JOURNAL ARTICLE

Structural Vibration Identification in Ancient Buildings Based on Multi-Feature and Multi-Sensor.

  • Published In: International Journal of Structural Stability & Dynamics, 2025, v. 25, n. 9. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yang, Yulong; Qian Chen; Zhang, Yumiao; Pan, Jiafu; Wang, Jintao; Tan, Yang; Zhou, Jiawei 3 of 3

Abstract

Ancient buildings have strict standards for vibration control. Effectively identifying vibration types and controlling construction vibrations during construction activities is advantageous to the structural safety of ancient buildings. This study is based on an analysis of vibration data from the top, foundation, and bedrock of the White Pagoda in Hangzhou, Zhejiang province, which is an ancient building. Considering the surrounding construction and wind environment, this study focuses on analyzing the data features of tower vibrations under three types of structural vibration. We propose a support vector machine (SVM) vibration identification method that incorporates multi-feature parameters and multi-sensor signal correlation. This method effectively identifies the source of structural vibration by distinguishing between typical wind-induced vibrations, typical construction vibrations, and typical mixed vibrations. The application of this method could guide construction activities and mitigate the safety impacts of construction and mixed vibrations on historical building structures. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Structural Stability & Dynamics. 2025/05, Vol. 25, Issue 9, p1
  • Document Type:Article
  • Subject Area:Anthropology
  • Publication Date:2025
  • ISSN:0219-4554
  • DOI:10.1142/S0219455425500944
  • Accession Number:184678554
  • Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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