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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. ...

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 » Background Uncertainty Quantification (UQ) focuses on identifying, characterizing, and managing uncertainties in computational models and real-world systems. These uncertainties are classified into aleatory and epistemic types. Aleatory uncertainty, arising from inherent variability in natural systems, is irreducible, such as fluctuations in material properties. Epistemic uncertainty results from incomplete knowledge or assumptions in the modeling process and can be reduced with better information or models. Both types often coexist in practical problems, and quantifying them is essential for reliable predictions in various scientific and engineering disciplines. Forward Uncertainty Quantification (FUQ) is a specialized area within UQ that predicts how uncertain inputs affect model outputs, considering both aleatory and epistemic uncertainties. This process is vital for developing robust models that reflect real-world behavior under uncertainty. For example, FUQ helps ensure the safety and reliability of engineering structures by assessing how uncertainties influence their performance, aiding in decision-making, risk management, and optimization. ...

Submit Paper » Large-scale systems are prevalent across critical infrastructure, manufacturing, offshore, automotive, aerospace, energy, and other sectors. These systems are inherently complex, and are characterized by the interactions among various components within the system and between the system and its environment. These systems are plagued with uncertainties stemming from various sources including incomplete or unreliable information, lack of data, and partially known physics. There is a growing demand for advanced techniques that can efficiently manage large-scale system complexity and result in robust and reliable design solutions with limited computational resources, ultimately minimizing failures with catastrophic consequences. ...

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 » Background Engineering systems and structures are often subject to a wide range of uncertainties arising from material properties, environmental conditions, manufacturing tolerances, operational fluctuations, etc. Probabilistic analysis is usually applied to describe these uncertainties, although more often than not they also involve epistemic uncertainties arising from modelling the randomness under insufficient information, and/or a lack of modelling details of the physical processes with computational simulators. Accurately quantifying these uncertainties is critical for designing robust and reliable engineering solutions. This Special Issue aims to highlight the latest developments and innovative approaches in the field of uncertainty quantification tailored specifically for engineering applications. ...