A Framework for Post-Earthquake Response Planning in Emerging Seismic Regions: An Oklahoma Case Study

This paper presents a framework for establishing post-earthquake response protocols in regions facing emerging seismic hazards through a case study of Oklahoma bridges. First, it establishes the need for new attenuation models for the Oklahoma area because of the poor fit of current attenuation models. Then, two methods are established to inspect bridges after an earthquake: smart inspection radii and ShakeCast. The smart radii use a modified version of the Campbell (2003) attenuation model to determine seismic demand and a trigger S1 value to represent bridge capacity. This trigger S1 value is validated by calculating slight HAZUS fragility curves for past earthquakes. ShakeCast is an online resource from USGS that uses real-time ground motion data (i.e., a ShakeMap) as seismic demand and modified HAZUS fragility curves to represent bridge capacity. Because of better-informed data on the ground shaking levels, ShakeCast recommends significantly fewer inspections than inspection radii, translating to cost savings for the Oklahoma Department of Transportation.

Poisson Regression Analysis of Highway Fatality Accident Data in Oklahoma

Analysis of fatality automobile accident data can be challenging in rural areas where a relatively small number of such accidents occurs on specific sections of highways. Combining crash data from the Fatality Analysis Reporting System (FARS) and highway networks and design specifications from the Oklahoma Department of Transportation (ODOT), this article employs Poisson regression analysis to determine what roadway characteristics (e.g. grade, geometry, and design) are most associated with fatal accidents on predominantly rural segments of highways in Oklahoma. The results provide information about what combinations of highway design traits have contributed most to past crashes and therefore can identify potentially dangerous road segments system-wide. This information will help transportation engineers evaluate current construction practice and seek ways to address design issues that are shown to contribute significantly to serious crashes.

Poisson Regression Analysis of Highway Fatality Accident Data in Oklahoma

Analysis of fatality automobile accident data can be challenging in rural areas where a relatively small number of such accidents occurs on specific sections of highways. Combining crash data from the Fatality Analysis Reporting System (FARS) and highway networks and design specifications from the Oklahoma Department of Transportation (ODOT), this article employs Poisson regression analysis to determine what roadway characteristics (e.g. grade, geometry, and design) are most associated with fatal accidents on predominantly rural segments of highways in Oklahoma. The results provide information about what combinations of highway design traits have contributed most to past crashes and therefore can identify potentially dangerous road segments system-wide. This information will help transportation engineers evaluate current construction practice and seek ways to address design issues that are shown to contribute significantly to serious crashes.

Archaeological Investigations at 34WG220: A Prehistoric Occupation in the Arkansas River Valley of Eastern Oklahoma

In January and February of 1997, the Oklahoma Department of Transportation (ODOT) conducted archaeological work at site 34WG220 within right-of-way for a project involving realignment of SH-72 just south of Coweta, Wagoner County, Oklahoma. The site investigation consisted of the monitoring of trench excavation as well as controlled machine stripping. A few prehistoric artifacts, none of which are diagnostic, were found during the investigation. However, two cultural features were discovered during the monitoring of the trench excavation. The features were investigated by hand excavation of a 1 x 2 test unit.

Gradation and Moisture Effects on Resilient Moduli of Aggregate Bases

Resilient modulus ( M R) which properly characterizes the load-deformation response of pavement materials under traffic loading, is evaluated. The M R values due to three different gradations and three different moisture contents were investigated for the Richard Spur and the Sawyer aggregates, which are commonly used in Oklahoma as the subbase or base materials of roadway pavements. The three gradations were finer limit, median, and coarser limit, as specified by the Oklahoma Department of Transportation for Type A aggregate. The three moisture contents selected are optimum moisture content (OMC), 2 percent below OMC, and 2 percent above OMC. To investigate the variability of the test results, six duplicate M R tests under identical conditions were performed for each case by using the AASHTO T294-94 method. Furthermore, the material properties, K1 and K2, which are required input in the AASHTO pavement design equation, were evaluated for the M R values obtained. Finally, multiple linear regression models for predicting the M R values of the two aggregates were established.

Modeling Time-Dependent Behavior of Pavement Drainage Using Linear System Identification and Neural Network Techniques

Inadequate drainage continues to be a major cause of problems associated with long-term structural integrity and performance of roadway pavements. To reduce the impact of these drainage-related problems, it is customary to provide measures to prevent water from entering the pavement system and to enhance the drainage capability of the pavement base to rapidly move the water that inevitably finds its way into the pavement system. The performance of different drainage systems in the field is not clearly known. Oklahoma Department of Transportation (ODOT) has been collecting rainfall and outflow information at sites with edge drains since 1992. The sites have different types of surfaces, base courses, and edge drains. The data collected by ODOT at these sites were used to develop two types of numerical models to predict the outflow–time history using rainfall–time history as the input. One model is based on linear system identification theory, and the other model is based on an artificial neural network. The development of these models is presented, and the model predictions are compared with the measured field data. The efficiency of the drainage systems, including the AASHTO criteria for the drainage time, at these sites is compared by using the numerical models and synthetic, but the same, rainfall events.

Development of New Network Optimization Model for Oklahoma Department of Transportation

A new pavement network optimization model based on the Markov decision process (MDP) is presented. The new model is a global optimization model in which the entire network can be optimized without being divided into mutually independent groups. Current MDP models in use for pavement management use only one routine maintenance model for all types of rehabilitation and reconstruction treatments. The new formulation provides separate routine maintenance models for each type of treatment, which is more realistic than the currently available formulations. Methods for estimating pavement maintenance and rehabilitation benefits are described. These methods can be used for the optimization models with objectives of maximization when inadequate data are available to consider road user costs. The model has been applied to the network-level pavement management system for the Oklahoma Department of Transportation. Results of example runs are discussed.

Asphalt-Aggregate Interactions Characterized by Zeta Potential and Retained Strength Measurements for Natural and Organosilane-Treated Aggregates

Zeta potentials in water have been measured for a poorly performing, easily stripped hard limestone, a well-performing hard limestone, a sandstone, a rhyolite, soda lime glass beads, and for eight AC-20 asphalt binders. Four of the binders selected to represent a range of zeta potentials were combined with the sandstone, the limestones, and the glass beads according to Oklahoma Department of Transportation mix formulas and tested for retained strength. The good hard limestone was the best overall performer with retained strengths of 96 to 100 percent, the poor hard limestone was acceptable, overall the sandstone was unsatisfactory; and the glass beads did not survive the retained strength preconditioning procedure. The aggregates were then treated with organosilane derivatives to chemically modify their surfaces and to change the aggregate zeta potential. For example, the zeta potential of the poor hard limestone was made to be closer to that of the good hard limestone. The modified aggregates were then used in the same mix and the retained strengths measured. The good hard limestone mix exhibited no change in retained strength; however, the poor hard limestone, the sandstone, and the glass bead mixes gave excellent retained strengths.