Part A

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 » 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. ...

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 » 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. ...

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 » 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. ...