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Background

Bayesian inference provides a fundamental probabilistic framework to quantify uncertainty, incorporate evolving information, and make informed decisions. Over the years it has attracted ever-growing interest in various fields of science and engineering. In structural health monitoring (SHM) the approach has been explored for addressing challenges in extracting actionable information from data for structural identification, load estimation, damage diagnosis and prognosis, and remaining useful life prediction, for unknown and potentially changing structure and environment. Amidst emerging technologies such as artificial intelligence, machine learning, and digital twin, there are opportunities for exploring Bayesian techniques along deep learning methods to better account for modeling errors and uncertainties. This special collection aims to create a collaborative research platform for academics and practitioners worldwide to present the latest advances in Bayesian inference for SHM, focusing on its applications in monitoring and decision-making, covering the targeted engineering systems of civil infrastructure, mechanical systems, and aerospace structures.

Topics

  • Bayesian operational modal analysis
  • Bayesian machine learning and its SHM application
  • Bayesian model updating and its SHM application
  • Bayesian information fusion and its SHM application
  • Bayesian filters and their SHM application
  • Bayesian probabilistic modeling and their SHM application
  • Bayesian predictive maintenance and their SHM application
  • Bayesian decision-making and their SHM applications
  • New methods and approaches in Bayesian inference and uncertainty quantification
  • Case studies and real-world applications using Bayesian inferences

Special Issue Publication Dates

Paper submission deadline: October 31, 2025

Initial review completed: January 31, 2026

Publication date: June 30, 2026

Guest Editors

  • Yuguang Fu, Assistant Professor, School of Civil and Environmental Engineering, Nanyang Technological University, ( yuguang.fu@ntu.edu.sg )
  • Binbin Li, Assistant Professor, Zhejiang University- University of Illinois at Urbana-Champaign Institute, Zhejiang University, ( binbinli@intl.zju.edu.cn )
  • Hamed Ebrahimian, Assistant Professor, Department of Civil and Environmental Engineering at the University of Nevada, Reno, ( hebrahimian@unr.edu )
  • Siu-Kui Au, Professor, School of Civil and Environmental Engineering, Nanyang Technological University, ( ivanau@ntu.edu.sg )

Submission Guidelines

  • Please submit your manuscript via the Journal of ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering website.
  • If you already have an Editorial Manager account, log in as author and select Submit Paper at the bottom of the page. If you do not have an account, select Submissions and follow the steps. In either case, at the Paper Submittal page, select the Journal of ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering and then select the special collection Forward Uncertainty Quantification for Aleatory & Epistemic Uncertainties: Methodologies, Tools, and Applications.
  • Detailed information on the submission process is provided in the “Publishing in ASCE Journals” section of the ASCE Author Center .
  • Papers received after the deadline or papers not selected for inclusion in the Special Issue may be accepted for publication in a regular issue.

Please note that all accepted papers submitted in response to this Call for Papers will be published in regular issues of the Journal of ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering and assembled online on a page dedicated to this Special Collection. See Journal of ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering Special Collections for the list of Special Collections already published.