Fares Beainy

Quality Assurance of Hot Mix Asphalt Pavements Using the Intelligent Asphalt Compaction Analyzer

Adequate compaction of asphalt pavements during their construction is essential to the long-term performance of the pavement. Current quality control techniques determine the quality at a limited number of points and are not indicative of the overall quality of the pavement. In this paper, the intelligent asphalt compaction analyzer (IACA) is used to estimate the density of an asphalt pavement during its construction and thereby determine the overall quality of compaction. A comparison of these estimates with the percent within limits (PWL) calculations based on roadway cores demonstrates that the IACA can be effectively used as a nondestructive quality assurance (QA) tool. Further, since the IACA continuously estimates the density of the asphalt in real time, inadequate compaction can be addressed during the construction, thereby improving the overall quality of pavement. Thus, the IACA can also serve as a valuable quality control (QC) tool during the construction.

Asphalt compaction quality control using Artificial Neural Network

Adequate compaction of asphalt pavements during their construction is essential to the long term performance of the pavement. Under/over-compaction during the construction process leads to the early deterioration and failure of the pavement. Current quality control techniques in the field involve the extraction of roadway cores and the measurement of density using point wise measurement techniques. Such tests determine the quality at discrete locations typically after compaction is complete and are not indicative of the overall quality of the pavement. Conversely, analyzing the vibrations of a vibratory compactor during asphalt pavement construction is a proven indicator of the complete compaction quality. The compaction of asphalt pavement is a complicated process resulting in the absence of a closed-form solution for estimating stiffness or density of the mat being compacted. In this paper, we present Intelligent Asphalt Compaction Analyzer (IACA) as a decision making device for operators to treat the asphalt pavement in an appropriate way to control the quality of compaction. IACA is a classification tool that uses Artificial Neural Network (ANN) to give an estimate density value of the road underneath the roller drum during compaction process. The Fast Fourier Transform of the roller vibrations is used by the ANN to estimate density values. Currently available Intelligent Compaction (IC) techniques provide a measure of quality that is hard to relate to any physical measurement. In contrast, the proposed method gives a density measurement that can be verified either by the extraction of roadway cores or through the use of conventional density gauges. As a result, the performance of the proposed method can be validated during the construction process.

Viscoelastic-Plastic Model of Asphalt-Roller Interaction

There are several factors that influence the quality of an asphalt pavement. While pavement design, material selection, and environmental parameters play a big role in the quality and useful life of the pavement, adequate quality control during the compaction process is also necessary to achieve the desired quality. Previous research has addressed the effect of material selection, pavement design and construction, and environmental factors on the long-term performance of asphalt pavement. However, very little work has been done to address the effect of the compaction process on the quality of pavement. A proper understanding of the interaction between the roller and an asphalt pavement is necessary to develop closed-loop control algorithms for intelligent compaction (IC) of asphalt pavements. While several variants of IC techniques are being offered in the market, there is a need for an accurate dynamical model of the compaction process to validate these methods. In this paper, a viscoelastic-plastic (VEP) model of an asphalt pavement is developed that can effectively represent the dynamical properties of the asphalt mat during compaction. This model is then integrated with a mathematical model of a vibratory compactor to study the response of the coupled system during compaction. The VEP model is based on Burger’s model and is used to represent the dynamical properties of the asphalt pavement. The parameters of the model are first estimated from the dynamic modulus master curves of the asphalt mix. These parameters are then continuously updated to accurately represent the properties of the asphalt mat during compaction. Detailed mathematical equations are developed that relate the changes in the asphalt mat to the vibratory response of the roller during compaction. Numerical simulation of the VEP model shows that the response of the coupled system can be used to study the compaction of asphalt pavements. Comparison of the simulation results with data gathered during construction of asphalt pavements indicate that this model could serve as a theoretical foundation for intelligent compaction of asphalt pavement.

Dynamical Response of Vibratory Rollers during the Compaction of Asphalt Pavements

Intelligent compaction (IC) of asphalt pavements is an emerging area of research that attempts to extend mechanistic-empirical design principles to the construction of asphalt pavements. These techniques monitor the vibrations of the compactor and vary the roller parameters in real time to ensure adequate and uniform compaction. Although these techniques are in various stages of field demonstration, their performance is still being verified. The lack of established theoretical foundations has limited the widespread acceptance of these techniques. In this paper, a viscoelastic-plastic (VEP) model is used to simulate the behavior of vibratory rollers during the compaction of asphalt pavements. The VEP model is shown to be relatively accurate, computationally tractable, and in a form that is conducive to numerical simulation. Comparison of the simulation results with data gathered during construction of asphalt pavements indicate that this model can serve as the basic theoretical foundation for the realization of intelligent compaction of asphalt pavements.

Development of an autonomous ATV for real-life surveillance operations

Mobile robots are becoming an essential participant in perimeter security, reconnaissance missions and search & rescue operations. Most mobile robots available for the surveillance are not designed for outdoor use and have limited payload capability. In this paper, the Zoiros-kinito-Mati (smart-mobile-eye/ZKM-1) using off the shelf components with high load capability and off-road navigation is introduced. ZKM-1 is built on a modified ATV platform and is capable of autonomous as well as remote controlled operations. ZKM-1 is built for outdoor demonstration purposes. A sub-meter GPS receiver is used for position updates and an electronic magnetic compass used for obtaining heading information. ZKM-1 has an onboard network for connecting different modules. This network can be connected wirelessly to other networks or computers. A network camera mounted on a pantilt platform with a built-in microphone is installed on the ATV for video and audio feedback. ZKM-1 can also be remotely operated using a regular gaming joystick over the network. In this paper, design, development and deployment of the ZKM-1 are presented in details.

Unmanned Aerial Vehicles operational requirements and fault-tolerant robust control in level flight

Unmanned Aerial Vehicles (UAVs) are playing an important role both in military as well as civilian applications. However, their role in civilian applications is hampered by the lack of adequate guidelines and operational requirements. In this paper a set of flight, operational and performance requirements for the use of fixed wing UAVs in civilian applications are collated from several resources in the public domain. The application of these requirements to the flight control of an unmanned fixed-wing aircraft is also addressed in this paper. A robust controller is designed to maintain the stability and the desired performance of the system in the presence of modeling uncertainty and measurement noise. A neural network based Fault Detection and Identification (FDI) scheme is then developed to estimate the effectiveness of control inputs. Finally, a reconfigurable controller is designed to compensate for the degradation of the actuation on the occurrence of a fault. Monte Carlo simulation is used to validate the capability and performance of the designed controller.

Modeling the dynamics of asphalt-roller interaction during compaction

AbstractCompaction is one of the important steps in pavement construction that significantly affects the quality and long-term performance of asphalt pavement. Compaction of asphalt pavement is inf...

Application of Intelligent Compaction Technology for Estimation of Effective Modulus for a Multilayered Asphalt Pavement

In this paper, a procedure for estimation of effective modulus of a multilayered hot mix asphalt (HMA) pavement using intelligent compaction (IC) is investigated. The study is conducted during the construction of an interstate highway (I-35) in Norman, OK. A complete coverage of the level of compaction of each of the asphalt pavement layers was recorded using the intelligent asphalt compaction analyzer (IACA). The collected IACA data allow determination of the level of compaction (density) at any selected location, for each layer, and provided a set of global positioning system (GPS) coordinates. Calibration procedures have previously been tested and validated by the authors to estimate the density of different types of pavements from IACA data. In this paper, a different calibration procedure is used to measure the dynamic modulus instead of the density of a pavement using IACA. Considering the IACA estimated density, the dynamic modulus of each of the selected locations for an individual pavement layer was measured from laboratory developed master curves. Thereafter, an effective modulus of the three-layer pavement system was calculated for all of the selected locations using Odemarks method. The proposed technique was verified by conducting falling-weight deflectometer (FWD) tests at these selected locations. Analyses of the results show that the proposed intelligent compaction technique may be promising in estimating the effective modulus of the pavement layers in a non-destructive manner. In addition, the Witczak model was used to estimate moduli of each of the pavement layers. The comparison of the Witczak model with FWD revealed that the model over-predicted the modulus significantly.

Dynamical model of asphalt-roller interaction during compaction

Proper and uniform compaction during construction is of utmost importance for the long term performance of asphalt pavement. Variations in the conditions of freshly laid pavements require adjustment of the compaction effort in order to obtain uniform and adequate density. One of the goals of on-going research in Intelligent Compaction (IC) is the development of adaptive feedback control mechanism to adjust the compaction effort according to the field and pavement conditions. Such feedback control systems require a good understanding of compaction dynamics. In this study, a dynamical model is developed to study the interaction between a moving vibratory roller and the underlying asphalt pavement during compaction. The asphalt pavement is represented as a lumped element model with visco-elastic-plastic properties. A procedure is presented to estimate the parameters of this model from standard tests on asphalt mix conducted in the laboratory. The combined roller-pavement dynamical model is used to replicate field compaction of an asphalt pavement using a vibratory roller. Numerical simulation results indicate good agreement with results observed during compaction of pavements in the field. Comparison between the simulation results and the results collected from the actual pavement construction job show that the model could be used as a mathematical basis for the development of advanced compaction methods.

Transient response of a vibratory roller during compaction

Compaction is the last, but possibly the most important, phase that an asphalt pavement goes through during construction. Adequate compaction is necessary for the long term performance of an asphalt pavement. Inadequate/improper compaction is one of the leading causes of early deterioration and failure of these pavements. Current quality control procedures also depend on destructive testing to ascertain the quality and thereby contribute to the early failure of the pavements. Non-destructive Intelligent Compaction (IC) techniques have been introduced to control the quality of construction of these pavements, but with limited success. Currently available IC systems display real-time measurements that are indicative of the pavement quality. However, these measurements are not adequately correlated with any measurements obtained from the finished pavement. One of the reasons for the poor accuracy is the lack of adequate modeling and mathematical analysis in the design of IC systems. These systems are typically built using heuristic data and are not amenable to mathematical analysis. In this paper, the dynamics of the vibratory compactor is studied and the effect of system parameters like the thickness of the pavement, type of asphalt mix, etc., on the response characteristics is determined. These measurements are then used to analyze the transient response of a vibratory roller during compaction. The response characteristics provide an insight into the requirements for feedback control and can be used as a starting point for improving the performance of IC systems.