Fuzzy-Logistic Models for Incorporating Epistemic Uncertainty in Bridge Management Decisions
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
Abstract
Many bridge management systems (BMSs) plan future maintenance and inspection based on deterioration models derived from probabilistic analysis of field inspection data. Such analysis considers the aleatoric but not the epistemic uncertainty arising from subjective or imprecise data. This raises questions regarding the efficiency and safety of maintenance and inspection decisions. Several methodologies have been proposed to address both uncertainties; however, they tend to be taxing in terms of inspection data requirements. Thus, this work proposes a new BMS-compatible methodology to derive deterioration models using logistic regression to capture aleatoric uncertainty and fuzzy set theory to capture epistemic uncertainty. To formulate the models, subjective or imprecise data, such as bridge condition rating, is modeled using membership functions, rather than discrete values, and then integrated into logistic regression analysis. This results in logistic models with fuzzy coefficients. The proposed fuzzy-logistic models can be used to predict a range of possible future bridge conditions, rather than a discrete condition and hence lead to a range of possible management strategies that can be then optimized using life-cycle cost analysis. The application of the proposed framework is demonstrated through a case study.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors gratefully acknowledge the support from the Ontario Graduate Scholarship provided by the Government of Ontario, and the start-up funding provided by the Faculty of Engineering at McMaster University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor.
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© 2022 American Society of Civil Engineers.
History
Received: Sep 27, 2021
Accepted: Mar 10, 2022
Published online: May 7, 2022
Published in print: Sep 1, 2022
Discussion open until: Oct 7, 2022
ASCE Technical Topics:
- Analysis (by type)
- Architectural engineering
- Artificial intelligence and machine learning
- Bridge engineering
- Bridge management
- Building management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Continuum mechanics
- Data analysis
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Freight transportation
- Fuzzy logic
- Infrastructure
- Inspection
- Logistics
- Maintenance and operation
- Methodology (by type)
- Motion (dynamics)
- Regression analysis
- Research methods (by type)
- Solid mechanics
- Statistical analysis (by type)
- Structural engineering
- Transportation engineering
- Uncertainty principles
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