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Current pavement performance prediction models are based on the parameters such as climate, traffic, environment, material properties, etc. while all these factors are playing important roles in the performance of pavements, the quality of construction and production are also as important as the other factors. The designed properties of Hot Mix Asphalt (HMA) pavements, known as flexible pavements, are subjected to change during production and construction stages. Therefore, most of the times the final product is not the exact reflection of the design. In almost any highway project, these changes are common and likely to occur from different sources, by various causes, and at any stage. These changes often have considerable impacts on the long-term performance of a project. The uncertainty of the traffic and environmental factors, as well as the variability of material properties and pavement structural systems, are obstacles for precise prediction of pavement performance. Therefore, it is essential to adopt a hybrid approach in pavement performance prediction and design; in which deterministic values work along with stochastic ones. Despite the advancement of technology, it is natural to observe variability during the production and construction stages of flexible pavements. Quality control programs are trying to minimize and control these variations and keep them at the desired levels. Utilizing the information gathered at the production and construction stages is beneficial for managers and researchers. This information enables performing analysis and investigations of pavements based on the as-produced and as-constructed values, rather than focusing on design values. This study describes a geo-relational framework to connect the pavement life-cycle information. This framework allows more intelligent and data-driven decisions for the pavements. The constructed geo-relational database can pave the way for artificial intelligence tools to help both researchers and practitioners having more accurate pavement design, quality control programs, and maintenance activities. This study utilizes data collected as part of quality control programs to develop more accurate deterioration and performance models. This data is not only providing the true perspective of actual measurements from different pavement properties but also answers how they are distributed over the length of the pavement. This study develops and utilizes different distribution functions of pavement properties and incorporate them into the general performance prediction models. These prediction models consist of different elements that are working together to produce an accurate and detailed prediction of performance. The model predicts occurrence and intensity of four common flexible pavement distresses; such as rutting, alligator, longitudinal and transverse cracking along with the total deterioration rate at different ages and locations of pavement based on material properties, traffic, and climate of a given highway. The uniqueness of the suggested models compared to the conventional pavement models in the literature is that; it carries out a multiscale and multiphysics approach which is believed to be essential for analyzing a complex system such as flexible pavements. This approach encompasses the discretization of the system into subsystems to employ the proper computational tools required to treat them. This approach is suitable for problems with a wide range of spatial and temporal scales as well as a wide variety of different coupled physical phenomena such as pavements. Moreover, the suggested framework in this study relies on using stochastic and machine learning techniques in the analysis along with the conventional deterministic methods. In addition, this study utilizes mechanical testing to provide better insights into the behavior of the pavement. A series of performance tests are conducted on field core samples with a variety of different material properties at different ages. These tests allow connecting the lab test results with the field performance survey and the material, environmental and loading properties. Moreover, the mix volumetrics extracted from the cores assisted verifying the distribution function models. Finally, the deterioration of flexible pavements as a result of four different distresses is individually investigated and based on the findings; different models are suggested. Dividing the roadway into small sections allowed predicting finer resolution of performance. These models are proposed to assist the highway agencies s in their pavement management process and quality control programs. The resulting models showed a strong ability to predict field performance at any age during the pavements service life. The results of this study highlighted the benefits of highway agencies in adopting a geo-relational framework for their pavement network. This study provides information and guidance to evolve towards data-driven pavement life cycle management consisted of quality pre-construction, quality during construction, and deterioration post-construction.
The purpose of this report is to share the experience gained and lessons learned by research staff during early data analyses of the General Pavement Studies (GPS) and to recommend procedures for future analysts. A review of the techniques used is provided. Shortcomings of the Long-Term Pavement Performance (LTPP) Data Base, known at the time of early analyses, are discussed and data base expectations for future analyses were identified. Some interesting and useful distress and roughness prediction models were developed that illustrate the effects of several design variables. Other analytical procedures for developing predictive equations were identified and described, which may be of use in future analyses. Ten techniques used by the research staff for evaluating the American Association of Highway and Transportation Officials (AASHTO) design equations are identified and recommendations for future evaluations provided.
"Climate change is one of the most concerning global issues and has the potential to influence every aspect of human life. Like different components of society, it can impose significant adverse impacts on pavement infrastructure. Although several research efforts have focused on studying the effects of climate change on natural and built systems, its impact on pavement performance has not been studied as extensively. The primary objectives of this thesis research was to quantify the effect of temperature changes on flexible pavement response and performance prediction using the AASHTOWare Pavement ME Design (PMED), and quantify the effects of Local Calibration Factors (LCFs) used by different state highway agencies in the United States on predicted pavement performance. Particular emphasis was given to LCF values used by the Idaho Transportation Department. The climatic data, as well as LCFs corresponding to several different states, were used to identify how different LCF values affect pavement performance prediction. The effects of atmospheric temperature changes on pavement temperature and Asphalt Concrete (AC) layer modulus were studied by analyzing the intermediate files generated by PMED. Finally, the impact of temperature change on AC dynamic modulus (E*) was also analyzed to link the PMED-predicted distresses with asphalt mix properties. Historical climatic data was obtained from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) database. Projected data considered to simulate the temperature changes in the future were generated by adopting two different approaches: (1) Manual alteration of historical temperature distribution data to represent scenarios with increased mean and standard deviation values; and (2) Use of temperature data projected by established Global Climate Models (GCM). All different climatic scenarios were used in PMED along with a standard pavement section, and the distresses predicted over the design life of the pavement were compared. Simulation results showed consistent increase in Total Pavement rutting and AC rutting with increasing air temperatures. The effect of temperature increase on AC thermal cracking predicted by PMED demonstrated inconsistent trends. In contrast, the projected temperature increase had no significant effect on bottom-up fatigue cracking for the chosen study locations. It was found that the impact of changed air temperatures can be different for pavement sections constructed in different geographic locations. Moreover, the analysis confirmed that the Local Calibration Factors (LCFs) established by different state highway agencies played a major role in governing the effect of future temperature increase on predicted pavement performance. Through an extensive stud."--Boise State University ScholarWorks.
The primary focus of this research was to determine the effects of design and construction features, such as overlay thickness and mix type, presence of milling, and type of restoration, on pavement response and performance and to establish their importance in the prediction of future performances of rehabilitated pavements. Long-Term Pavement Performance program Specific Pavement Study (SPS)-5 and SPS-6 experiments provided information to obtain a better understanding of the effects of design and construction features on pavement response and performance of rehabilitated flexible and rigid pavements.