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Description

The emergence of large language models (LLMs) and multimodal foundation models has revolutionized traditional approaches to risk assessment and predictive maintenance in engineering systems. These AI systems demonstrate unprecedented capabilities in processing heterogeneous data streams - from textual maintenance logs and equipment manuals to time-series sensor data and visual inspection reports - enabling comprehensive fault diagnosis across civil infrastructure (e.g., bridges, dams, power grids) and mechanical systems (e.g., rotating machinery, HVAC systems, industrial robots). However, the deployment of LLMs in safety-critical engineering applications introduces profound challenges that demand urgent research attention. First, the probabilistic nature of LLMs leads to inherent epistemic uncertainty in fault diagnosis, compounded by issues of model hallucination when interpreting sparse or noisy field data. Second, the black-box decision-making process of current models creates significant barriers to engineering validation, particularly in regulated industries where traceable risk assessment is mandatory. Third, the dynamic operating conditions of engineering systems (e.g., seasonal load variations in civil infrastructure or wear progression in mechanical components) require continuous model adaptation while maintaining operational safety margins. This special issue seeks to address these challenges through cutting-edge research at the intersection of AI reliability and engineering risk management.

Topics

  • LLMs for infrastructure risk monitoring (e.g., bridges, pipelines, power grids)
  • LLMs for fault detection in mechanical systems (e.g., turbines, HVAC, robotics)
  • Multimodal LLMs for sensor fusion and anomaly detection
  • LLM-based decision support for predictive maintenance
  • AI-driven predictive maintenance
  • Physics-guided machine learning in fault diagnosis
  • Uncertainty quantification in LLM-based diagnostics
  • Explainable AI for LLM-driven recommendations
  • Data-driven approaches for predictive maintenance under uncertainty

Special Issue Publication Dates

Paper submission deadline: May 31, 2026

Initial review completed: August 30, 2026

Publication date: December 2026

Guest Editors

Submission Guidelines

Authors should submit manuscripts electronically through the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering Editorial Manager website.

Authors should prepare their manuscripts according to guidelines found in the ASCE Author Center .

When submitting, authors should indicate in the submission questions that the paper is being submitted in response to this call for papers Large Language Models for Engineering Risk and Uncertainty: Applications in Fault Diagnosis and Predictive Maintenance.

Please note that this is an invitation to submit papers for peer review and does not imply acceptance for publication. Acceptance of submitted papers depends on the results of the normal refereed peer review process of the journal.

All accepted papers submitted through this solicitation will be published in regular issues of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering as they are accepted. They will also be added to the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering Special Collections (similar to a print version of a special issue) page and indexed for citations like other regular journal papers.