Identification and Assessment of Damage to Arch Bridge Booms Based on Quasi-static Strain Impact Lines
[1]ZHENG Yuanxun,FAN Congcong,WANG Boli,et al.Identification and Assessment of Damage to Arch Bridge Booms Based on Quasi-static Strain Impact Lines[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):14-21.[doi:10. 13705 / j. issn. 1671-6833. 2023. 06. 008]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
45
Number of periods:
2024 03
Page number:
14-21
Column:
Public date:
2024-04-20
- Title:
- Identification and Assessment of Damage to Arch Bridge Booms Based on Quasi-static Strain Impact Lines
- Keywords:
- bridge engineering; down-bearing tied arch bridge; strain-influenced line difference curvature; booms; damage assessment
- CLC:
- U466TU37
- DOI:
- 10. 13705 / j. issn. 1671-6833. 2023. 06. 008
- Abstract:
- In order to solve the problem of accurate identification of arch bridge boom damage, in this study a simplified mechanical model of an under-bearing tied arch bridge was established and the analytic formula of boom strain influence line of under-bearing tied arch bridge was obtained by force method derivation. Based on that, the damage identification method boom was proposed based on quasi-static strain influence line index of under-bearing tied arch bridge. Then the applicability of the method for conventional boom number arch bridge was verified with the help of finite element method. And the influence of test noise, damage location, damage degree, and damage category on the damage assessment results was studied by using finite element model calculations. A scientific implementation plan for vehicle loading was proposed. The results showed that within 10% test noise, the quasi-static strain-influence line difference curvature method could accurately locate the local damage location of arch bridge booms and quantitatively assess their damage degree. The method still had a good recognition effect when other structures, such as tie beams, wind braces, and arch ribs, were damaged.
- References:
-
[1] ZHENG X, YANG D H, YI T H, et al. Development of bridge influence line identification methods based on direct measurement data: a comprehensive review and comparison [J]. Engineering Structures, 2019, 198: 109539.[2] QU C X, YI T H, LI H N. Mode identification by eigensystem realization algorithm through virtual frequency response function[ J] . Structural Control and Health Monitoring, 2019, 26(10) : e2429.[3] 李戎, 杨萌, 刘林霞, 等. 水下 FG 圆柱壳临界载荷和 固有频率 预 测 方 法 [ J] . 郑 州 大 学 学 报 ( 工 学 版) , 2021, 42(1) :70-76. LI R, YANG M, LIU L X, et al. Prediction method for critical buckling pressure and natural frequency of submerged functionally graded cylindrical shell[ J] . Journal of Zhengzhou University ( Engineering Science) , 2021, 42(1) :70-76.[4] POOYA S M H, MASSUMI A. A novel damage detection method in beam-like structures based on the relation between modal kinetic energy and modal strain energy and using only damaged structure data[ J] . Journal of Sound and Vibration, 2022, 530: 116943.[5] 周宇, 狄生奎, 王立宪, 等. 基于柔度指标的系杆拱 桥损伤 识 别 与 寿 命 预 测 [ J] . 防 灾 减 灾 工 程 学 报, 2018, 38(6) : 1033-1043. ZHOU Y, DI S K, WANG L X, et al. Damage detection and life prediction of tied arch bridge based on flexibilitycurvature-difference-rate[J]. Journal of Disaster Prevention and Mitigation Engineering, 2018, 38(6): 1033-1043.[6] 王立宪, 周宇, 狄生奎, 等. 考虑边界非理想的铁路 桥梁挠度影响线分析与损伤识别[ J] . 工程科学与技 术, 2020, 52(3) :123-132. WANG L X, ZHOU Y, DI S K, et al. Influence line analysis and damage detection of railway bridge deflection with non-ideal boundaries [ J]. Journal of Sichuan University (Engineering Science Edition), 2020, 52(3):123-132.[7] 周宇, 狄生奎, 项长生, 等. 基于弹性约束支承梁转 角影响线的梁结构损伤诊断[ J] . 浙江大学学报( 工 学版) , 2020, 54(5) :879-888. ZHOU Y, DI S K, XIANG C S, et al. Beam structure damage detection based on rotational-angle-influence-lines of elastic-constrained-support beam [ J] . Journal of Zhejiang University ( Engineering Science) , 2020, 54 ( 5) : 879-888.[8] 王艺霖, 张平, 安新梅. 基于竖向支座反力的两跨连 续梁桥损伤定位方法[ J] . 中国公路学报, 2014, 27 (4) :79-84, 105. WANG Y L, ZHANG P, AN X M. Two-span continuous bridge damage localization method based on vertical support reaction[ J] . China Journal of Highway and Transport, 2014, 27(4) :79-84, 105.[9] WU B T, WU G, YANG C Q. Parametric study of a rapid bridge assessment method using distributed macrostrain influence envelope line [ J ] . Mechanical Systems and Signal Processing, 2019, 120: 642-663. [10] CHEN S Z, WU G, WU B T. Simultaneous identification method of damage and vehicle parameters on bridges utilizing long-gauge strain influence line under moving vehicle loads [ J ] . Proceeding of the SPIE, 2017, 10168: 573-582.[11] YANG J, HOU P, YANG C Q, et al. Study on the method of moving load identification based on strain influence line[ J] . Applied Sciences, 2021, 11(2) : 853.[12] 周宇, 狄生奎, 李喜梅, 等. 基于弹性约束梁应变影 响线曲率的桥梁结构损伤识别[ J] . 应用基础与工程 科学学报, 2021, 29(4) : 901-914. ZHOU Y, DI S K, LI X M, et al. Damage identification of bridge structural based on strain influence line curvature of elastic restrained beam[ J] . Journal of Basic Science and Engineering, 2021, 29(4) : 901-914.[13] 王涛, 张丽莎, 高岩. 基于 BP 神经网络的改进型新 奇检测技术诊断大跨度拱桥异常状态[ J] . 北京理工 大学学报, 2016, 36(2) :157-162. WANG T, ZHANG L S, GAO Y. Abnormality identification of large-span arch bridge based on BP neural improved novelty detection technique [ J ] . Transactions of Beijing Institute of Technology, 2016, 36(2) :157-162.[14] 张安琳, 张启坤, 黄道颖, 等. 基于 CNN 与 BiGRU 融 合神经网络的入侵检测模型[ J] . 郑州大学学报( 工 学版) , 2022, 43(3) : 37-43. ZHANG A L, ZHANG Q K, HUANG D Y, et al. Intrusion detection model based on CNN and BiGRU fused neural network [ J ] . Journal of Zhengzhou University (Engineering Science) , 2022, 43(3) : 37-43.[15] SEN S, ASWAL N, ZHANG Q H, et al. Structural health monitoring with non-linear sensor measurements robust to unknown non-stationary input forcing[J]. Mechanical Systems and Signal Processing, 2021, 152: 107472.
- Similar References:
Memo
-
Last Update:
2024-04-29