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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. Given that reliability analysis is data-driven in nature, a significant challenge is the limited information availability, such as: 1) component reliability data; and 2) model-form certainty over the structure/system. These introduce uncertainty in the analysis, which is present in real-world engineering problems. Hence, the reliability analysis is often accompanied by an uncertainty analysis, which can be performed via the Bayesian approach. ...

Submit Paper » 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. ...

The ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering is pleased to announce the launch of a new cycle of its Early Career Editorial Board (ECEB). Following the great success of the previous two cycles, this initiative continues to provide a unique opportunity for early career researchers to contribute to editorial leadership while gaining invaluable insight into the scholarly publishing process. The ECEB program is designed to support researchers within 1–3 years of earning their doctoral degree who have demonstrated excellence in scholarship and a commitment to advancing the journal’s mission. Selected members participate in a range of editorial tasks under the mentorship of the journal’s associate editors and leadership team. ...

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. ...

We are delighted to announce the recipients of the 2024 Bilal M. Ayyub Research Award for Risk and Uncertainty in Engineering Systems, recognizing the best papers published in our journal this year. The award highlights outstanding scholarly contributions to the advancement of risk and uncertainty analysis in engineering systems. 📰 Best Paper in Part A: Civil Engineering Title: Risk Tolerance, Aversion, and Economics of Energy Utilities in Community Resilience to Wildfires Authors: Bilal M. Ayyub, Ramsay M. Raven, David R. Johnson, Jennifer Helgeson, Yumi Suzuki, Vincent R. Tidwell Published: June 2024 DOI: 10.1061/AJRUA6.RUENG-1254 ...

Submit Paper » The rapid evolution of mechanical systems and increasing industrial complexity have driven the need for advanced predictive maintenance strategies. Cognitive Digital Twins (CDTs), integrating AI, real-time data analytics, and cognitive computing, have emerged as a transformative solution. Unlike traditional digital twins, CDTs can learn, reason, and adapt, enabling more accurate and dynamic predictive maintenance. However, their reliability is challenged by modeling uncertainties, sensor noise, environmental variability, and unforeseen operational conditions. ...

Submit Paper » Industrial equipment, such as engine, robot, machine tool, energy harvester, vehicle, etc., plays a pivotal role in enhancing production efficiency, ensuring product quality, and reducing labor expenses. However, the randomness of structural parameters and external excitations can potentially threaten the operation and safety of industrial equipment. Consequently, structural reliability, which can quantify the given performance and safety level of system under various uncertainties, is essential to ensure the quality of industrial equipment. ...

Image Source: vanderbilt.edu On behalf of the ASCE-ASME Journal of Risk and Uncertainty in Engineering, we congratulate Professor Sankaran Mahadevan on receiving the 2025 Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure. EMI Past-President, Sankaran Mahadevan , Ph.D., F.EMI, M.ASCE, is the 2025 recipient of the Alfredo Ang Award on Risk Analysis & Management of Civil Infrastructure. Prof. Mahadevan has contributed extensively to risk and reliability analysis, uncertainty quantification, and decision-making for engineered systems. As a former Managing Editor of the journal, he played a key role in advancing research and its dissemination in these areas. ...

Submit Paper » Background Bayesian inference provides a fundamental probabilistic framework to quantify uncertainty, incorporate evolving information, and make informed decisions. Over the years it has attracted ever-growing interest in various fields of science and engineering. In structural health monitoring (SHM) the approach has been explored for addressing challenges in extracting actionable information from data for structural identification, load estimation, damage diagnosis and prognosis, and remaining useful life prediction, for unknown and potentially changing structure and environment. Amidst emerging technologies such as artificial intelligence, machine learning, and digital twin, there are opportunities for exploring Bayesian techniques along deep learning methods to better account for modeling errors and uncertainties. This special collection aims to create a collaborative research platform for academics and practitioners worldwide to present the latest advances in Bayesian inference for SHM, focusing on its applications in monitoring and decision-making, covering the targeted engineering systems of civil infrastructure, mechanical systems, and aerospace structures. ...