Effect of Mix Times on PCC Properties, HR-1066, 1998

The objectives of this research were the collection and evaluation of the data pertaining to the importance of concrete mixing time on air content and distribution, consolidation and workability for pavement construction. American Society for Testing and Materials (ASTM) standard C 94 was used to determine the significance of the mixing time on the consistency of the mix being delivered and placed on grade. Measurements of unit weight, slump, air content, retained coarse aggregate and compressive strength were used to compare the consistency of the mix in the hauling unit at the point of mixing and at the point placement. An analysis of variance was performed on the data collected from the field tests. Results were used to establish the relationship between selected mixing time and the portland cement concrete properties tested. The results were also used to define the effect of testing location (center and side of truck, and on the grade) on the concrete properties. Compressive strength test concepts were used to analyze the hardened concrete pavement strength. Cores were obtained at various locations on each project on or between vibrator locations to evaluate the variance in each sample, between locations, and mixing times. A low-vacuum scanning electron microscope (SEM) was used to study air void parameters in the concrete cores. Combining the data from these analysis thickness measurements and ride in Iowa will provide a foundation for the formulation of a performance based matrix. Analysis of the air voids in the hardened concrete provides a description of the dispersion of the cemtitious materials (specifically flyash) and air void characteristics in the pavement. Air void characteristics measured included size, shape and distribution.

Evaluating the Relationship between the Driver and Roadway to Address Rural Intersection Safety using the SHRP 2 Naturalistic Driving Study Data, RB05-013, 2016

Rural intersections account for 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Transportation agencies have traditionally implemented countermeasures to address rural intersection crashes but frequently do not understand the dynamic interaction between the driver and roadway and the driver factors leading to these types of crashes. The Second Strategic Highway Research Program (SHRP 2) conducted a large-scale naturalistic driving study (NDS) using instrumented vehicles. The study has provided a significant amount of on-road driving data for a range of drivers. The present study utilizes the SHRP 2 NDS data as well as SHRP 2 Roadway Information Database (RID) data to observe driver behavior at rural intersections first hand using video, vehicle kinematics, and roadway data to determine how roadway, driver, environmental, and vehicle factors interact to affect driver safety at rural intersections. A model of driver braking behavior was developed using a dataset of vehicle activity traces for several rural stop-controlled intersections. The model was developed using the point at which a driver reacts to the upcoming intersection by initiating braking as its dependent variable, with the driver’s age, type and direction of turning movement, and countermeasure presence as independent variables. Countermeasures such as on-pavement signing and overhead flashing beacons were found to increase the braking point distance, a finding that provides insight into the countermeasures’ effect on safety at rural intersections. The results of this model can lead to better roadway design, more informed selection of traffic control and countermeasures, and targeted information that can inform policy decisions. Additionally, a model of gap acceptance was attempted but was ultimately not developed due to the small size of the dataset. However, a protocol for data reduction for a gap acceptance model was determined. This protocol can be utilized in future studies to develop a gap acceptance model that would provide additional insight into the roadway, vehicle, environmental, and driver factors that play a role in whether a driver accepts or rejects a gap.