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    题名: Applications of Artificial Neural Networks to Pavement Prediction Modeling: A Case Study
    作者: Lee, Ying-Haur;Ker, Hsiang-Wei;Liu, Yao-Bin
    贡献者: 淡江大學土木工程學系
    关键词: Pavement deflection, prediction modeling, artificial neural networks, dimensional analysis, convergence
    日期: 2014-05
    上传时间: 2014-06-12 13:49:02 (UTC+8)
    出版者: Virginia:American Society of Civil Engineers
    摘要: 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.
    關聯: PROCEEDINGS OF THE 10TH ASIA PACIFIC TRANSPORTATION DEVELOPMENT CONFERENCE, pp.289-295
    显示于类别:[土木工程學系暨研究所] 會議論文

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