Development of a Manual Crack Quantification and Automated Crack Measurment System, Project TR-457 Final Report, 2005

Final report produced by DOT on development of manual crack quantification and automatic crack measurment system.

Feasibility Study for Detection and Quantification of Corrosion in Bridge Barrier Rails, RB11-012, 2013

"Technical challenges exist with infrastructure that can be addressed by nondestructive evaluation (NDE) methods, such as detecting corrosion damage to reinforcing steel that anchor concrete bridge railings to bridge road decks. Moisture and chloride ions reach the anchors along the cold joint between the rails and deck, causing corrosion that weakens the anchors and ultimately the barriers. The Center for Nondestructive Evaluation at Iowa State University has experience in development of measurement techniques and new sensors using a variety of interrogating energies. This research evaluated feasibility of three technologies — x-ray radiation, ground-penetrating radar (GPR), and magnetic flux leakage (MFL) — for detection and quantification of corrosion of embedded reinforcing steel. Controlled samples containing pristine reinforcing steel with and without epoxy and reinforcing steel with 25 percent and 50 percent section reduction were embedded in concrete at 2.5 in. deep for laboratory evaluation. Two of the techniques, GPR and MFL, were used in a limited field test on the Iowa Highway 210 Bridge over Interstate 35 in Story County. The methods provide useful and complementary information. GPR provides a rapid approach to identify reinforcing steel that has anomalous responses. MFL provides similar detection responses but could be optimized to provide more quantitative correlation to actual condition. Full implementation could use either GPR or MFL methods to identify areas of concern, followed by radiography to give a visual image of the actual condition, providing the final guidance for maintenance actions." The full 103 page report and the 2 page Tech Transfer Summary are included in this link.

Quantifying Uncertainty in Real Time Performance Measurement for Highway Winter Maintenance Operations – Phase 2, RB03-013, 2014

Winter weather in Iowa is often unpredictable and can have an adverse impact on traffic flow. The Iowa Department of Transportation (Iowa DOT) attempts to lessen the impact of winter weather events on traffic speeds with various proactive maintenance operations. In order to assess the performance of these maintenance operations, it would be beneficial to develop a model for expected speed reduction based on weather variables and normal maintenance schedules. Such a model would allow the Iowa DOT to identify situations in which speed reductions were much greater than or less than would be expected for a given set of storm conditions, and make modifications to improve efficiency and effectiveness. The objective of this work was to predict speed changes relative to baseline speed under normal conditions, based on nominal maintenance schedules and winter weather covariates (snow type, temperature, and wind speed), as measured by roadside weather stations. This allows for an assessment of the impact of winter weather covariates on traffic speed changes, and estimation of the effect of regular maintenance passes. The researchers chose events from Adair County, Iowa and fit a linear model incorporating the covariates mentioned previously. A Bayesian analysis was conducted to estimate the values of the parameters of this model. Specifically, the analysis produces a distribution for the parameter value that represents the impact of maintenance on traffic speeds. The effect of maintenance is not a constant, but rather a value that the researchers have some uncertainty about and this distribution represents what they know about the effects of maintenance. Similarly, examinations of the distributions for the effects of winter weather covariates are possible. Plots of observed and expected traffic speed changes allow a visual assessment of the model fit. Future work involves expanding this model to incorporate many events at multiple locations. This would allow for assessment of the impact of winter weather maintenance across various situations, and eventually identify locations and times in which maintenance could be improved.