordingly, the best DANN model was chosen based on three performance indices, which included the Rˆ2, RMSE, and a20-index. The IP algorithm was then adopted to optimize the weights and biases of the DANN model in order to establish the hybrid DANN-IP model. The results obtained from the proposed DANN-IP model were compared with of the results from the DANN model and the existing empirical formulas. The comparison showed that the proposed DANN-IP model achieved the best accuracy with an Rˆ2 of 0.996, an RMSE of 23.260 kN, and an a20-index of 0.891. Finally, a Graphical User Interface (GUI) tool was developed in order to effectively use the proposed DANN-IP model for practical applications. | ||
| Key Words | ||
| artificial neural network; interior-point algorithm; machine learning; patch loading resistance; steel plate girder | ||
| Address | ||
| Sy Hung Mai and Viet Tiep Nguyen:Department of Hydraulic Engineering and Construction, Hanoi University of Civil Engineering (HUCE), Hanoi, Vietnam Viet-Linh Tran and Duy-Duan Nguyen:Department of Civil Engineering, Vinh University, Vinh 461010, Vietna | ||