Please find attached the Call for Papers for the Special Collection on Extreme Damage Mechanics for Lifecycle Fatigue Resilience of Infrastructure Systems.
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.