4 resultados para Prediction model

em Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States


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This report is one of two products for this project with the other being a design guide. This report describes test results and comparative analysis from 16 different portland cement concrete (PCC) pavement sites on local city and county roads in Iowa. At each site the surface conditions of the pavement (i.e., crack survey) and foundation layer strength, stiffness, and hydraulic conductivity properties were documented. The field test results were used to calculate in situ parameters used in pavement design per SUDAS and AASHTO (1993) design methodologies. Overall, the results of this study demonstrate how in situ and lab testing can be used to assess the support conditions and design values for pavement foundation layers and how the measurements compare to the assumed design values. The measurements show that in Iowa, a wide range of pavement conditions and foundation layer support values exist. The calculated design input values for the test sites (modulus of subgrade reaction, coefficient of drainage, and loss of support) were found to be different than typically assumed. This finding was true for the full range of materials tested. The findings of this study support the recommendation to incorporate field testing as part of the process to field verify pavement design values and to consider the foundation as a design element in the pavement system. Recommendations are provided in the form of a simple matrix for alternative foundation treatment options if the existing foundation materials do not meet the design intent. The PCI prediction model developed from multi-variate analysis in this study demonstrated a link between pavement foundation conditions and PCI. The model analysis shows that by measuring properties of the pavement foundation, the engineer will be able to predict long term performance with higher reliability than by considering age alone. This prediction can be used as motivation to then control the engineering properties of the pavement foundation for new or re-constructed PCC pavements to achieve some desired level of performance (i.e., PCI) with time.

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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.

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This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.

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This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.