  This paper proposes a multitask learning framework for probabilistic model
updating by jointly using strain and acceleration measurements. This framework
can enhance the structural damage assessment and response prediction of
existing steel frame structures with quantified uncertainty. Multitask learning
may be used to address multiple similar inference tasks simultaneously to
achieve a more robust prediction performance by transferring useful knowledge
from one task to another, even in situations of data scarcity. In the proposed
model-updating procedure, a spatial frame is decomposed into multiple planar
frames that are viewed as multiple tasks and jointly analyzed based on the
hierarchical Bayesian model, leading to robust estimation results. The
procedure uses a displacement-stress relationship in the modal space because it
directly reflects the elemental stiffness and requires no prior knowledge
concerning the mass, unlike most existing model-updating techniques. Validation
of the proposed framework by using a full-scale vibration test on a one-story,
one-bay by one-bay moment resisting steel frame, wherein structural damage to
the column bases is simulated by loosening the anchor bolts, is presented. The
experimental results suggest that the displacement-stress relationship has
sufficient sensitivity toward localized damage, and the Bayesian multitask
learning approach may result in the efficient use of measurements such that the
uncertainty involved in model parameter estimation is reduced. The proposed
framework facilitates more robust and informative model updating.
