Special Collection/Issue

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. ...

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. ...

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 » 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. ...

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. ...

Submit Paper » Background This Special Collection (SC) aims to gather contributions to advance the state-of-the-art methods and applications of uncertainty propagation in high-dimensional stochastic systems. Effective uncertainty propagation is critical for rational decision-making, risk assessment, and optimization of engineering systems. Particularly, high-dimensional stochastic systems represent a significant class of problems encountered in various domains. Nevertheless, uncertainty propagation in high-dimensional settings poses significant challenges due to the “curse of dimensionality”. Traditional methods often become computationally prohibitive. Consequently, there is a growing need for advanced techniques to handle the complexity of high-dimensional systems accurately and efficiently. This SC focuses on efficient analytical, data-driven, and computational methods for uncertainty propagation, novel control techniques for stochastic systems, and advanced optimization approaches. Grounded in solid theory, these methods aim for real-world applications, bridging the gap between theory and practice with practical solutions in aerospace, civil engineering, energy systems, and environmental modeling. ...

Submit Paper » Aims & Scope This Special Collection (SC) aims to provide a dedicated space for in-depth exploration and dissemination of advancements in uncertainty modeling and quantification of numerical methods in geotechnical engineering. The primary goal of this SC is to feature emerging developments, which address the calibration of soil or rock constitutive models developed in recent time, data-driven and physics-informed models for soil or rock constitutive relations, database assessment of the variability in geotechnical numerical predictions, and benchmark exercises for geotechnical analyses by commercial software. The contributions are supposed to provide a deeper insight into the calibration and verification of numerical models in geotechnics, as well as the quantification of variability in numerical predictions of geo-structural response (e.g., deformation, capacity or stability). By establishing this SC, we aim to foster a collaborative environment that encourages researchers to contribute high-quality works, sharing insights and innovations in the field of uncertainty in geotechnical numerical methods. ...