Title :
Foundation settlement forecasting using the new BP-Gompertz model
Author :
Liang, Haonan ; Qin, Feihu ; Wang, Jiehao ; Zhang, Tian ; Liang, Yan
Author_Institution :
Sch. of Mech. & Civil Eng., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
In view of each advantages of Gompertz and BP neural network, the Gompertz growth curve model was combined with BP neural network. The BP-Gompertz foundation forecasting model was proposed through using the capability of approximating the true value of BP neural network to optimize the curve fitting capability of Gompertz. An example shows that compared with the traditional Gompertz model, the prediction accuracy of the new BP-Gompertz model is significantly improved. The model provides a new method for the foundation settlement prediction.
Keywords :
approximation theory; backpropagation; curve fitting; foundations; neural nets; optimisation; structural engineering computing; BP neural network; BP-Gompertz Model; Gompertz growth curve model; curve fitting capability; foundation settlement forecasting; prediction accuracy; Computer languages; Measurement uncertainty; Time measurement; BP neural network; BP-Gompertz model; Gompertz growth curve model; foundation settlement;
Conference_Titel :
Information Networking and Automation (ICINA), 2010 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-8104-0
Electronic_ISBN :
978-1-4244-8106-4
DOI :
10.1109/ICINA.2010.5636420