Statistical Data Analysis of the Aging Effect on Time-Dependent Corrosion Rate for Steel Girder Bridges with Concrete Decks

Abstract

Significant research has been conducted on the practice of bridge asset management (BAM). Although bridge site inspections yield observations on current conditions, their usefulness in a bridge asset management strategy requires a sound historical and statistical basis. Therefore, the present practice of bridge site inspection can only support a reactive mode in bridge asset life cycle management and is raising a critical need for a risk analysis methodology that will effectively support decision making in a forecasting mode. This research addressed that challenge by employing a series of relatively simple, well-established statistical techniques as a precursor to a forecasting methodology. A knowledge-based risk assessment method (RAM) is recommended for optimizing BAM of the New York City Department of Transportation (NYCDOT) bridge database. The limitations of the current practice of bridge condition rating are reactive in nature. Reactive approaches do not provide analytical tools for forecasting the aging effect on the time-dependent bridge infrastructure deterioration for the prioritization of BAM. The development of statistical data analysis can be done by using the aging effect on the corrosion rate as a health monitoring parameter of the girder. The results of this research provide bridge asset managers with a statistical data-driven analysis approach to the time-dependent aging effect as a decision support tool for preemptive prioritization and a prespecified vulnerability level in BAM planning. The infrastructure base selected for this study was 92 steel girder bridges with concrete decks (single-span/multispan) managed by NYCDOT, which were clustered into eight major age groups. There were multiple primary member degradation indicators, e.g., corrosion, section loss, cracks, and others. This research analyzed the corrosion rate of primary members. It illustrated the application of statistical data analysis for the development and application of RAM for forecasting the effect of aging on the corrosion rate as a structural degradation indicator for the preemptive planning of a BAM strategy.