Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Multi-Objective DIRECT Algorithm Towards Structural Damage
  Identification with Limited Dynamic Response Information   A major challenge in Structural Health Monitoring (SHM) is to accurately
identify both the location and severity of damage using the dynamic response
information acquired. While in theory the vibration-based and impedance-based
methods may facilitate damage identification with the assistance of a credible
baseline finite element model since the changes of stationary wave responses
are used in these methods, the response information is generally limited and
the measurements may be heterogeneous, making an inverse analysis using
sensitivity matrix difficult. Aiming at fundamental advancement, in this
research we cast the damage identification problem into an optimization problem
where possible changes of finite element properties due to damage occurrence
are treated as unknowns. We employ the multiple damage location assurance
criterion (MDLAC), which characterizes the relation between measurements and
predictions (under sampled elemental property changes), as the vector-form
objective function. We then develop an enhanced, multi-objective version of the
DIRECT approach to solve the optimization problem. The underlying idea of the
multi-objective DIRECT approach is to branch and bound the unknown parametric
space to converge to a set of optimal solutions. A new sampling scheme is
established, which significantly increases the efficiency in minimizing the
error between measurements and predictions. The enhanced DIRECT algorithm is
particularly suitable to solving for unknowns that are sparse, as in practical
situations structural damage affect only a small number of finite elements. A
number of test cases using vibration response information are executed to
demonstrate the effectiveness of the new approach.

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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org