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Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Auteur(s):







Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-18
DOI: 10.1155/2019/7108262
Abstrait:

It is currently known that using stress wave and drilling resistance to detect the internal damage in the ancient timber structure is not a highly precise process. To improve the detection precision of this process, a simulation test was used to detect the internal damage of poplar and elm in ancient buildings. In this empirical study, we compared the detection precision of these two detection methods. Based on the idea of variable weight, we introduced three combined forecasting models based on the IOWA operator, IOWGA operator, and IOWHA operator to predict the internal damage in the ancient timber structure. The results show that the combined forecasting model based on the IOWA operator is more effective in predicting compared to a single detection method and other combined forecasting models. To be more specific, the results show that the detection precision of the combined model is increased by 25.8% and 4.7%, respectively, compared to the precision of the stress wave and drilling resistance tests. The error indicators of the combined forecasting model based on the IOWA operator are better than those of the other combined forecasting models. In addition, the analysis results based upon cross-validation theory show the combined forecasting model based on the IOWA operator has the best applicability, which provides a new practical method for evaluating internal damage of timber components in ancient buildings.

Copyright: © Ziyi Wang et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10364994
  • Publié(e) le:
    21.08.2019
  • Modifié(e) le:
    02.06.2021
 
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