DocumentCode :
2567077
Title :
Model optimization of load - bearing capacity of macadam pile composite foundation based on genetic algorithm
Author :
Liu, Meixia ; Qie, Zhihong ; Wu, Xinmiao ; Dong, Wenwen ; Zheng, Haili
Author_Institution :
Dept. of Water Conservancy Eng., Agric. Univ. of Hebei, Baoding
fYear :
2008
fDate :
2-4 July 2008
Firstpage :
3903
Lastpage :
3907
Abstract :
In this paper, model optimization method of load - bearing capacity of composite foundation based on genetic algorithm is put forward. In this method, the chromosome bit string, which is looked as the generator, is used to complete random combination of influence factor and therefore need not be decoded. Considering both the fitting accuracy to modeling data and prediction accuracy to other data, model samples are divided into training samples and checkout samples, and in order to the balance between fitting accuracy and prediction accuracy, the multiple regression function is built and optimized. Through analyzing the static load experiment data of fifteen vibrating macadam pile, the genetic regression model of bearing capacity of macadam pile composite foundation is established, the prediction result shows, that the method has good fitting accuracy and predict accuracy and the stability of the model is satisfactory.
Keywords :
foundations; mechanical strength; optimisation; regression analysis; chromosome bit string; genetic algorithm; genetic regression model; load-bearing capacity; macadam pile composite foundation; model optimization; model stability; multiple regression function; static load experiment data; vibrating macadam pile; Accuracy; Agricultural engineering; Algorithm design and analysis; Electronic mail; Error correction; Genetic algorithms; Hydroelectric power generation; Load modeling; Predictive models; Water conservation; genetic algorithm; load - bearing capacity of composite foundation; multiple regression; optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
Type :
conf
DOI :
10.1109/CCDC.2008.4598063
Filename :
4598063
Link To Document :
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