CSDL Bài trích Báo - Tạp chí
Artificial neural network with adaptive moment estimation training approaches for prediction of punching shear capacity of steel
Tác giả: Hoang Nhat DucTóm tắt:
Estimating punching shear capacity (PSC) of steel fibre reinforced concrete slabs (SFRCS) is a crucial task in structural design. This study investigates the performances of artificial neural networks trained by the adaptive moment estimation (Adam) method in dealing with the task of interest. To alleviate overfitting problem, decoupled weight decay (AdamW) and L2regularization (AdamL2) are used. A dataset including 140 samples has been used to train and verify the machine learning approaches. Interms of root mean square error (RMSE), Experimental results including 20 independent runs point out that predictive performances of the AdamW (RMSE = 30.60) and AdamL2(RMSE = 31.74) are better than that of the Adam (RMSE = 36.62). However, performance of a combination of AdamW and AdamL2(RMSE = 32.31) is worse than those obtained from the individual AdamW and AdamL2.