桥梁状况评估的线性和非线性时间序列方法:文献综述

IF 2.1 4区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Igor Ribeiro, Andreia Meixedo, Diogo Ribeiro, Túlio Nogueira Bittencourt
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引用次数: 0

摘要

铁路桥梁是任何运输系统的重要组成部分,通常会受到多种环境和操作行为的影响,从而造成损坏。此外,铁路桥梁不易更换,其故障可能造成灾难性后果。考虑到桥梁的预期使用寿命,必须保证其足够的适用性和安全性。在这种情况下,结构健康监测(SHM)应运而生,它可以在损坏变得严重之前及早识别。损坏识别通常是通过比较从监测数据中获得的损坏响应和未损坏响应来实现的。在从响应中提取的多个特征中,时间序列模型表现出更好的性能和早期损伤检测能力,也可应用于使用无监督机器学习框架的在线损伤检测策略中。本文综述了用于损伤检测的先进时间序列方法。首先介绍了 SHM 中常用的几种时间序列模型,如自回归模型 (AR)、递归神经网络 (RNN)、门控递归单元 (GRU) 和长短期记忆 (LSTM)。随后,还详细介绍了通常应用这些模型的框架,包括最新的升级和最相关的结果。最后,结论总结并阐明了时间序列模型的当前前景和研究空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear and nonlinear time-series methodologies for bridge condition assessment: A literature review
Railway bridges are essential components of any transportation system and are typically subjected to several environmental and operational actions that can cause damage. Furthermore, they are not easily replaced, and their failure can have catastrophic consequences. Considering the expected lifespan of bridges, it is essential to guarantee their adequate serviceability and safety. In this scenario, emerges the Structural Health Monitoring (SHM), which allows the early identification of damage before it becomes critical. Damage identification is usually performed by the comparison between the damaged and undamaged responses obtained from monitoring data. Among the several features extracted from the responses, the time-series models exhibit a better performance, capability of early damage detection, and may also be applied within online damage detection strategies using unsupervised machine learning frameworks. In this paper, a review of advanced time-series methodologies for damage detection is presented. Initially, several time-series models often used in SHM are described, such as Autoregressive Models (AR), Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Later, the framework where these models are usually applied is also detailed, including the latest upgrades and most relevant results. Finally, the conclusions summarize and elucidate the current perspectives and research gaps on the time-series models.
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来源期刊
Advances in Structural Engineering
Advances in Structural Engineering 工程技术-工程:土木
CiteScore
5.00
自引率
11.50%
发文量
230
审稿时长
2.3 months
期刊介绍: Advances in Structural Engineering was established in 1997 and has become one of the major peer-reviewed journals in the field of structural engineering. To better fulfil the mission of the journal, we have recently decided to launch two new features for the journal: (a) invited review papers providing an in-depth exposition of a topic of significant current interest; (b) short papers reporting truly new technologies in structural engineering.
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