Submit Paper » Background The reliability of civil engineering structures is paramount for sustainable and resilient infrastructure. Ensuring robust behavior, particularly in the face of extreme events, is crucial for longevity and adaptability. This Special Collection focuses on a pivotal aspect of structural resilience: the control of vibrations, specifically addressing uncertainties. Scholars are invited to contribute original research papers exploring the nuanced interplay between vibration control and broader resilient civil engineering structures. This thematic issue serves as a guide for risk and reliability analysis, emphasizing the vital role of vibration control devices in reinforcing stability amidst uncertainty. ...

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 Understanding the data and reaching accurate conclusions are of paramount importance in the present era of Big Data. Machine learning has been widely used in academia and industry to analyze voluminous and intricate datasets to uncover hidden patterns and reach incisive insights. Whilst machine learning approaches have extraordinary potential and are increasingly employed to aid in various complicated tasks, their results are not wholly reliable due to the challenges introduced by data uncertainty (aleatory uncertainty) and model uncertainty (epistemic uncertainty). It is essential to accommodate uncertainties and provide uncertainty estimates to uncover beneficial information for a better decision-making process. To this end, the development and application of novel uncertainty quantification methods in tandem with different machine-learning-enhanced techniques are crucial to yield useful information and amplify the interpretability and reliability of the results. With this in mind, this SC will gather contributions presenting state-of-the-art breakthroughs in uncertainty quantification for machine learning. ...

Submit Paper » In recent decades, fault diagnostics and failure prognostics have demonstrated their great potential for health monitoring and risk management of complex engineering systems, including smart factories, power plants, space systems, and heavy equipment. The credibility and applicability of fault diagnostics and failure prognostics, however, are significantly affected by various uncertainties, such as model uncertainty, data uncertainty, process uncertainty, environmental uncertainty, and the inherent uncertainty of engineered systems. Therefore, accurately quantifying the effects of these uncertainties is essential and one of the most widely-held concerns to ensure trustworthy decision-making based on diagnostic and prognostic results. The purpose of this special issue is to present the latest advancements in the field of uncertainty-aware diagnostics and prognostics for the health management of engineered systems. ...

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. ...

Hi there, here is the current table of content. Click to download the PDF 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. ...

Hi there, here is the current newsletter. Click to download the PDF 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. ...

Hi there, here is the current table of content. Click to download the PDF 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. ...

Please find attached the Call for Papers for the Special Collection on Non-Deterministic Model Updating and Structural Health Monitoring for Existing Structures. Click to download the CFP Submit Paper » Guest Editors Masaru Kitahara, Assistant Professor, Department of Civil Engineering, The University of Tokyo, kitahara@bridge.t.u-tokyo.ac.jp Sifeng Bi, Lecturer, Department of Mechanical and Aerospace Engineering, University of Strathclyde, sifeng.bi@strath.ac.uk Matteo Broggi, Deputy Head, Institute for Risk and Reliability, Leibniz University Hannover, broggi@irz.uni-hannover.de Takayuki Shuku, Associate Professor, Architecture, Civil Engineering and Environmental Management Program, Okayama University, shuku@cc.okayama-u.ac.jp Aims & Scope This Special Collection (SC) aims to gather contributions presenting the state-of-the-art on uncertainty analysis in model updating and structural health monitoring (SHM) for existing structures. Over the past few decades, civil infrastructures have been aging in many countries, and more and more infrastructures are being assessed as structurally deficient. Such structural deficiencies in key infrastructures come with massive consequences such as structural failures and even human deaths. The development of a framework for the safe operation and maintenance of infrastructures is thus required. To this end, SHM has attracted increasing attention in recent years, aiming at condition assessment and service life monitoring of existing structures, often on the basis of structural vibration data. SHM strategies can be mainly classified into two categories, i.e., model-based and data-driven methods. Model-based SHM employs physics-based models in combination with inverse analysis techniques to infer a set of model parameters, such that the best possible fit is gained between model outputs and measurements. This approach is generally referred to as model updating. Data-driven SHM, on the other hand, only exploits the monitoring data without use of physics-based models to infer structural condition. This approach is often rooted in signal processing, pattern recognition or machine learning techniques. Regardless of whether model-based or data-driven approach is used, uncertainties are practically inevitable in both the measuring and modeling processes due to very limited number of sensors, variation in environmental and operational conditions, simplification and approximation of complex physical behavior, and so on. Uncertainties may cause large deviations in model updating and SHM results and thus need to be appropriately dealt with by non-deterministic approaches, i.e., either probabilistic or non-probabilistic approaches. Considering the above issues, this SC reports the latest advances and challenges related to uncertainty analysis in model updating and SHM, encompassing not only the theoretical and computational aspects but also the practical and application aspects, especially for large-scale civil infrastructures. The concept of model updating and health monitoring have been widely accepted and used in many different fields such as geotechnical engineering, and the scope of this SC is not limited to structural engineering. ...

Please find attached the Call for Papers for the Special Collection on Resilience of Power Infrastructure System. Click to download the CFP Guest Editors Wei Zhang, Associate Professor, University of Connecticut, wzhang@uconn.edu Ge (Gaby) Ou, Assistant Professor, University of Florida, gaby.ou@essie.ufl.edu Youngjib Ham, Associate Professor, Texas A&M University, yham@tamu.edu Zongjie Wang, Assistant Professor, University of Connecticut, zongjie.wang@uconn.edu Aims & Scope Extreme weather events, such as hurricanes, droughts, and flooding, are expected to be more “common” under a more variable climate system. With threats from stronger hurricanes, wildfires, snowstorms, etc., power infrastructure systems are experiencing critical threats, leading to many community residents and industrial facilities without power for days, weeks or longer. With the interdependency with other infrastructure systems, such as the communication, water, and transportation systems, the damages or failures of critical components of power infrastructure system could potentially create cascading effects and create disastrous damages to communities, which might take years to recover. The main objective of this special collection is to bring together researchers working on different aspects of the resilience of power infrastructure systems. State-of- the-art knowledge and expertise from the researcher, engineers, operators, and owners are expected to be synthesized to enhance the resilience of power infrastructure systems when confronting extreme weather events in their life cycles. ...