NSERC Discovery Grants (2009-2014) : Towards risk-informed infrastructure engineering — Development of risk-informed in-service inspection (RI-ISI) methodology for Canadian nuclear industry.

Traditional in-service inspection programs for nuclear power plants were established mainly based on deterministic rules using engineering judgment and insights specific to these systems. Industrial experience has shown that considerable inspection effort was inefficiently spent at locations where few flaws were found, while degradation mechanisms not anticipated by the inspection programs, such as stress corrosion cracking and flow accelerated corrosion, caused damage at locations that were not inspected. The major objective of the awarded NSERC research program is to develop an advanced risk-informed in-service inspection methodology for pressure-retaining components in nuclear power plant.  Particularly, stochastic modeling methodology is to be developed that systematically addresses uncertainties associated with the degradation data obtained from in-service inspections.  Then the degradation-based life prediction will be benchmarked against with the statistical failure time data analysis.  Finally, an advanced inspection and maintenance model that provides optimal strategies for the life-cycle management of nuclear power plant components will be developed.

Ministry of Transportation Ontario, Highway Infrastructure Innovation Funding Program (2010-2011): Development of Database for Local Calibration of MEPDG Distress Models

The MEPDG is a pavement design tool developed by the American Association of State Highway and Transportation Officials (AASHTO) jointly with the National Cooperative Highway Research Program (NCHRP). The design guide represents a major change and paradigm shift from existing empirical design procedures, both in design approach and in design inputs. The design guide contains a number of mechanistic distress models that require careful calibration to reflect local experience. The purpose of the calibration is to establish transfer functions relating pavement responses (stresses, strains, and deflections) to specific forms of physical distresses, which mainly includes permanent deformation, cracking, and roughness. The national calibration-validation process has been completed in the United States using the LTPP database. Although this effort was very comprehensive, the design guide requires further local calibration be conducted before its implementation to confirm that the national calibration factors or functions are adequate for the construction, materials, climate, traffic, and other conditions that are encountered within a provincial highway system.

The major objective of this project, under the Highway Infrastructure Innovation Funding Program (HIIFP), is to develop a pavement distress database for the calibration of the distress models in the mechanistic-empirical pavement design guide (M-E PDG) to reflect the experience of the Ministry of Transportation of Ontario (MTO).

Ministry of Transportation Ontario, Highway Infrastructure Innovation Funding Program (2009 – 2010): Verification and validation of pavement deterioration models based on long-term evaluation of in-service pavements

Based on the long-term field performance evaluation data of in-service pavements, the pavement deterioration models used in the currently MTO pavement management system (PMS2) are verified and updated. It is found that the existing sigmoidal model is flexible enough to characterize the different degradation trend and pattern, and therefore there is no practical need to change the functional form of the model. Moreover, the existing model provides very good fit to individual section-cycle profiles of deterioration. However, no statistically significant regression relationship can be found between the deterioration trend and the pavement attributes such as structural design, traffic, subgrade soil, and environment. A new set of model parameters for the existing MTO deterioration models are updated. Effort has also been made in this project to model individual distress modes by a Markov chain process.

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