Title :
Soft ground subsidence prediction of highway based on the BP neural network
Author :
Xu Lei ; Zhang Zhen ; Ye Sheng ; Lu Guilin
Author_Institution :
Sch. of Mech. & Civil Eng., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
We take BP artificial neural network and use soft ground foundation subsidence data of Ningsuxu highway to build model, and do the forecasts of soft ground subsidence final settlement, then comparing the predict results with curve-fitting hyperbola method, the curve method, three-point method forecast results. It turns out that neural network can avoid the human factors of interference from traditional methods, gaining high precision.
Keywords :
backpropagation; civil engineering computing; curve fitting; foundations; neural nets; roads; BP artificial neural network; Ningsuxu highway; curve fitting hyperbola method; soft ground foundation subsidence data; soft ground subsidence final settlement; soft ground subsidence prediction; three-point method; Data models; Educational institutions; Predictive models; Road transportation; Soil; Testing; Training; artificial neural network; soft ground foundation; subsidence prediction;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-8622-9
DOI :
10.1109/ITAIC.2011.6030243