DocumentCode :
483322
Title :
Study on Parameter Distribution in Structure Reliability Analysis: Machine Learning Algorithm and Application
Author :
Wan, Yi ; Zhang, Yangu
Author_Institution :
Coll. of Comput. Sci. & Eng., Wenzhou Univ. Wenzhou, Wenzhou
fYear :
2009
fDate :
23-25 Jan. 2009
Firstpage :
833
Lastpage :
836
Abstract :
The discrimination of parameter probability distribution type is the key to structure reliability analysis. A support vector machine (SVM) intelligent recognition model of probability distribution law is presented aiming at traditional method disadvantage. The intelligent recognition model of probability distribution is constructed by SVM algorithm realization, network design and feature extraction, inward stress probability distribution type of a stem structural member is recognized by the model, recognition result is Weibull distribution, SVM has a good generalization ability and clustering ability by comparison between network recognition result and regression analysis, the experiment result shows total recognition rate achieved 98.25%, it provides a good new method for structure reliability analysis.
Keywords :
feature extraction; generalisation (artificial intelligence); reliability; statistical distributions; structural engineering computing; support vector machines; Weibull distribution; feature extraction; intelligent recognition model; inward stress probability distribution type; machine learning algorithm; parameter distribution; probability distribution law; stem structural member; structure reliability analysis; support vector machine; Algorithm design and analysis; Clustering algorithms; Feature extraction; Intelligent networks; Intelligent structures; Learning systems; Machine learning algorithms; Probability distribution; Stress; Support vector machines; SVM; intelligent recognition model; probability distribution law; structure reliability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
Conference_Location :
Moscow
Print_ISBN :
978-0-7695-3543-2
Type :
conf
DOI :
10.1109/WKDD.2009.169
Filename :
4772064
Link To Document :
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