Dynamic Cluster Zoning of Arch Dam Deformation Considering Changing Working Conditions

IF 4.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Structural Control & Health Monitoring Pub Date : 2024-11-11 DOI:10.1155/2024/8813251

Xudong Chen, Hongdi Guo, Shaowei Hu, Chongshi Gu, Na Lu, Jinjun Guo, Xing Liu

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Abstract

Arch dam deformation has regional characteristics, and clustering is a common method of regional classification for arch dams. Traditional methods ignore the impact of dynamic changes in temperature and water level. Besides, the noise of deformation data is detrimental to mining potential information. The objective is to devise a dynamic cluster zoning method for arch dams, which considers the changing working conditions under the coupling of water level and temperature in this study. First, the deformation periods are classified by K-means clustering, and the arch dam deformation series are denoised using a sparrow search algorithm-optimized variational mode decomposition combined with wavelet threshold (SSA–VMD–WT) denoising method. The arch dam measuring points for different periods are then clustered. The engineering case study demonstrates that the SSA–VMD–WT denoising method improves the reliability of deformation data. The dynamic cluster zoning method reasonably describes the deformation regularity of the arch dam under different working conditions.

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考虑工作条件变化的拱坝变形动态群组分区

拱坝变形具有区域特征，聚类是拱坝区域划分的常用方法。传统方法忽略了温度和水位动态变化的影响。此外，变形数据的噪声不利于挖掘潜在信息。本研究的目标是设计一种拱坝动态群组分区方法，该方法考虑了水位和温度耦合作用下的工况变化。首先，通过 K-means 聚类对变形期进行分类，并使用麻雀搜索算法优化的变模分解结合小波阈值（SSA-VMD-WT）去噪方法对拱坝变形序列进行去噪。然后对不同时期的拱坝测量点进行聚类。工程案例研究表明，SSA-VMD-WT 去噪方法提高了变形数据的可靠性。动态聚类分区法合理地描述了拱坝在不同工况下的变形规律性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Structural Control & Health Monitoring 工程技术-工程：土木

CiteScore

9.50

自引率

13.00%

发文量

234

审稿时长

8 months

期刊介绍： The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.