Farshid Abdoli , Maria Rashidi , Jun Wang , Rafat Siddique , Vahid Nasir
{"title":"Structural health monitoring of timber bridges – A review","authors":"Farshid Abdoli , Maria Rashidi , Jun Wang , Rafat Siddique , Vahid Nasir","doi":"10.1016/j.rineng.2024.103084","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"24 ","pages":"Article 103084"},"PeriodicalIF":6.0000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590123024013392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.