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Background

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of Big Data. Machine learning has been widely used in academia and industry to analyze voluminous and intricate datasets to uncover hidden patterns and reach incisive insights. Whilst machine learning approaches have extraordinary potential and are increasingly employed to aid in various complicated tasks, their results are not wholly reliable due to the challenges introduced by data uncertainty (aleatory uncertainty) and model uncertainty (epistemic uncertainty). It is essential to accommodate uncertainties and provide uncertainty estimates to uncover beneficial information for a better decision-making process. To this end, the development and application of novel uncertainty quantification methods in tandem with different machine-learning-enhanced techniques are crucial to yield useful information and amplify the interpretability and reliability of the results. With this in mind, this SC will gather contributions presenting state-of-the-art breakthroughs in uncertainty quantification for machine learning.

Topics

  • Fundamental methods and applications of Bayesian deep learning in engineering
  • Advanced statistical inference schemes for Bayesian neural networks
  • Uncertainty-aware deep learning for civil engineering problems
  • Fundamental methods and applications of interval uncertainty analysis methods in machine learning
  • Fundamental methods and applications of fuzzy machine learning
  • Probabilistic machine learning schemes for structural dynamics and health monitoring
  • Physics-informed neural networks for forward and inverse PDE problems with noisy data
  • Advanced uncertainty quantification with the neural network-based surrogate model
  • Gaussian process schemes for forecasting, regression, and metamodeling
  • Explainable probabilistic deep learning frameworks for structural engineering
  • New strategies for improving the efficiency and accuracy of probabilistic deep learning
  • Comparative study of different probabilistic machine learning schemes for uncertainty estimates

Special Issue Publication Dates

Paper Submission Deadline: 11/30/2024

Initial Review Completed: 4/30/2025

Special Issue Publication Date: 11/30/2025

Guest Editors

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 Risk and Reliability Analysis of Resilient Civil Engineering Structures with Vibration Control Devices.
  • 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.