Open AccessA prediction model for local scour depth based on BP and GA-BP neural network
Author(s) -
Haiyang Dong,
Chong Lin,
Hanyu Zhou,
Zongyu Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/525/1/012005
Subject(s) - artificial neural network , bridge (graph theory) , bay , field (mathematics) , pier , data set , set (abstract data type) , engineering , computer science , structural engineering , artificial intelligence , mathematics , civil engineering , medicine , pure mathematics , programming language
A prediction model for the local scouring depth at bridge piers is built using BP and GA-BP neural network method. Measured data is collected from laboratory tests and field observation to work as train set of the predicting model which is verified by the measured data of Hangzhou Bay Bridge. The result shows that the predicted values of the local scour depth of the piers obtained by the two models are in good agreement with the measured values, which is more suitable than the existing general formula. What’s more, GA-BP model performs better than BP model.