Using the Air Void Anal Analyzer for Real-Time Quality Control Adjustments in the Field, November 2004

The air void analyzer (AVA) with its independent isolation base can be used to accurately evaluate the air void system—including volume of entrained air, size of air voids, and distribution of air voids—of fresh portland cement concrete (PCC) on the jobsite. With this information, quality control adjustments in concrete batching can be made in real time to improve the air void system and thus increase freeze-thaw durability. This technology offers many advantages over current practices for evaluating air in concrete.

Linking Real Time and Location in Scheduling Demand-Responsive Transit, September 1996

The role of rural demand-responsive transit is changing, and with that change is coming an increasing need for technology. As long as rural transit was limited to a type of social service transportation for a specific set of clients who primarily traveled in groups to common meal sites, work centers for the disabled, or clinics in larger communities, a preset calendar augmented by notes on a yellow legal pad was sufficient to develop schedules. Any individual trips were arranged at least 24 to 48 hours ahead of time and were carefully scheduled the night before in half-hour or twenty-minute windows by a dispatcher who knew every lane in the service area. Since it took hours to build the schedule, any last-minute changes could wreak havoc with the plans and raise the stress level in the dispatch office. Nevertheless, given these parameters, a manual scheduling system worked for a small demand-responsive operation.

Using Scanning Lasers for Real-Time Pavement Thickness Measurement, TR-538, 2006

Due to limited budgets and reduced inspection staff, state departments of transportation (DOTs) are in need of innovative approaches for providing more efficient quality assurance on concrete paving projects. The goal of this research was to investigate and test new methods that can determine pavement thickness in real time. Three methods were evaluated: laser scanning, ultrasonic sensors, and eddy current sensors. Laser scanning, which scans the surface of the base prior to paving and then scans the surface after paving, can determine the thickness at any point. Also, scanning lasers provide thorough data coverage that can be used to calculate thickness variance accurately and identify any areas where the thickness is below tolerance. Ultrasonic and eddy current sensors also have the potential to measure thickness nondestructively at discrete points and may result in an easier method of obtaining thickness. There appear to be two viable approaches for measuring concrete pavement thickness during the paving operation: laser scanning and eddy current sensors. Laser scanning has proved to be a reliable technique in terms of its ability to provide virtual core thickness with low variability. Research is still required to develop a prototype system that integrates point cloud data from two scanners. Eddy current sensors have also proved to be a suitable alternative, and are probably closer to field implementation than the laser scanning approach. As a next step for this research project, it is suggested that a pavement thickness measuring device using eddy current sensors be created, which would involve both a handheld and paver-mounted version of the device.

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.