MS Title:
Mitigating Uncertainties in Predicting the Life-cycle Performance of Aging Structures and Infrastructure Systems Using Observational Information

Description:
Civil structures and infrastructure systems are inherently characterized with uncertainties in terms of material properties, environmental stressors and deterioration rates, which prevent decision-makers from accurately predicting the lifecycle performance of aging structures and making sound management strategies. For example, developments in structural health monitoring techniques have made available a huge amount of test data collected by a variety of sensors. While the increasing diversity of test data provides more information on structural states, it remains a challenging task to take full advantage of the mass data to mitigate the uncertainties involved in the structural performance prediction, so as to provide references to the decision-makings of structural management strategies. This mini-symposium aims to address the emerging methods and successful applications that use observational information from various sources to mitigate the uncertainties in predicting the life-cycle performance of aging structures and infrastructure systems. This mini-symposium encourages contributions related to the following topics, while other pertinent topics are also welcome.

•     Probabilistic inference methods to mitigate uncertainties in predicting the life- cycle performance of aging structures using observational information
•     Data fusion algorithms to mitigate the uncertainties in structural identification using heterogeneous test data
•     Application  of  leveraging  observational  information  to  aid  decision-making  in structural management strategy
•     Application  of  machine  learning  and  transfer  learning  methods  for  structural identification
   

Session Chairs:
Siyi Jia, Waseda University, Tokyo, Japan. E-mail:syjia@aoni.waseda.jp
Mitsuyoshi  Akiyama, Waseda University,  Tokyo, Japan. E-mail: akiyama617@waseda.jp
Dan M. Frangopol, Lehigh University, Bethlehem, USA. E-mail: dmf206@lehigh.edu