SELECTION OF LONG LASTING REHABILITATION TREATMENT USING LIFE CYCLE COST ANALYSIS AND PRESENT SERVICEABILITY RATING

A wide range of variables influence the selection strategy for rehabilitation and maintenance of pavements. The focus of this study is to conduct a project-level evaluation of high traffic volume asphalt-surfaced pavements located in the state of Oklahoma and develop a performance based rehabilitation strategy. In order to develop feasible rehabilitation strategies, a systematic collection of relevant pavement-related data was provided by ODOT. The collected data includes performance measurements, traffic, climate and structural integrity of existing pavements obtained by falling weight deflectometer (FWD) analysis. These various data sets are supplemented with laboratory testing to determine the material characterization and damage characterization of different surface rehabilitation mixtures. The national highways located in the state of Oklahoma are divided in several pavement family groups. The representative pavement sections for family groups are identified and the required data for analysis are either extracted from existing sources or measured in the laboratory. Three levels of rehabilitation activities including light, medium and heavy rehabilitation are considered for each of the pavement family groups and a mechanistic-empirical methodology is employed to obtain an estimate of the performance of potential rehabilitation activities and their extended service life. A combination of local material properties, structural integrity and environmental condition are used for structural analysis and development of an evaluation output matrix. At the end of this study a series of time-based renewal solutions are recommended for pavement family groups with a similar existing condition and the most cost effective methodology is determined by performing life cycle cost analysis using RealCost software.Final report, November 2013-October 2015N

Assessment on Bonding Potentials of Trackless Tack under a Thin Overlay

Trackless tacks are used to minimize the loss of tack materials caused by adherence to moving tires. During the last two decades, the paving industry and highway agencies have had an increasing interest in the use of trackless tacks to ensure an adequate bond between the overlay and the existing pavement. Therefore, the need for more studies on the bonding characteristics of various trackless tacks is growing. The purpose of this study is to measure the bonding potential of trackless tacks and identify several variables that affect the shear resistance in terms of bonding strength and energy using statistical analysis. The improvement of interlayer shear resistance by tack treatment is different depending on the tack and surface types. Higher tack reactivation temperatures increase the interlayer shear resistance. Compaction effort is considered to have only a marginal effect on bond performance. Tack and surface types play a more critical role in determining the shear bond strength than residual tack rate in the field experiment

Determining the Optimum Sample Size for Quality Assurance (QA) of Asphalt Mixtures: A Case Study

Acceptance plans for asphalt mixtures use a certain sample size that is often established based on the purpose of sampling, population size, risk, and allowable error for evaluation. The rate of quality control (QC) sample size is often higher than the quality assurance (QA) sample size. The test results obtained from the QA samples are commonly used to validate the QC test results and to assist the state department of transportation (DOT) with payment decisions. However, if the QA sample size is insufficient to make accurate judgments, the probability of making incorrect decisions regarding acceptance increases. On the other hand, oversampling needlessly consumes both time and cost. To identify the appropriate sample size for QA testing, a balance must be struck between a number of variables. In this case study, two models were developed using the Oregon Department of Transportation (ODOT) data to determine the appropriate QA sample size. The need for this work was realized when a review of ODOT paving projects revealed a large variability in lot size. These ranged from 3000 to more than 100 000 tons with commensurate QA sample size rates. The typical standard deviation (STDEV) values of asphalt content (AC) and in-place density were determined. The developed models show that using the STDEV values that represented more than 90 percent of the projects, ODOT needed to increase QA sample size for both AC and density in lots of less than 22 000 tons. The results also show that sample can be decreased for AC and remain as is for density in projects of more than 22 000 tons of asphalt mixtures. The proposed models can be used to determine the optimum sample size for different lots sizes

COMPILATION OF LOCAL STUDIES AND REGIONAL CALIBRATION OF PAVEMENT ME DESIGN FOR RIGID AND FLEXIBLE PAVEMENTS IN OKLAHOMA (FHWA-OK-19-08 2277)

The AASHTOWare Pavement ME Design software was developed as an advanced pavement design tool based on the mechanistic-empirical pavement design guide (MEPDG). The existing performance prediction models utilized in this design guide are nationally calibrated, and it is essential to calibrate these models based on the specific regional materials and environmental conditions and pavement performance information. In this study, the Pavement ME design prediction models were calibrated using local inputs and performance data for the state of Oklahoma. Also, to facilitate using Pavement ME, an interface software named INput ME was developed. This software can be used to process and compile the required pavement ME input data based on the Oklahoma material, traffic, and climate properties gathered from long term pavement performance and Oklahoma and national cooperative highway research program datasets. The material input data were evaluated, and the most accurate available data was selected. The predictions from distress and international roughness index (IRI) models were evaluated and compared with the measured distress values, and the accuracy and bias of each model were determined. The nationally calibrated models showed large errors and significant bias values, which asserts the need for local calibration. The locally calibrated coefficients for the distress and IRI models for the Oklahoma pavement system were determined. The predictions from calibrated models show that the use of calibrated coefficients improves the accuracy of the pavement ME predictions and the design of flexible pavements in Oklahoma.Final Report May 2017-November 2019N