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Description

The rapid advancement of sensing technologies, structural health monitoring (SHM), and intelligent data analytics has significantly enhanced the ability to detect damage and diagnose faults in engineering systems. From bridges and tunnels to energy facilities and transportation networks, large volumes of monitoring data are continuously collected through distributed sensors, imaging systems, and inspection platforms. Meanwhile, advances in signal processing, machine learning, and deep learning have enabled increasingly accurate and automated condition assessment.

However, a critical gap remains between intelligent condition monitoring and risk assessment in engineering systems. While data-driven diagnostic methods can identify damage or anomalies, their outputs are often not systematically integrated into probabilistic reliability analysis, risk quantification, or decision-making frameworks. Uncertainties arising from measurement noise, environmental variability, model assumptions, and algorithmic predictions may propagate through the assessment process, influencing system-level risk estimation and maintenance planning.

Recent work in this area, including prior Special Collections on Bayesian inference and monitoring-based decision frameworks, has advanced probabilistic modeling and reliability updating using monitoring data. Building upon these developments, this Special Collection focuses on the integration of intelligent condition monitoring with risk assessment. In particular, it emphasizes how advances in signal processing, machine learning, and deep learning can be incorporated into probabilistic frameworks for reliability evaluation, risk quantification, and life-cycle decision-making.

This Special Collection aims to promote research that bridges monitoring data, intelligent diagnostics, and risk-informed decision support in civil infrastructure and industrial engineering systems. Contributions are encouraged that integrate monitoring data, signal processing results, and AI-based diagnostic outputs into probabilistic frameworks for system safety evaluation, life-cycle management, and risk mitigation. Particular attention is given to studies on uncertainty quantification, reliability updating using real-time data, and risk-informed maintenance and infrastructure management.

Topics

  • Intelligent condition monitoring for civil and industrial systems
  • Signal processing techniques for damage detection and anomaly identification
  • Machine learning and deep learning for condition monitoring and diagnostics
  • Uncertainty quantification in monitoring data and diagnostic models
  • Bayesian updating and probabilistic damage assessment
  • Reliability updating using monitoring and inspection data
  • Integration of monitoring results into risk assessment frameworks
  • Monitoring-informed risk analysis and decision support
  • Risk-informed maintenance and life-cycle management
  • Physics-informed and hybrid data-driven models
  • Explainable and trustworthy AI for diagnostics
  • Digital twin-based monitoring and risk evaluation

Special Issue Publication Dates

Paper submission deadline: November 30, 2026

Initial review completed: February 28, 2027

Publication date: May 31, 2027

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 to the Special Collection on Integrating Intelligent Condition Monitoring with Risk Assessment in Engineering Systems.

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.