An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

Perception and Interpretation of Advance Warning Signs on County Roads, HR-256, 1984

This research consisted of five laboratory experiments designed to address the following two objectives in an integrated analysis: (1) To discriminate between the symbol Stop Ahead warning sign and a small set of other signs (which included the word-legend Stop Ahead sign); and (2) To analyze sign detection, recognizability, and processing characteristics by drivers. A set of 16 signs was used in each of three experiments. A tachistoscope was used to display each sign image to a respondent for a brief interval in a controlled viewing experiment. The first experiment was designed to test detection of a sign in the driver's visual field; the second experiment was designed to test the driver's ability to recognize a given sign in the visual field; and the third experiment was designed to test the speed and accuracy of a driver's response to each sign as a command to perform a driving action. A fourth experiment tested the meanings drivers associated with an eight-sign subset of the 16 signs used in the first three experiments. A fifth experiment required all persons to select which (if any) signs they considered to be appropriate for use on two scale model county road intersections. The conclusions are that word-legend Stop Ahead signs are more effective driver communication devices than symbol stop-ahead signs; that it is helpful to drivers to have a word plate supplementing the symbol sign if a symbol sign is used; and that the guidance in the Manual on Uniform Traffic Control Devices on the placement of advance warning signs should not supplant engineering judgment in providing proper sign communication at an intersection.

Safety Analysis of Low-Volume Rural Roads in Iowa, 2010

Iowa features an extensive surface transportation system, with more than 110,000 miles of roadway, most of which is under the jurisdiction of local agencies. Given that Iowa is a lower-population state, most of this mileage is located in rural areas that exhibit low traffic volumes of less than 400 vehicles per day. However, these low-volume rural roads also account for about half of all recorded traffic crashes in Iowa, including a high percentage of fatal and major injury crashes. This study was undertaken to examine these crashes, identify major contributing causes, and develop low-cost strategies for reducing the incidence of these crashes. Iowa’s extensive crash and roadway system databases were utilized to obtain needed data. Using descriptive statistics, a test of proportions, and crash modeling, various classes of rural secondary roads were compared to similar state of Iowa controlled roads in crash frequency, severity, density, and rate for numerous selected factors that could contribute to crashes. The results of this study allowed the drawing of conclusions as to common contributing factors for crashes on low-volume rural roads, both paved and unpaved. Due to identified higher crash statistics, particular interest was drawn to unpaved rural roads with traffic volumes greater than 100 vehicles per day. Recommendations for addressing these crashes with low-cost mitigation are also included. Because of the isolated nature of traffic crashes on low-volume roads, a systemic or mass action approach to safety mitigation was recommended for an identified subset of the entire system. In addition, future development of a reliable crash prediction model is described.