Decision-Making Based on Multi-Dimensional Quality Control for Bridges
Source abstract
Abstract
Quality control (QC) may be applied as a framework for maintenance planning when assigning different intervention measures to single structural elements or systems. This work proposes a reliability-based maintenance decision-making process for planning visual inspections on bridges based on the value of information and prior inspection data, and also promotes updating and improvement cycles for subsequent planning. To that aim, an integration between SHM (Structural Health Monitoring) data with a multidisciplinary approach is proposed to obtain a reliability index attending to QC. The data analysis was mainly carried out with respect to an existing measurement database and structural assessments, which were combined to obtain weighted importance coefficients for each component according to their significance in the structure. The Iranian railway network has a built stock of nearly 28,200 bridges from which a database obtained from 104 bridges was studied in this work, considering the data obtained from technical identification checklists. The results were then calibrated and validated with a dataset of seven bridges, which were inspected onsite. The inspection comprised the identification and grading of damages and defects on each element. Observed defects were considered as input for the risk analysis of each component of the network by considering the probability of detection, occurrence and its likely consequences. Decision making with inspection and intervention costs optimization was then performed, for a specific case study, using Principal Component Analysis (PCA) together with the value of information (VOI) for data filtering. With this approach, several parameters with lower values reduced from inspection and other valuable data remain for bridge quality assessment with optimum maintenance cost.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Concrete Corrosion and Durability · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, ISISE, University of Minho, 4800-058 Guimarães, Portugal
Funding
FCT/MCTES through national funds (PIDDAC)
References (53)
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