MS Title:
Robustness Design and Quantification for New and Existing Structures Against Disproportional Collapse
 

Description:
Disproportional or progressive collapse is a relatively rare event, as it requires both an abnormal loading (e.g. explosions, vehicle impacts, fire and terrorist attacks) or an unforeseen event to initiate local damage and a structure that lacks adequate continuity, ductility, and redundancy to resist the spread of damage. However, significant casualties and serious losses of property may result when progressive collapse of structures occurs, such as the 1968 Ronan Point Apartment collapse in the UK, the 2001 WTC collapse in New York, and the 2021 Condominium Building collapse in Florida. Therefore, it is of paramount importance to design structures to be sufficiently robust in order to mitigate the risk of disproportional collapse. Moreover, in the framework of optimal decision making, it is necessary to assess the structural robustness in a quantitative way.

This MS aims at bringing together scientists, academics and practicing engineers addressing theoretical aspects, experimental results, numerical modeling, and practical recommendations for design against disproportional collapse for both new and existing structures. The scope of the MS covers among others: 

  • robustness and fragility modeling;
  • alternate load path modeling;
  • aging or deterioration effects, system reliability and risk analysis in relation to structural robustness;
  • design optimization and performance-based design for structural robustness. 

Both theoretical developments and applications involving different structural systems are particularly welcomed in this session.
 

Session Chairs:
Robby Caspeele, Ghent University, Ghent, Belgium. E-mail: Robby.Caspeele@UGent.be
Jianbing Chen, Tongji University, Shanghai, China. E-mail: chenjb@tongji.edu.cn
De-Cheng Feng, Southeast University, Nanjing, China, E-mail: dcfeng@seu.edu.cn
Luchuan Ding, Tongji University, Shanghai, China. E-mail: LuchuanDing@tongji.edu.cn