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The development and validation of a tool that can estimate the level of compaction of a Hot Mix Asphalt (HMA) pavement during its construction is addressed in this paper. Densification of asphalt pavements during their construction is usually accomplished through the use of vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. In this paper, it is shown that the measured vibrations of the compactor along with the process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the density of the asphalt mat. Contrary to existing techniques in the literature where a model is developed to fit the experimental data and to predict the density of the mat, a novel neural network based approach is adopted that is model-free and uses pattern-recognition techniques to estimate the density. During compaction of a HMA mat, the neural network then classifies the observed vibrations as those corresponding to a known level of compaction. The results also show that the analyzer can estimate the density continuously, and in realtime with accuracy levels adequate for quality control in the field. Using this tool, for the first time, the overall quality of construction of a HMA pavement can be verified thereby creating the potential to improve the quality of the roads.

An energy efficient cover of a region using

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Long-term performance of an asphalt pavement depends not only on the material properties but also on the stiffness achieved during compaction. Because the determination of stiffness during construction is not straightforward, a common approach uses predictive models to estimate the dynamic modulus of hot-mix asphalt (HMA) specimens. Four predictive models—the Witczak 1999, Witczak 2006, Hirsch, and Al-Khateeb models—were evaluated for their use in estimating the dynamic modulus of selected HMA mixtures that are commonly used in Oklahoma. Five mixes representing various aggregate sources, aggregate sizes, binder grades, and air void levels were tested in the laboratory, and the measured dynamic modulus of each mix was compared with the value predicted by each of the models. The performance of each predictive model was evaluated by three approaches: goodness-of-fit statistics, comparison of the measured and predicted values, and local bias statistics (slope, intercept, and average error). Analyses of the results showed that the predictive power of each model varied with the temperature and air void levels of a compacted specimen. Calibration factors were developed for each model to obtain an accurate estimate of dynamic modulus. The calibration factors are helpful for Level 2 and Level 3 designs of the Mechanistic–Empirical Pavement Design Guide.

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Intelligent Compaction (IC) of subgrade soil has been proposed to continuously monitor the stiffness of subgrade during its compaction. Modern IC rollers are vibratory compactors equipped with (1) an onboard measuring system capable of estimating the stiffness of the pavement material being compacted, (2) Global Positioning System (GPS) sensor to precisely locate the roller, and (3) an integrated mapping and reporting system. Using IC, the roller operator is able to evaluate the entire subgrade and address deficiencies encountered during compaction. Continuous monitoring of quality during construction can help build better quality and long-lasting pavements. However, most of the commercially available IC rollers report stiffness in terms of Original Equipment Manufacturer (OEM) specified indicator, known as Intelligent Compaction Measurement Value (ICMV). Although useful, additional tests are required to establish the correlation between these ICMV values and the resilient modulus of subgrade (M r ). Since the mechanistic design of the pavement is performed using M r , it is important to know if the design M r is achieved on the entire subgrade during compaction. This paper presents a systematic procedure for monitoring the level of compaction of subgrade in real time using intelligent compaction (IC). Specifically, the Intelligent Compaction Analyzer (ICA) developed at the University of Oklahoma was used for estimating the modulus of the subgrade. Results from two demonstration studies show that the ICA is able to estimate subgrade modulus with an accuracy that is acceptable for quality control activities during the construction of pavements.