Risk-based methodology to assess bridge condition based on visual inspection

被引:20
|
作者
Bertola, Numa J. [1 ]
Bruehwiler, Eugen [1 ]
机构
[1] Swiss Fed Inst Technol Lausanne EPFL, Sch Architecture Civil & Environm Engn ENAC, Lab Maintenance & Safety Struct MCS, Lausanne, Switzerland
关键词
Bridges; condition evaluations; visual inspection; infrastructure management; concrete structures; risk assessment; damage detection; CIVIL INFRASTRUCTURE; RELIABILITY; MANAGEMENT;
D O I
10.1080/15732479.2021.1959621
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The visual inspection of existing infrastructure is a critical step for asset management, as the detection and quantification of damage must be useful to prioritise maintenance. In Switzerland, main inspections are made every five years for all road bridges. For each bridge, a condition value ranging from 1 to 5 is given. As only element-based degradations are currently taken into account in bridge-condition evaluations, inaccurate assessments of global structural safety are often provided by bridge inspectors. In this paper, a risk-based methodology is introduced to evaluate bridge conditions based on visual-inspection data. Degradation states of bridge elements are coupled with element-failure consequences on the global structural safety in risk analysis to accurately assess the bridge condition. A case study of a strategic road involving sixty bridges is used to assess bridge-condition evaluations using the risk-based methodology based on recent visual inspections. The study reveals that including element-failure consequences in bridge-condition assessments supports more accurate evaluations of the impacts of damage on the global structural safety, leading to more objective decisions on asset management actions. Analyses of four damaged bridges show that inspection reports are often over-pessimistic in terms of structural damage, and this can lead to unnecessary rehabilitation interventions.
引用
收藏
页码:575 / 588
页数:14
相关论文
共 50 条
  • [31] Machine learning approach for risk-based inspection screening assessment
    Rachman, Andika
    Ratnayake, R. M. Chandima
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2019, 185 : 518 - 532
  • [32] Risk-based maintenance and inspection of riverine flood defence systems
    Klerk, Wouter Jan
    van Bergeijk, Vera
    Kanning, Wim
    Wolfert, Rogier
    Kok, Matthijs
    STRUCTURAL SAFETY, 2024, 106
  • [33] A risk-based tool to support the inspection management in chemical plants
    Vianello, Chiara
    Milazzo, Maria Francesca
    Guerrini, Ludovica
    Mura, Alberto
    Maschio, Giuseppe
    JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2016, 41 : 154 - 168
  • [34] New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes
    Folch-Calvo, Martin
    Brocal, Francisco
    Sebastian, Miguel A.
    MATERIALS, 2019, 12 (22)
  • [35] Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar
    Alsharqawi, Mohammed
    Zayed, Tarek
    Abu Dabous, Saleh
    AUTOMATION IN CONSTRUCTION, 2018, 89 : 135 - 145
  • [36] Risk-based importance factors for bridge networks under highway traffic loads
    Fiorillo, Graziano
    Ghosn, Michel
    STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2019, 15 (01) : 113 - 126
  • [37] Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection
    Zhang, Wei-Heng
    Qin, Jianjun
    Lu, Da-Gang
    Liu, Min
    Faber, Michael H.
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 231
  • [38] Development of track condition assessment model based on visual inspection
    Sadeghi, J. M.
    Askarinejad, H.
    STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2011, 7 (12) : 895 - 905
  • [39] Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks
    Luque, Jesus
    Straub, Daniel
    STRUCTURAL SAFETY, 2019, 76 : 68 - 80
  • [40] Self-Adaptive Risk-Based Inspection Planning in Petrochemical industry by evolutionary algorithms
    Dabagh, Sh
    Javid, Y.
    Sobhani, F. M.
    Saghaiee, A.
    Parsa, K.
    JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2022, 77