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Archive note (July 13, 2026): This newsletter is preserved as published. For a concise, deadline-sorted view of active opportunities, see Open Calls for Papers . ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems - Call for Papers Part A: Civil Engineering, and Part B. Mechanical Engineering The Editorial Board of the ASCE-ASME Journal invites contributions presenting state-of-the-art research and best practices for addressing risk, disaster and failure-related challenges arising from uncertainty. We particularly welcome emerging research relating to availability and processing of big data, including data driven decision support, machine learning and further computational intelligence tools relating to risk and uncertainty. ...

Submit Paper » Modern engineering systems, from vibration-sensitive mechanical devices and safety-critical structures to aerospace, transportation, industrial, and energy applications, are increasingly equipped with advanced sensing, modeling, and control technologies to enhance safety, serviceability, and resilience. However, assessing and predicting their performance are inevitably affected by multiple sources of uncertainty, including variability in material and geometric properties, modeling simplifications, measurement noise, environmental effects, and the stochastic nature of external excitations. ...

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems - Call for Papers Part A: Civil Engineering, and Part B. Mechanical Engineering The Editorial Board of the ASCE-ASME Journal invites contributions presenting state-of-the-art research and best practices for addressing risk, disaster and failure-related challenges arising from uncertainty. We particularly welcome emerging research relating to availability and processing of big data, including data driven decision support, machine learning and further computational intelligence tools relating to risk and uncertainty. ...

Submit Paper » 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. ...

Submit Paper » 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. ...

Submit Paper » Description This Special Collection (SC) aims to provide a dedicated space for the in-depth exploration and dissemination of advancements in reliability-based, risk-based, and uncertainty-informed decision-making. The primary goal of this SC is to showcase emerging developments that address reliability and risk management to enhance the resilience and sustainability of our infrastructure systems and built environment. Contributions are expected to present key ideas, concepts, and technologies for solving significant challenges posed by the high complexity and multidisciplinary nature of problems as well as the comprehensive quantification, efficient processing, and management of induced uncertainties. By establishing this SC, we aim to foster a collaborative environment that encourages researchers to share insights and innovations in the multifaceted fields of risk, uncertainty, and decision-making within infrastructure systems. ...

Submit Paper » About this joint special collection This multi-journal ASCE special collection brings together research, applied case studies, and forward-looking perspectives on engineering solutions for climate adaptation and infrastructure resilience. Topics include risk modeling, performance-based and life-cycle engineering, adaptive design, resilient infrastructure planning, digital twins, artificial intelligence, and decision-making under deep uncertainty. ...

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems - Call for Papers Part A: Civil Engineering, and Part B. Mechanical Engineering The Editorial Board of the ASCE-ASME Journal invites contributions presenting state-of-the-art research and best practices for addressing risk, disaster and failure-related challenges arising from uncertainty. We particularly welcome emerging research relating to availability and processing of big data, including data driven decision support, machine learning and further computational intelligence tools relating to risk and uncertainty. ...

Submit Paper » Description Reliability is a key aspect of safety-critical structures and systems such as bridges, aircrafts, dams, and nuclear structural facilities. For this reason, the performance of such structures and systems needs to be assessed via reliability analysis, to ensure they operate safely and, in turn, protect lives. ...