Prediction of deterioration in structural systems is associated with
large uncertainties. Inspections can reduce these uncertainties and support the
planning of measures to ensure the integrity of structural assets, but inspections
are costly and should be optimized. Past research and applications of risk-based
inspection planning have treated sequentially the questions “when to inspect?”
and “where to inspect?” to limit the computational cost of optimizing inspection
plans. To optimize inspections in larger structural systems, we develop a
methodology that accounts for component interactions and interdependence such
as stochastic dependence in deterioration processes at different locations, structural
interactions and progressive damage evolution. The methodology involves
a hierarchical dynamic Bayesian network to compute the updated system reliability
with component inspection results. The optimization utilizes a heuristic
for defining inspection strategies at the system level. In particular, component
inspections are prioritized based on their value of information (VoI). We investigate
heuristics that combine component characteristics which are closely linked
to the VoI. We define a Prioritization Index and study its effect for different combinations
of component structural importance, uncertainty and correlation. For
numerical investigations, the methodology is applied to an idealized steel structure
subject to fatigue deterioration.
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Prediction of deterioration in structural systems is associated with
large uncertainties. Inspections can reduce these uncertainties and support the
planning of measures to ensure the integrity of structural assets, but inspections
are costly and should be optimized. Past research and applications of risk-based
inspection planning have treated sequentially the questions “when to inspect?”
and “where to inspect?” to limit the computational cost of optimizing inspection
plans. To optimize in...
»
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