Calls for Papers

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

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 Submissions for this archived call are closed. 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.

Please find attached the Call for Papers for the Special Collection on New Technologies in Risk Assessment of Maritime Transport. Click to download the CFP Guest Editors Qing Yu, Jimei University, China, qing.yu@jmu.edu.cn Jakub Montewka, Gdansk University of Technology, jakub.montewka@pg.edu.pl Floris Goerlandt, Dalhousie University, Canada, floris.goerlandt@dal.ca Chengpeng Wan, Wuhan University of Technology, Wuhan, China, cpwan@whut.edu.cn Zhisen Yang, Shenzhen Technology University, Shenzhen, China, yangzhisen@sztu.edu.cn Zaili Yang, Liverpool John Moores University, UK, z.yang@ljmu.ac.uk Aims & Scope Motivated by the transition of trading demands in context of ongoing economic developments, the shipping industry is of rising importance from both national and international perspectives. However, maritime transport still suffers various risks due to emerging technological development (e.g., autonomous ships), new hazards/threats (e.g., climate change, cybersecurity, and COVID-19), foci evolution from local to network levels (e.g., impact of Suez Canal blockage to supply chains), and new and emergent transportation routes (e.g. Arctic shipping). The continued need for focus on maritime risks is evident also from several accidents which have occurred in the past years. Although various studies have been conducted in assessing risks associated with marine systems, remaining challenges involve comprehensive maritime risk modelling in the abovementioned emerging aspects. This requires focused attention and continued work in the academic field, as only limited research can be found in the relevant literature. ...

Please find attached the Call for Papers for the Special Issue on Probabilistic Digital Twins in Additive Manufacturing. Click to download the CFP Guest Editors Zequn Wang, Assistant Professor, Michigan Technological University, USA, zequnw@mtu.edu Zhen Hu, Assistant Professor, University of Michigan-Dearborn, USA, zhennhu@umich.edu Moon Seung Ki, Associate Professor, Nanyang Technological University, Singapore, skmoon@ntu.edu.sg Hong-Zhong Huang, Professor, University of Electronic Science and Technology of China, China, hzhuang@uestc.edu.cn Qi Zhou, Associate Professor, Huazhong University of Science and Technology, China, qizhou@hust.edu.cn Aims & Scope Additive manufacturing (AM) has made enormous progress over the past decade, as it is capable of producing complex parts with significantly less fabrication constraints compared to existing manufacturing technologies over a broad dimensional scale. Complicated AM process variability is one of the greatest obstacles in performance evaluation and quality control of additively manufactured materials and products, and thus hinders widespread implementation of AM techniques. Digital twin, as a digital replica of a production system or process, has great potential in overcoming quality variability and reliability issues in AM processes. With the development of probabilistic digital twins in AM and uncertainty management techniques, it becomes possible to reduce the computational burden for multi-scale modeling and realize reliable AM processes by taking advantage of large volumes of in situ sensor data to optimize process parameters, detect, and prevent process faults.

Please find attached the Call for Papers for the Special Issue on Modeling and Analysis of Inspection Uncertainties in Structural Health Monitoring. Click to download the CFP Guest Editors Yi Zhang, Associate Professor, School of Civil Engineering, Tsinghua University, China, zhang-yi@tsinghua.edu.cn Chul-Woo Kim, Professor, Department of Civil and Earth Resources Engineering, Kyoto University, Japan, kim.chulwoo.5u@kyoto-u.ac.jp Yan-Gang Zhao, Professor, Department of Architecture, Kanagawa University, Japan, zhao@kanagawa-u.ac.jp Pei-Pei Li, Postdoctoral Research Fellow, School of Civil Engineering, Tsinghua University, China, lipeipei626@gmail.com Aims & Scope In recent years, structural health monitoring (SHM) technology has developed rapidly and is now gradually applied to civil, mechanical, automobile, and aerospace engineering practices. One of the most widely-held concerns is to utilize useful information provided by SHM for quantitative assessment of structural health and updating structural models. To accomplish this, inspection or measurement information is gathered through monitoring, followed by a thorough analysis that uses finite element analysis models, mathematical statistics tools, artificial intelligence technologies, or other advanced methods to obtain an objective quantitative assessment of structural health or updating structural models. However, due to uncertainties in mathematical modeling and analysis in SHM, objectives for satisfactory results of the quantitative structural health assessment and structural model updating have not been fully realized in real-world conditions, and many problems still require further research.

Please find attached the Call for Papers for the Special Issue on Digital Twins: A New Frontier in Critical Infrastructure Protection and Resilience. Click to download the CFP Update: Submissions are open until January 31, 2023. Guest Editors Nii Attoh-Okine, PhD, University of Delaware, USA, okine@udel.edu Yaw Adu-Gyamfi, PhD, University of Missouri, USA, adugyamfiy@missouri.edu Aims & Scope A digital twin is a computational model (or set of coupled) that evolves over time to persistently represent the critical structure, its components, system or process. Digital twin underpins intelligent automation by supporting data-driven decision making and enabling asset specific analysis and system behavior. Within the contexts of critical Infrastructure systems, the digital twins represent the flow of information among connected platforms. In the future, as many agencies turn to digital twin capabilities, they have to migrate towards continuous real-time performance models and calibrate by pairing data from real-time sensors, meters, weather, and other data. The digital twin can be used to run “what-if” scenarios, predict and prevent failures, provide early alerts of anomalies and conduct predictive analysis. The strength of a digital twin is the interconnectivity of data and models. The main characteristics of a digital twin are ...

Please find attached the Call for Papers for the Special Issue on Uncertainty Quantification & Management in Nonlinear Dynamical Systems in Aerospace and Mechanical Engineering. Click to download the CFP Guest Editors Jie Yuan, University of Strathclyde, United Kingdom, jie.yuan@strath.ac.uk Jinglang Feng, University of Strathclyde, United Kingdom, jinglang.feng@strath.ac.uk Enora Denimal, INRIA Institut National de Recherche en Informatique et en Automatique, France, enora.denimal@inria.fr Quan Hu, Beijing Institute of Technology, China, huquan2690@bit.edu.cn Sifeng Bi, University of Strathclyde, United Kingdom, sifeng.bi@strath.ac.uk Alice Cicirello, Delft University of Technology, The Netherlands, A.Cicirello@tudelft.nl Aims & Scope The study of aerospace systems is becoming an increasingly critical challenge due to the presence of a wide range of nonlinearities (such as large structural deformations, joints, fluid-structure interaction, electro-mechanical interaction, etc.), and rigorous requirements of their efficiency and reliability to achieve net zero targets. Moreover, spacecraft dynamics also experience strong nonlinearities due to the perturbation forces (such as non-sphericity of Earth, atmospheric drag, solar radiation pressure, etc.), which make the spacecraft motion very sensitive to the initial state and these unmodeled forces. Therefore, it is important to characterize these uncertainties and quantify their influences on dynamical performance for improved design and analysis, and knowledge contributions in the identification of these nonlinear systems and their high sensitivity to dynamical behaviors. This Special Issue seeks submissions related to the modeling, quantification, and management of uncertainties and nonlinearities for the design, navigation, control, and identification of aerospace systems.

Please find attached the Call for Papers for the Special Collection Advances in Efficient Methods in Random Fields Modeling and Analysis. Click to download the CFP Guest Editors Zhenhao Zhang, Changsha University of Science & Technology, zhangzhenhao@csust.edu.cn De-Cheng Feng, Southeast University, dcfeng@seu.edu.cn You Dong, The Hong Kong Polytechnic University, you.dong@polyu.edu.hk Emilio Bastidas-Arteaga, La Rochelle University, ebastida@univ-lr.fr Aims & Scope Spatial and temporal variability widely exists in practical engineering and has a significant influence on structural performance. Generally, it is modeled by the random field/process methods which typically transfer the field into a set of random variables, then it can be implemented in conventional uncertainty analysis framework. Efficient random field modeling and analysis usually involves three aspects, the adopted mathematical representation method, the accurate reflection of the geometric correlations, and the effective sampling of the discretized random variables. With the development of probabilistic mechanics and random process theory, novel methods are developed for efficient random field modeling and convenient uncertainty analysis of structures involving random field properties. Besides, the AI-inspired data-driven approaches bring new insights for resolving the traditional difficulties of random field analysis, e.g., correlation relation identification, surrogate models, dimension reduction methods, etc. This special collection aims to gather contributions presenting the recent advances in efficient random field modeling, analysis, and applications.

Please find attached the Call for Papers for the Special Issue on Community Resilience to Disruptive Events: Models and Analyses, Lessons Learned, and Case Studies. Click to download the CFP Guest Editors Cao Wang, University of Wollongong, Australia, wangc@uow.edu.au Matthias G.R. Faes, TU Dortmund University, Germany, matthias.faes@tu-dortmund.de Michael Beer, Leibniz University Hannover, Germany, beer@irz.uni-hannover.de Enrico Zio, Politecnico di Milano, Italy, enrico.zio@polimi.it John W. van de Lindt, Colorado State University, USA, jwv@engr.colostate.edu Aims & Scope Many types of disruptive events, such as earthquakes, tropical cyclones, floods, wildfires, and remarkably the coronavirus (COVID-19) pandemic, have threatened communities around the world with dramatic consequences. With respect to this, society is asking justified questions: how resilient is our community against disruptive events? How can we use resilience approaches to counteract disruptive events? What lessons can we learn from real-world practices to enhance the resilience of our community? This Special Issue is aimed at gathering contributions of methods, lessons, and practices useful to achieve community resilience in the face of disruptive events. Papers discussing the impacts of disruptive events on a community’s resilience in terms of functionality loss and recovery process, uncertainty quantification in community resilience modeling, resilience approaches to counteract impacts of disruptive events, and critical lessons learned from existing practices towards enhancing community resilience are all solicited.