Part A

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