چکیده مقاله
Rubble mound breakwaters with concrete and stone armor units are essential in coastal engineering, protecting against wave action and sediment transport Stability of toe in these structures is critical, supporting the armor layer and preventing scour, thus influencing the breakwater's performance and longevity This study advances toe stability prediction using an Artificial Neural Network ANN approach Experimental data from laboratory tests on breakwaters with concrete, stone, and composite toes, conducted mainly in deep water conditions, were used to train and validate the ANN model Featuring a hidden layer and an advanced optimization algorithm, the model effectively captures complex, non linear relationships The ANN's performance was compared to empirical methods using metrics such as the correlation coefficient of determination R² , root mean square error RMSE , mean absolute error MAE , mean squared error MSE , correlation coefficient CC , and normalized bias nBias Results demonstrated the ANN's superior accuracy, with higher R2 and lower RMSE, MAE, MSE, and nBias values, highlighting the significant potential of neural networks for enhancing toe stability analysis in breakwater design
کلیدواژهها
نویسندگان
Ali Jamali Rovesht
MSc Student, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
Ali Mohammadi
Professor, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
Mehdi Shafieefar
Professor, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
شیوه ارجاع
Jamali Rovesht, Ali and Mohammadi, Ali and Shafieefar, Mehdi,1404,Prediction of Toe Stability in Rubble Mound Breakwaters Using Artificial Neural Networks,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران