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Description

The digital era is reshaping geotechnical uncertainty quantification (UQ) and reliability analysis through modern sensing, continuous monitoring, high-performance computing, and digital-twin ecosystems. Practice is increasingly data-rich, yet still challenged by sparse/biased data, nonstationarity, and complex ground–structure interactions. Meanwhile, the rapid integration of machine learning with physics-based and probabilistic models raises new demands for rigor, transparency, data efficiency, and deployability. This Special Collection seeks original research and practice-oriented advances that (i) separate, represent, propagate, and reduce uncertainty from site characterization and design to construction and operation, and (ii) translate uncertainty into decision-ready reliability and risk metrics. Contributions featuring verification/validation, uncertainty-aware interpretability, and reproducible workflows or well-documented datasets/case studies are particularly encouraged.

Topics

  • ML-assisted UQ for soil/rock properties and spatial random fields
  • Physics-informed and surrogate models (e.g., PINNs, GP, ensembles) for UQ & sensitivity
  • Integration of digital twins and sensor data for real-time uncertainty reduction
  • Bayesian methods and optimization techniques in geotechnical reliability
  • Probabilistic modeling and simulation using AI for geotechnical reliability analysis
  • Big-data/IoT-driven monitoring and real-time reliability updating
  • Digital twins for dynamic reliability- and risk-based decision-making
  • Case studies on applying new paradigms and methods to infrastructure resilience and sustainable geoengineering

Special Issue Publication Dates

Paper submission deadline: October 31, 2026

Initial review completed: January 31, 2026

Publication date: Date: June 30, 2027

Guest Editors

  • Zi-Jun Cao , Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, China
  • Tengyuan Zhao , School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, China
  • Yu Wang , Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

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 Geotechnical Uncertainty Quantification and Reliability Analysis in the Digital Era: New Paradigms, Methods, and Applications.

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.