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Submissions for this archived call are closed. 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. ...

Submissions for this archived call are closed. 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. ...

Submissions for this archived call are closed. 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. ...

Submissions for this archived call are closed. Engineering systems are increasingly complex. They need to meet advanced requirements for mission-critical fields with a low failure tolerance. As unexpected failures during the designed lifespan of a system may lead to catastrophic consequences, their reliability modeling and assessment are of utmost importance. The reliability modeling should achieve the assessment at a reasonable confidence level to help decision-makers arrive at sound decisions in practice. ...

Submit Paper » Fuel cells are a vital component of renewable energy warranting significant consideration and ever-increasing applications. With the increase in the application of these systems, the need for safety analysis also increases. These systems can fail due to several reasons that can result in economic losses and catastrophes. Increasing the life expectancy of fuel cells is an important aspect that needs substantial attention. Hence, to avoid sudden failures and achieve better life expectancy the discovery, identification, and implementation of enhanced health indicators for effective diagnosis and prognosis is critical. ...

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. ...

Submissions for this archived call are closed. Background This Special Collection (SC) aims to provide a dedicated space for in-depth exploration and dissemination of advancements in vulnerability analysis, risk management, and uncertainty modeling. The primary goal of this SC is to feature emerging developments, which address hazards, risks and respective mitigation strategies towards resilience and sustainability of our infrastructure systems and the built environment. The contributions are supposed to provide key ideas, concepts and technologies to solve major challenges concerned with the high complexity and the multi-disciplinary character of the problems as well as with the comprehensive quantification, efficient processing and management of the involved uncertainties. 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 multifaceted fields of risk, uncertainty, and decision-making. ...

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. ...