木桥结构健康监测 - 综述

IF 6 Q1 ENGINEERING, MULTIDISCIPLINARY
Farshid Abdoli , Maria Rashidi , Jun Wang , Rafat Siddique , Vahid Nasir
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引用次数: 0

摘要

有关木桥结构健康监测的研究十分有限。由于木材具有各向异性、吸湿性以及在气候变化下的高变化性等独特特性,因此有必要为每种特定结构定制结构健康监测系统。本文深入探讨了现有的木桥结构健康监测方法,包括木桥劣化机理、实用的无损检测技术、人工智能、数字孪生、传感器/数据融合和物联网等未来在木桥结构健康监测应用中的潜在进步。与钢或混凝土等其他材料制造的桥梁相比,木桥结构健康监测受到的关注较少。更具体地说,大多数研究都是针对监测木材桥梁的湿度、温度、结构性能和荷载下的振动行为。然而,这些研究并未调查影响木桥结构性能的因素之间的相关性。此外,与应用于木桥的数据驱动和人工智能方法相关的研究也很有限。因此,当前的研究是关于木桥的结构健康监测,重点是木材的劣化机制、用于检测损坏的非破坏性工具,以及对新兴方法的讨论,包括人工智能工具、传感器/数据融合和物联网以及数字孪生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural health monitoring of timber bridges – A review
Studies on structural health monitoring of timber bridges are limited. The unique characteristics of timber, such as anisotropy, hygroscopicity, and high variability under climate changes, necessitate the customization of a structural health monitoring system for each specific configuration. This paper provides a thorough examination of the existing methodologies for structural health monitoring of timber bridges, including the timber bridge deterioration mechanism, the practical non-destructive testing techniques, potential future advancements such as artificial intelligence, digital twin, and sensor/data fusion and Internet of Things in the application of structural health monitoring to timber bridges. Structural health monitoring of timber bridges has received less attention than monitoring of bridges manufactured from other materials such as steel or concrete. More specifically, most studies have been conducted on monitoring moisture, temperature, structural performance, and vibration behavior under the loads in timber bridges. However, these studies have not investigated the correlation between the factors that affected the structural performance of timber bridges. Also, studies related to data-driven and artificial intelligence methods applied to timber bridges are limited. Therefore, the current study is about structural health monitoring of timber bridges by focusing on deterioration mechanisms of timber, non-destructive tools for damage detection, and discussion about emerging approaches, including artificial intelligence tools, sensor/data fusion and Internet of Things, and digital twin.
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来源期刊
Results in Engineering
Results in Engineering Engineering-Engineering (all)
CiteScore
5.80
自引率
34.00%
发文量
441
审稿时长
47 days
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