Monitoring onsite‐temperature prediction error for condition monitoring of civil infrastructures

نویسندگان

چکیده

An inverse input–output method is proposed for long-term condition monitoring of civil infrastructures through the prediction error air temperature recorded at site a structure. It known that structural natural frequencies are affected by temperature. Hence, considers as input and output to train machine learning algorithm (MLA). To this end, after signal preprocessing using variational mode decomposition (VMD), different MLAs employed, associated with regarded damage–sensitive feature. hypothesised further confirmed solving numerical benchmark problems deviates significantly from upper bond control limit an R-chart (errors signal) constructed based on soon damage occurs. The frequency–temperature scatter plots indicate linear dependency between Moreover, similar slope obtained regression line fitted indicates high collinearity among pairs frequencies. This observation implies interaction term must be considered such in model. results both experimental studies confirm model most accurate problem predicting structures. also compared direct strategy, whereby its superiority demonstrated.

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ژورنال

عنوان ژورنال: Structural control & health monitoring

سال: 2022

ISSN: ['1545-2263', '1545-2255']

DOI: https://doi.org/10.1002/stc.3112