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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98194

    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.
    Appears in Collections:[土木工程學系暨研究所] 會議論文

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