DocumentCode :
3211841
Title :
Road pavement performance evaluation model based on hybrid genetic algorithm neural network
Author :
Qian, Wei-Dong
Author_Institution :
Sch. of Automobile & Traffic Eng., Jiangsu Univ., Zhenjiang, China
Volume :
1
fYear :
2010
fDate :
13-14 Sept. 2010
Firstpage :
209
Lastpage :
212
Abstract :
Road pavement performance evaluation and prediction is two most important parts of pavement management system. In order to scientifically and accurately predict the future road pavement situation,evaluation indexes and main influence factors of pavement performance were analyzed. Then functional performance,structure performance, safety performance, and comfortability performance was selected as the evolution index and three factors were taken as parameters, including temperature, annual precipitation,annual average daily traffic. The two prediction models of BP neural network and hybrid algorithms based on neural network and genetic algorithm were built respectively. Forecasting result shows that neural network model based on genetic algorithms has higher prediction accuracy and more network generalization than those of BP neural network.
Keywords :
backpropagation; generalisation (artificial intelligence); genetic algorithms; neural nets; performance evaluation; performance index; road traffic; roads; traffic engineering computing; BP neural network; annual average daily traffic; annual precipitation; comfortability performance; evaluation indexes; forecasting; functional performance; hybrid genetic algorithm neural network; network generalization; pavement management system; prediction accuracy; road pavement performance evaluation; safety performance; structure performance; Artificial neural networks; Gallium; Indexes; Prediction algorithms; Predictive models; Roads; evalution models; genetic algorithm; neural network; pavement performance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7705-0
Type :
conf
DOI :
10.1109/CINC.2010.5643855
Filename :
5643855
Link To Document :
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