The Effects of Headcut and Knickpoint Propagation on Bridges in Iowa, TR-541, 2008

The overarching goal of the proposed research was to provide a predictive tool for knickpoint propagation within the HCA (Hungry Canyon Alliance) territory. Knickpoints threaten the stability of bridge structures in Western Iowa. The study involved detailed field investigations over two years in order to monitor the upstream migration of a knickpoint on Mud Creek in Mills County, IA and identify the key mechanisms triggering knickpoint propagation. A state-of-the-art laser level system mounted on a movable truss provided continuous measurements of the knickpoint front for different flow conditions. A pressure transducer found in proximity of the truss provided simultaneous measurements of the flow depth. The laser and pressure transducer measurements led to the identification of the conditions at which the knickpoint migration commences. It was suggested that negative pressures developed by the reverse roller flow near the toe of the knickpoint face triggered undercutting of the knickpoint at this location. The pressure differential between the negative pressure and the atmospheric pressure also draws the impinging jet closer to the knickpoint face producing scour. In addition, the pressure differential may induce suction of sediment from the face. Other contributing factors include slump failure, seepage effects, and local fluvial erosion due to the exerted fluid shear. The prevailing flow conditions and soil information along with the channel cross-sectional geometry and gradient were used as inputs to a transcritical, one dimensional, hydraulic/geomorphic numerical model, which was used to map the flow characteristics and shear stress conditions near the knickpoint. Such detailed flow calculations do not exist in the published literature. The coupling of field and modeling work resulted in the development of a blueprint methodology, which can be adopted in different parts of the country for evaluating knickpoint evolution. This information will assist local government agencies in better understanding the principal factors that cause knickpoint propagation and help estimate the needed response time to control the propagation of a knickpoint after one has been identified.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.