Mechanistic Deterioration Modeling for Bridge Design and Management

The combination of continued highway bridge aging and constrained maintenance, repair, and replacement budgets create the pressing need to develop strategies to allocate available management resources most effectively. Strategies for preventative maintenance scheduling and budget projection greatly depend on the ability to predict bridge deterioration. Mechanistic deterioration models, which analyze the physical processes causing deterioration, have the potential to supplement purely statistical deterioration models and to address limitations associated with bridge inspection data and statistical methods. A variety of mechanistic models that consider specific aspects of deterioration processes is available in the literature. This study considered how existing mechanistic models for predicting corrosion-induced cracking of RC bridge decks can be assembled into a comprehensive model that can predict the complete deck service life and be used to propose preventative maintenance schemes for the decks. The construction practices of modern RC bridge decks were investigated and an attempt was made to assemble a mechanistic model, including the effects of epoxy-coated rebar, waterproofing membranes, asphalt overlays, joint deterioration, and deck maintenance. In some cases, new temporary models had to be proposed to fill gaps in existing modeling capabilities. After a complete model was assembled, Monte Carlo simulation with probabilistic model inputs was applied to simulate the inherent randomness associated with deterioration. The results of this effort indicated that mechanistic models are, indeed, promising, and well-timed preventative maintenance may provide longer bridge deck service life with fewer total maintenance actions than current methods do. However, experimental studies of specific deterioration processes and additional bridge data collection are needed to supplement existing models and validate model predictions.


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  • Accession Number: 01664711
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Mar 9 2018 3:01PM