The Superpave specification parameter |G*|/sin δ for high temperature performance grading of paving asphalts has not been found to be adequate in rating various binders, especially some polymer-modified ones, for their rutting resistance. This has led researchers to seek other possible parameters that may better relate to rutting resistance and also to search for ways to improve the existing parameter |G*|/sin δ so that it is more sensitive to pavement performance. Some researchers have suggested the repeated creep and recovery test, while others have used a semiempirical approach as a means to refine the existing Superpave specification parameter. The present work revisits the proposed refinements of the Superpave specification parameter and shows that the semiempirical approach involving curve-fitting of experimental data is not necessary if the derivations are based on fundamental concepts. The final equations obtained through a theoretical development are verified using part of the same experimental data that were used by the earlier researchers.

The inhomogeneity of aggregates in an asphalt concrete pavement can be a primary factor in its deterioration. Statistical tests are useful for judging the extent of inhomogeneity. However, their use requires knowledge of both the distribution function of the statistic and the power of the test. The distribution is used in deciding whether or not a pavement is homogeneous, while the power of a test is used to select the test that is most appropriate for a particular gradation. Simulation is used to derive the distributions and powers of four tests, two based on aggregate frequencies and two on aggregate separation distances. Simulation must be used for evaluating the distribution of the homogeneity indices because they cannot be derived analytically, which is generally the case for asphalt aggregate specimens. A nearest-neighbor statistic is shown to be the most powerful for one homogeneous-inhomogeneous condition. Simulation is also used to show that independent estimates of a statistic can be obtained from a single core, which will increase the number of values of a statistic from a given number of specimens.

When constructing laboratory samples of bituminous mixture that are intended to have the same characteristics as the in situ pavement layer, the most important factor to consider is compaction. It is widely accepted that different laboratory compaction methods produce samples with different orientations and distributions of aggregates, and different distributions and shapes of voids. The effect of different laboratory compaction procedures (namely, roller, Marshall, vibrating hammer, and gyratory techniques) on the indirect tensile stiffness and fatigue properties of two standard Irish bituminous mixtures (namely, hot-rolled asphalt and dense base coarse macadam) was investigated. The roller compaction method produced specimens of lower stiffness, similar to site compacted samples. The influence of the compaction method on the fatigue strength of asphalt mixes would appear to be mixture dependent; mixes with grading profiles that are designed for aggregate interlock were found to have higher fatigue strengths, provided the material was compacted using a method that would facilitate reorientation of the aggregates.

The accurate prediction of rutting development is an essential element for the efficient management of pavement systems. The objective of this paper is to demonstrate the effectiveness of the estimation of rutting models by combining the information from two data sources, the AASHO and the WesTrack road tests. Combined estimation with both data sources is used to identify parameters that are not identifiable from one data source alone. In addition, this estimation approach also yields more efficient parameter estimates. The results presented in this paper demonstrate that joint estimation produces more realistic parameter estimates than those obtained by using either data set alone. Furthermore, joint estimation allows us to account for the effects of pavement structure, axle load configuration, asphalt concrete mix properties, freeze-thaw cycles, and hot temperatures in a single model. Finally, it allows us to predict the relative contributions of rutting originating both in the asphalt concrete and in the unbound layers in the same model.

This research compares various methods for determining the drag coefficient of a capsule moving through a pneumatic capsule pipeline. A theoretical drag coefficient equation is derived using the 1D energy equation, continuity equation, and momentum equation. The equation contains the orifice coefficients of the end disks, which cannot be predicted accurately without knowing the curvature of the edge of the end disks. The equation is compared with other equations and experimental data found in literature. Using the sharp-crested orifice coefficient published in textbooks overpredicts experimental data by >30%. Calibrating the equation to match individual sets of experimental data yields a maximum 14% error. Finally, using the equation and an empirical formula to correct the orifice coefficient as a function of the diameter ratio of the end disks can predict the drag coefficient within 20% of the experimental data. Achieving greater accuracy requires more accurate determination of the ratio of pipe to disk diameter, curvature of the disk edge, or direct measurement of the drag of individual capsules through model or prototype tests.

An evaluation of wave-induced pore pressures and effective stresses has been recognized by marine geotechnical engineers as an important factor in the design of offshore pipelines. Most previous investigations for the wave-seabed-pipe interaction problem have not considered the material of the pipeline. Thus, the internal stresses and deformation of the pipeline have not been investigated in the wave-seabed-pipe interaction problem. This paper considers the pipeline itself as an elastic material and links the analysis of the pipeline with the wave-seabed interaction problem. Based on the numerical results presented, the effective stresses in the angular direction σ_;gu;gu and shear stress τ within the pipe are much larger than the wave-induced pore pressure.

Steel pipelines buried in cold regions are subject to thermally induced axial loads, hoop stresses caused by the action of the fluids they convey, and differential settlements caused by factors such as thaw settlements, frost heave, and landslides. Due to these loads, pipelines may experience localized deformations well into the plasticity range of the pipe material. This paper presents an experimental program consisting of seven tests conducted to investigate and document the deformational behavior of full-scale pipes subjected to loads similar to those in the field. The specimens and loading conditions were carefully selected to match those in the field. A finite-element model was developed using the finite-element simulator ABAQUS to predict the local buckling behavior of pipes. The finite-element model was based on a large displacement, large rotation, finite-membrane-strain formulation and included material nonlinearity effects. The comparison between the numerical and the experimental results demonstrates the ability of the analytical model to predict the local buckling behavior of pipes when deformed well into the postyield range.

The paper presents a simple vehicle dynamics model for estimating maximum vehicle acceleration levels based on a vehicle's tractive effort and aerodynamic, rolling, and grade resistance forces. In addition, typical model input parameters for different vehicle, pavement, and tire characteristics are presented. The model parameters are calibrated/validated against field data that were collected along the Smart Road test facility at Virginia Tech utilizing a truck and trailer for 10 weight-to-power configurations, ranging from 85 kg/kW to 169 kg/kW (140 lb/hp to 280 lb/hp). The model was found to predict vehicle speeds at the conclusion of the travel along the section to within 5 km/h (3.1 mi/hr) of field measurements, thus demonstrating the validity and applicability of the model.

Artificial neural networks are applied as a means of estimating the average annual daily traffic (AADT) volume from short-period traffic counts. Fifty-five automatic traffic recorder sites located on low-volume rural roads in Alberta, Canada are studied. The neural network models used in this study are based on a multilayered, feedforward, and back-propagation design for supervised learning. The AADT estimation errors resulting from various durations and frequencies of counts are analyzed by computing average and percentile errors. The results of this study indicate a clear preference for two 48-h counts as compared to other frequencies (one or three) or durations (24- or 72-h) of counts. In fact, the 95th percentile error values of about 25% for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. A number of advantages of the neural network approach over the traditional factor approach of AADT estimation are also included in the paper.

The U.S. airline industry is facing delays due to congestion problems in the air transportation network. These delays not only lead to increased costs, but they also have safety related implications. Some of the costs related to such delays could be minimized by holding aircraft on the ground at the originating airport when congestion-related delays are expected en route or at the destination airport. Aside from reducing operating costs, the ground holding policy (GHP) also has beneficial implications on some safety concerns, since it is generally felt that it is better to hold an aircraft on the ground than in the air. The GHP is formulated as an integer programming problem and solved using heuristic techniques. However, the presence of discrete and binary integer variables increases the complexity involved when solved using traditional algorithms. Genetic algorithms (GAs) offer a powerful alternative for efficiently solving such problems. A GA is a search and optimization technique based on natural genetics and selection. This paper presents a discussion of GAs and their applicability to evaluating the GHP. Specific examples are presented to illustrate such applications. Results obtained from the test problems are consistent with expectations, indicating that GA can be used as a solution technique. It is observed that the program run time was reasonably low.

Ground holding policy (GHP) offers a potential solution to alleviate increasing congestion problems in the air transportation network. The policy proposes to hold an aircraft before takeoff so that when it arrives at its destination, it need not wait in the air before landing. This is motivated by the fact that airborne delays are costlier than ground delays. Many attempts have been made to solve the static GHP problem. However, aspects such as flight cancellations, transfers at hub airports, en route speeding, weather changes, and the interdependent nature of arrival and departure capacities suggest that updating ground holding decisions as time progresses is important. In this paper the dynamic instantaneous GHP problem, considering some of the above aspects, is formulated as an integer programming model. The presence of binary integer variables and discrete variables make the GHP problem complex and computationally difficult to solve using traditional algorithms. As an alternate approach, the use of genetic algorithms is demonstrated and analyzed in this paper. The observed program run time is reasonably small, indicating that genetic algorithms can be used for implementing GHP in real time for dynamic operating environments.

This paper presents the results of a recent investigation on the performance of four recycled concrete aggregates (RCAs). The materials, obtained by crushing concrete with compressive strength ranging from 15 MPa to 75 MPa, were reconstituted to satisfy the grading requirements for a subbase material. Triaxial specimens were tested under repeated loading one day after compaction. It appears that the original concrete compressive strength, the amount of softer material in the RCA, and the flakiness index of the RCA can significantly affect the resilient modulus. Degradation is mostly related to the crushing of softer and flaky materials within the aggregate matrix. In this regard, the Ten Percent Fines test is suitable as an evaluation test as it does not impose an excessive force on the RCA. The overall results indicated that RCA may be utilized as a subbase or base course material if it can be produced to consistently meet product quality standards.