Applications of Artificial Neural Networks to Pavement Prediction Modeling: A Case Study

機構典藏 > College of Engineering > Graduate Institute & Department of Civil Engineering > Proceeding > Item 987654321/98194

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Title:	Applications of Artificial Neural Networks to Pavement Prediction Modeling: A Case Study
Authors:	Lee, Ying-Haur;Ker, Hsiang-Wei;Liu, Yao-Bin
Contributors:	淡江大學土木工程學系
Keywords:	Pavement deflection, prediction modeling, artificial neural networks, dimensional analysis, convergence
Date:	2014-05
Issue Date:	2014-06-12 13:49:02 (UTC+8)
Publisher:	Virginia：American Society of Civil Engineers
Abstract:	Artificial neural networks (ANN) have been used in many pavement prediction modeling analyses. However, the convergence characteristics and model selection guidelines are rarely studied duc to the requirement of extensive network training time. Thus, the techniques and applications of back propagation neural networks were briefly reviewed. Three ANN models were developed using deflection databases generated by factorial BISAR runs. A study of the convergence characteristics indicated that the resulting ANN model using all dominating dimensionless parameters was proved to have higher accuracy and require less network training time and data than the other counterpart using purely input parameters. Increasing the complexity of ANN models does not necessarily improve the modeling statistics. With the incorporation of subject-related engineering and statistical knowledge into the modeling process, reasonably good predictions may be achieved with more convincing generalization and explanation yet requiring minimal amount oftime and effort.
Relation:	PROCEEDINGS OF THE 10TH ASIA PACIFIC TRANSPORTATION DEVELOPMENT CONFERENCE, pp.289-295
Appears in Collections:	[Graduate Institute & Department of Civil Engineering] Proceeding

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icpt27-2014-ann.pdf	應用類神經網路於鋪面預測模式構建之個案研究	4481Kb	Adobe PDF	219	View/Open

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