Indirect damage detection for bridges using sensing and temporarily parked vehicles

稳健性(进化) 情态动词 卡车 残余物 桥(图论) 结构工程 计算机科学 结构健康监测 工程类 汽车工程 算法 材料科学 医学 基因 内科学 生物化学 化学 高分子化学
作者
Zhenkun Li,Yifu Lan,Weiwei Lin
出处
期刊:Engineering Structures [Elsevier]
卷期号:291: 116459-116459 被引量:3
标识
DOI:10.1016/j.engstruct.2023.116459
摘要

Due to the influence of many factors such as vehicle properties, road roughness, and external noises, accurate indirect identification of the bridge’s frequencies is challenging. Further, given the insensitivity of the bridge’s frequencies to damage and limited acquired modal information, damage detection is often difficult to be implemented in practical engineering. This paper proposes an indirect approach to localize and quantify bridge damage using sensing and parked vehicles. First, equations for back-calculating residual contact-point responses of the sensing vehicle with suspension and tire damping and sensor-installing errors are newly deduced to eliminate its self-frequencies and suppress the negative effects of road roughness. Second, another temporarily parked truck is introduced to increase the amount of modal information about the bridge and its sensitivity to local damage. Third, a novel modal assurance criterion-based objective function using indirectly identified frequencies is proposed to enhance the robustness of damage detection. Numerical simulations utilizing a half-car model and a simply supported bridge verify the effectiveness of the proposed strategy. It is found that the new objective function improves the robustness of damage detection when the parked truck is employed at different positions. In addition, a higher speed of the sensing vehicle can negatively affect damage detection, while the ongoing traffic can help to resist the negative impact of environmental noises and bridge damping. By considering possible influence factors and model updating errors in practical applications, the damage can be located and quantified with acceptable precision.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
哲欣完成签到,获得积分10
刚刚
无花果应助123456采纳,获得10
1秒前
2秒前
淡定猎豹完成签到,获得积分20
2秒前
3秒前
changping应助科研通管家采纳,获得10
4秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
lalala应助科研通管家采纳,获得10
4秒前
lasalu应助科研通管家采纳,获得10
4秒前
FashionBoy应助科研通管家采纳,获得100
4秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
SciGPT应助科研通管家采纳,获得10
4秒前
小马甲应助科研通管家采纳,获得10
4秒前
英姑应助科研通管家采纳,获得10
5秒前
lalala应助科研通管家采纳,获得10
5秒前
科研通AI2S应助科研通管家采纳,获得10
5秒前
科研通AI6应助科研通管家采纳,获得10
5秒前
科研通AI2S应助科研通管家采纳,获得10
5秒前
科研通AI6应助科研通管家采纳,获得10
5秒前
小马甲应助科研通管家采纳,获得10
5秒前
李爱国应助科研通管家采纳,获得10
5秒前
chenqiumu应助科研通管家采纳,获得30
5秒前
淡定猎豹发布了新的文献求助10
5秒前
852应助科研通管家采纳,获得10
6秒前
lalala应助科研通管家采纳,获得10
6秒前
科研通AI2S应助科研通管家采纳,获得10
6秒前
今后应助科研通管家采纳,获得30
6秒前
烟花应助科研通管家采纳,获得10
6秒前
6秒前
乐乐应助科研通管家采纳,获得10
6秒前
6秒前
6秒前
Hello应助科研通管家采纳,获得10
6秒前
6666发布了新的文献求助10
6秒前
慕青应助Painkiller_采纳,获得10
7秒前
龙龙冲发布了新的文献求助20
9秒前
养狗了没有完成签到 ,获得积分10
9秒前
小鱼儿发布了新的文献求助10
10秒前
肥猫发布了新的文献求助30
11秒前
懦弱的博涛给懦弱的博涛的求助进行了留言
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Vertébrés continentaux du Crétacé supérieur de Provence (Sud-Est de la France) 600
A complete Carnosaur Skeleton From Zigong, Sichuan- Yangchuanosaurus Hepingensis 四川自贡一完整肉食龙化石-和平永川龙 600
FUNDAMENTAL STUDY OF ADAPTIVE CONTROL SYSTEMS 500
微纳米加工技术及其应用 500
Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices 500
Performance optimization of advanced vapor compression systems working with low-GWP refrigerants using numerical and experimental methods 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5306536
求助须知:如何正确求助?哪些是违规求助? 4452296
关于积分的说明 13854370
捐赠科研通 4339755
什么是DOI,文献DOI怎么找? 2382830
邀请新用户注册赠送积分活动 1377724
关于科研通互助平台的介绍 1345400