Part A

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. ...

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. ...

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.

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 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 Collection on Extreme Damage Mechanics for Lifecycle Fatigue Resilience of Infrastructure Systems. Click to download the CFP Guest Editors Xuhong Zhou, Chongqing University, zxh@cqu.edu.cn Yongtao Bai, Chongqing University, bai.yongtao@cqu.edu.cn Frédéric Ragueneau, Paris‐Saclay University, frederic.ragueneau@ens‐paris‐saclay.fr Julio Florez‐Lopez, Chongqing University, j.florezlopez@cqu.edu.cn Aims & Scope This Special Collection aims to gather prestigious contributions presenting the state‐of‐the‐art breakthroughs on extreme damage mechanicsfor the lifecycle fatigue resilience of infrastructure systems. Since the 19th century, when the use of steels in civil engineering began to increase, it has been recognized that structural components and systems subjected to repetitive load cycles may fail in service life. This type of failure is well known as “fatigue” due to the formation and propagation of crack damages caused by repeated stress or strain fluctuations. It has been estimated that nearly 90% of the failures can be attributed to fatigue. For instance, bridges and wind turbines subjected to fluctuating live loads may be damaged due to high cycle fatigue. On the other hand, low cycle fatigue is usually characterized by large amplitude and low‐frequency plastic strains such as seismic actions on skyscrapers. Depending on uncertainties of the loading reversal, amplitude/intensity, and occurrence frequency in lifecycle, we should generally couple the probability methodology with computational damage mechanics for risk assessment of large‐scale infrastructure systems. Furthermore, for the goal of “emission peak and carbon neutrality”, there is a demand to develop resilient,sustainable, and long lifecycle infrastructure. To this aim, novel mathematical and computational approaches based on the probability theory, damage and fracture mechanics are needed in the broad topics of lifecycle fatigue assessment of steel and composite structural systems. This challenging aim might today be able to realize with the implementation of valuable data availability, uncertainty quantification, and artificial intelligence technologies.

Please find attached the general Call for Papers for Part A: Civil Engineering. Click to download the CFP