Scholarlink

<p>This study presents an approach for output-only, ambient vibration data time series, analysis-based damage detection in a benchmark structural model. A three-dimensional finite element model based on the benchmark laboratory model is simulated to show the efficacy of the suggested approach. Also, a new damage-sensitivity feature that is based on autoregressive time series models with exogenous inputs (ARX) is introduced by using output acceleration responses from sensors under the influence of ambient loads provided in the modeling. Minor localized damage close to supports, and more extensive damage that might occur throughout a bridge’s service lifetime are simulated with the finite element model to show the robustness and stability of the suggested damage feature. Next, numerous damage features proposed by other researchers are compared with the feature introduced in this study, utilizing the generated data and the damage scenarios that were created. The findings demonstrate that the proposed damage feature can reliably and accurately identify and locate minor damage close to supports (regarded as a challenge in identification studies) and offer a measure of the level of damage. In addition, the damage feature can accurately locate large-scale damage without producing false positive or negative results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving damage detection methods in structural health monitoring by introducing a hybrid innovative damage-sensitivity feature using time series analysis

  • 期刊论文

  • 发表日期：

    2026年6月18日

查看全文
  • Seyed Arman Hashemi,
  • Behnam Adhami,
  • Ali Golsoorat Pahlaviani

摘要

This study presents an approach for output-only, ambient vibration data time series, analysis-based damage detection in a benchmark structural model. A three-dimensional finite element model based on the benchmark laboratory model is simulated to show the efficacy of the suggested approach. Also, a new damage-sensitivity feature that is based on autoregressive time series models with exogenous inputs (ARX) is introduced by using output acceleration responses from sensors under the influence of ambient loads provided in the modeling. Minor localized damage close to supports, and more extensive damage that might occur throughout a bridge’s service lifetime are simulated with the finite element model to show the robustness and stability of the suggested damage feature. Next, numerous damage features proposed by other researchers are compared with the feature introduced in this study, utilizing the generated data and the damage scenarios that were created. The findings demonstrate that the proposed damage feature can reliably and accurately identify and locate minor damage close to supports (regarded as a challenge in identification studies) and offer a measure of the level of damage. In addition, the damage feature can accurately locate large-scale damage without producing false positive or negative results.