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Within the Long-Term Pavement Performance (LTPP) program, two sources of data are used to characterize the climatic conditions for each test section. For the majority of the test sections, data for nearby national weather stations are obtained from the National Climatic Data Center (NCDC) and are used to compute estimated data for a virtual test section located at the test section. In addition, onsite instrumentation is used to obtain site-specific climatic data for the test sections included in the Seasonal Monitoring Program and for the Specific Pavement Studies (SPS) –1, –2, and –8 project sites. This report documents a study undertaken to examine the reliability and accuracy of the LTPP climatic data. The study confirmed that accurate daily, monthly, and yearly estimates of climatic data for a project location can be derived by using the NCDC weather data for several nearby weather stations. The variation in the climatic data was also characterized.
Long-Term Pavement Performance (LTPP) Seasonal Monitoring program (SMP) instrumentation was installed in pavement sections at the Ohio SHRP Test Road. The pavements are monitored for the seasonal variations of moisture, temperature and frost penetration. Data from the instrumentation is subjected to quality checks and prepared for uploading to the FHWA Information Management System (IMS). Findings from the testing are to be incorporated into future pavement design procedures.
Highway agencies across the nation are moving towards implementation of the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for pavement design. The benefits of implementing the MEPDG for routine use in Ohio includes (1) achieving more cost effective and reliable pavement designs, (2) lower initial and life cycle costs to the agency, and (3) reduced highway user impact due to lane closures for maintenance and rehabilitation of pavements. Implementation of the MEPDG is a process that requires time and agency resources (staffing, training, testing facilities including equipment, and so on). A key requirement is validating the MEPDG's nationally calibrated pavement distress and smoothness prediction models when applied under Ohio conditions and performing local calibration if needed. Feasibility of using the MEPDG's national models in Ohio was investigated under this study using data from a limited number of LTPP projects located in Ohio. Results based on limited data showed inadequate goodness of fit and significant bias in a number of the MEPDG new HMA pavement and JPCP performance prediction models. Limited recalibration of these models showed promising results indicating that a full-scale recalibration effort using a more extensive database assembled from projects located throughout the state is feasible.
The response of flexible pavement is largely influenced by the resilient modulus of the pavement profile. Different methods/approaches have been adopted in order to estimate or measure the resilient modulus of each layer assuming an average modulus within the layer. In order to account for the variation in the modulus of elasticity with depth within a layer in elastic pavement analysis, which is due to temperature or moisture variation with depth, the layer should be divided into several sublayers and the modulus should be gradually varied between the layers. A powerful and innovative computer program has been developed for elastic pavement analysis that overcomes the limitations of the existing pavement analysis programs. The new program can predict accurately and efficiently the response of the pavement consisted of any number of layers/sublayers and any number of loads. The complexity of the tire-pavement loading configuration can be modeled easily as well. Practical pavement engineering problems have been analyzed and discussed taking into consideration the modulus variation with depth as well as the complex tire-pavement loading configuration utilizing our newly developed MultiSmart3D program. The analyzed problems illustrated that powerful analytical tools, such as MultiSmart3D, are needed to study and predict the pavement response in practical and fast manners. For example, the predicted life time of the pavement can be increased or decreased by a factor more than two if the modulus of elasticity variation with depth is taken into consideration.
Sponsored by the Highway Division of ASCE; Long-Term Performance Program of the Federal Highway Administration. This collection contains papers from the International Contest on LTPP [Long-Term Performance Program] Data Analysis 1998-1999. This competition involved university students in the analysis of data in the LTPP database. Topics include: effect of locked-in curvature on portland cement concrete pavement; evaluation of a pavement performance prediction model using LTPP data; prediction of pavement performance?a neural network approach; and effectiveness of preventative maintenance strategies for asphalt concrete pavements based on LTPP distress data.