About the research
Pavement design techniques have advanced to incorporate modern technology and scientific-based models to improve pavement construction, performance, maintenance, and rehabilitation. The reliability of these models depends upon input data gathered in the field via pavement instrumentation. There is a demonstrated lack of rigid pavement instrumentation and experimental field data nationally, leading to a shortage of pavement field data required to examine, model, and simulate the interaction of pavement components in the field (such as pavement base material, slab, tie bar, etc.). In order to rely upon more scientific-based models to improve pavement systems (and ultimately reduce pavement life-cycle costs), more data is required to refine existing and new pavement performance models.
Perhaps the most commonly used and popularly accepted model-based, modern approach to highway design is embodied in the Mechanistic-Empirical Pavement Design Guide (MEPDG), which incorporates models embedded in dedicated software (such as AASHTOWare Pavement ME Design) to predict pavement performance in greater detail than prior predictive models. Such dedicated software incorporates scientific data such as material mechanics, climate data, axle-load spectra, and other factors. Full implementation of the Mechanistic-Empirical Pavement Design by state departments of transportation (DOTs) requires customization or calibration/validation of the software for variables and pavement conditions at state and local levels. This in turn requires the collection of region-specific field data on climate, material properties, load response, and pavement performance for use in calibration and implementation of the software. Mechanistic-Empirical Pavement Design software uses these data inputs to more accurately simulate the load response of pavements and long-term pavement performance. Local calibration of the software involves comparing long-term performance simulation results to actual performance data at local sites (if possible) or from matching pavements in the Federal Highway Administration Long-Term Pavement Performance (LTPP) database. Several numerical models are available to predict pavement performance, and these models are an effective tool to predict the likelihood of pavement damage and longevity. These numerical models also enable improvements in road design methods, whether for new or rehabilitated pavements, that will help mitigate the problems of load-induced damage. However, most of these models have not been calibrated against actual field data obtained under realistic conditions.
To enhance the effectiveness of these models and to assist in their application, instrumented test sections of pavement can be monitored and data gathered to monitor performance factors (such as soil pressure, pavement temperature, strains and deflections caused by daily changes in temperature within the pavement, along with air temperature, wind speed, relative humidity, solar radiation, and precipitation). The pavement construction process can also be monitored, and the materials used in the pavement can be tested in the laboratory or in the field (using non-destructive testing) to ascertain material property information. As pavement systems are highly nonlinear in their responses to loads and load related strains, field data collected via instrumentation helps indicate which parameters need to be emphasized in the models to describe pavement performance and response to conditions. Data from the laboratory tests (or from non-destructive field testing) is input to the model to predict the road response. The predicted response is then compared to the measured response. A sensitivity analysis also helps determine which parameters should be adjusted to best fit actual conditions. Also the LTPP database can be used for calibration in addition to data related to material properties, traffic data, and pavement performance data provided by other DOTs. In the long term, the calibrated model is used in conjunction with existing Mechanistic-Empirical design models and Mechanistic-Empirical Pavement Design software to improve the design method. The data can be put into a format directly useful to designers and engineers.
Since 2001, the New York State Department of Transportation (NYSDOT) has significantly invested in instrumenting test pavement sections to acquire local data to improve calibration of Mechanistic-Empirical Pavement Design software. The instrumented field pavements in New York include I-490, I-90, and I-86. The installed sensors are still functioning to an extent that permits data collection of additional useful scientific information, and I-490 is providing high-quality data that will positively impact future design, construction, and maintenance of roads. As NYSDOT progresses in its adoption of the Mechanistic-Empirical Pavement Design approach, the test sections it has invested in over the past decade will play a key role in the validation of that approach. In addition to collecting load response data, it will be possible to assess the long-term performance of these pavements. (Mechanistic-Empirical Pavement Design requires both.)
An extended study will verify that the performance benefit is maintained in the long term and that these designs will save money in the long run. Additionally, on I-86, three different concrete pavement rehabilitation techniques were tested previously, with some differences in the performance. Extended study will provide a definitive conclusion of which method provides the best performance and is the most economical. Several states previously have conducted projects using instrumentation in pavement test sections to collect pavement performance data. In addition to the three I-86 sections instrumented to measure deflection, strain, and temperature in order to study different techniques of portland cement concrete (PCC) pavement rehabilitation, additional sections on I-490 and two sections on I-90 are being used to measure the effects of varying base types.
Other states such as Ohio, Minnesota, and Delaware, among others, have instrumented concrete sections to collect data which can be used for analysis. The sharing of data from multiple DOTs and geographic regions (via resources such as the LTPP database) adds significant value to pavement performance modeling tools and to the body of scientific knowledge; this pooled approach among multiple sources/DOTs also offers a more efficient and economical manner than on an individual basis.
The objectives of this study include: (1) Collecting load response and performance data and environmental monitoring at selected test pavements for four years; (2) installing new instrumented sections as needed for a better understanding of rigid pavement response, including monitoring for the duration of the project; (3) determining the impact of a base and other components (such as dowel bars, tie bars, etc.) on long-term performance of rigid pavement utilizing the data acquired and other nationally available data on the topic; (4) documentation of the processes, procedures, and findings; and (5) finalization of the rigid pavement design catalog with local validation and calibration of mechanistic-empirical models.