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May 7, 2022

Fuzzy-Logistic Models for Incorporating Epistemic Uncertainty in Bridge Management Decisions

Authors: Ahmed M. Abdelmaksoud, S.M.ASCE https://orcid.org/0000-0002-4947-8828 [email protected], Georgios P. Balomenos, M.ASCE https://orcid.org/0000-0001-6468-3654 [email protected], and Tracy C. Becker, A.M.ASCE [email protected]Author Affiliations
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
https://doi.org/10.1061/AJRUA6.0001247
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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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Information & Authors

Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 3 • September 2022

Copyright

© 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

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ASCE Technical Topics:

  1. Analysis (by type)
  2. Architectural engineering
  3. Artificial intelligence and machine learning
  4. Bridge engineering
  5. Bridge management
  6. Building management
  7. Computer programming
  8. Computing in civil engineering
  9. Construction engineering
  10. Construction management
  11. Continuum mechanics
  12. Data analysis
  13. Dynamics (solid mechanics)
  14. Engineering fundamentals
  15. Engineering mechanics
  16. Freight transportation
  17. Fuzzy logic
  18. Infrastructure
  19. Inspection
  20. Logistics
  21. Maintenance and operation
  22. Methodology (by type)
  23. Motion (dynamics)
  24. Regression analysis
  25. Research methods (by type)
  26. Solid mechanics
  27. Statistical analysis (by type)
  28. Structural engineering
  29. Transportation engineering
  30. Uncertainty principles

Authors

Affiliations

Ahmed M. Abdelmaksoud, S.M.ASCE https://orcid.org/0000-0002-4947-8828 [email protected]
Ph.D. Candidate, Dept. of Civil Engineering, McMaster Univ., 1280 Main St. W., Hamilton, ON, Canada L8S 4L7; Assistant Lecturer, Dept. of Civil Engineering, Cairo Univ., Giza 12613, Egypt. ORCID: https://orcid.org/0000-0002-4947-8828. Email: [email protected]
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Georgios P. Balomenos, M.ASCE https://orcid.org/0000-0001-6468-3654 [email protected]
Assistant Professor, Dept. of Civil Engineering, McMaster Univ., 1280 Main St. W., Hamilton, ON, Canada L8S 4L7 (corresponding author). ORCID: https://orcid.org/0000-0001-6468-3654. Email: [email protected]
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Tracy C. Becker, A.M.ASCE [email protected]
Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of California, Berkeley, 760 Davis Hall, Berkeley, CA 94720. Email: [email protected]
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