Search results for: A. Sidess

Authors: Mai Sirhan, Arieh Sidess

Abstract:

Asphaltic mixture is a heterogeneous material composed of three main components: aggregates; bitumen and air voids. The professional experience and scientific literature categorize asphaltic mixture as a viscoelastic material, whose behavior is determined by temperature and loading rate. Properties characterization of the asphaltic mixture used under the service conditions is done by compacting and testing cylindric asphalt samples in the laboratory. These samples must resemble in a high degree internal structure of the mixture achieved in service, and the mechanical characteristics of the compacted asphalt layer in the pavement. The laboratory samples are usually compacted in temperatures between 140 and 160 degrees Celsius. In this temperature range, the asphalt has a low degree of strength. The laboratory samples are compacted using the dynamic or vibrational compaction methods. In the compaction process, the aggregates tend to align themselves in certain directions that lead to anisotropic behavior of the asphaltic mixture. This issue has been studied in the Strategic Highway Research Program (SHRP) research, that recommended using the gyratory compactor based on the assumption that this method is the best in mimicking the compaction in the service. In Israel, the Netivei Israel company is considering adopting the Gyratory Method as a replacement for the Marshall method used today. Therefore, the compatibility of the Gyratory Method for the use with Israeli asphaltic mixtures should be investigated. In this research, we aimed to examine the impact of the compaction method used on the mechanical characteristics of the asphaltic mixtures and to evaluate the degree of anisotropy in relation to the compaction method. In order to carry out this research, samples have been compacted in the vibratory and gyratory compactors. These samples were cylindrically cored both vertically (compaction wise) and horizontally (perpendicular to compaction direction). These models were tested under dynamic modulus and permanent deformation tests. The comparable results of the tests proved that: (1) specimens compacted by the vibratory compactor had higher dynamic modulus values than the specimens compacted by the gyratory compactor (2) both vibratory and gyratory compacted specimens had anisotropic behavior, especially in high temperatures. Also, the degree of anisotropy is higher in specimens compacted by the gyratory method. (3) Specimens compacted by the vibratory method that were cored vertically had the highest resistance to rutting. On the other hand, specimens compacted by the vibratory method that were cored horizontally had the lowest resistance to rutting. Additionally (4) these differences between the different types of specimens rise mainly due to the different internal arrangement of aggregates resulting from the compaction method. (5) Based on the initial prediction of the performance of the flexible pavement containing an asphalt layer having characteristics based on the results achieved in this research. It can be concluded that there is a significant impact of the compaction method and the degree of anisotropy on the strains that develop in the pavement, and the resistance of the pavement to fatigue and rutting defects.

Keywords: anisotropy, asphalt compaction, dynamic modulus, gyratory compactor, mechanical properties, permanent deformation, vibratory compactor

Procedia PDF Downloads 92

Authors: M. Sirhan, S. Bekhor, A. Sidess

Abstract:

In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

Procedia PDF Downloads 104