Curtin University Homepage
  • Library
  • FAQ
    • Log in

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Methods of enhancing forecasting capacity of subgrade settlement

    Access Status
    Fulltext not available
    Authors
    Zhang, F.
    Liu, G.
    Shao, Zongping
    Chen, W.
    Han, W.
    Date
    2013
    Collection
    • Curtin Research Publications
    Type
    Journal Article
    Metadata
    Show full item record
    Abstract

    Due to the complication and randomness of dynamic change during settlement, the reliability and predictability of mathematical models are always limited in the settlement forecasting. In order to enhance the reliability and predictability of the mathematical forecasting model, using the settlement monitoring data of a high-speed railway roadbed in the loess region to establish the non-synchronization of the three exponential smoothing model, cross-certification analytic method and after-verification analytic method were used to compare and validate the fitting accuracy and forecasting accuracy on different synchronization models for getting the best reliable forecasting model; then, parallel-modification analytic method was used to modify forecasting results for enhancing forecasting capacity. The results show that the cross-certification analytic method and after-verification analytic method can provide a guarantee for the best forecasting model, and parallel-modification analytic method can not only enhance forecasting accuracy and forecasting length, but improve the comprehensive forecasting capacity as well. At the same time, the three methods can be applied to other mathematical models, because of simple principle, easy operation and strong adaptability.

    Citation
    Zhang, F. and Liu, G. and Shao, Z. and Chen, W. and Han, W. 2013. Methods of enhancing forecasting capacity of subgrade settlement. Journal of China Coal Society. 38 (SUPPL.1): pp. 88-92.
    Source Title
    Journal of China Coal Society
    URI
    http://hdl.handle.net/20.500.11937/63258
    Department
    Department of Chemical Engineering

    Related items

    Showing items related by title, author, creator and subject.

    • Modelling pile capacity and load-settlement behaviour of piles embedded in sand & mixed soils using artificial intelligence
      Alkroosh, Iyad Salim Jabor (2011)
      This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial ...
    • Applications of neural networks in market risk
      Mostafa, Fahed. (2011)
      Market risk refers to the potential loss that can be incurred as a result of movements inmarket factors. Capturing and measuring these factors are crucial in understanding andevaluating the risk exposure associated with ...
    • Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm
      Chan, Kit Yan; Dillon, Tharam; Singh, Jaipal; Chang, Elizabeth (2011)
      This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm, which aims to improve the generalization capabilities of ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorsTitlesSubjectsDocument TypesThis CollectionIssue DateAuthorsTitlesSubjectsDocument Types

    My Account

    Log in

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Connect with Curtin

    • 
    • 
    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Send FeedbackContact Us
    DSpace software copyright © 2002-2015  DuraSpace