Asphalt endurance limit is a strain value if experienced by asphalt pavement layer, no accumulated damage will occur and is directly related to asphalt healing. Therefore, if the pavement experiences this value of strain, or lower, no fatigue damage would accumulate within that pavement section. Beam fatigue test data conducted under the NCHRP Project 9-44A were extracted and utilized to create an artificial neural network predictive model (ANN) to determine the endurance limit strain values for conventional asphalt concrete pavements. The developed ANN model architecture as well as how to utilize it to predict the endurance limit were demonstrated and discussed in detail. Also, a stand-alone equation that is capable in the prediction of the endurance limit strain value, separate from the ANN model environment, was derived utilizing the eclectic extraction approach. The model training and validation data included 934 beam fatigue laboratory data points, as extracted from NCHRP Project 9-44A report. The developed model was able to determine the endurance limit strain value as a function of the stiffness ratio, number of cycles to failure, initial stiffness and rest period, and had a reasonable coefficient of determination (R2) value, which indicates the reliability of both the developed ANN model and the stand-alone equation. Furthermore, a correlation between the endurance limit strain values, as predicted utilizing the generated regression model under the NCHRP project 944-A, and the endurance limit strain values predicted utilizing the stand-alone ANN derived equation was found with a high R2 value.

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Asphalt concrete healing is one of the important concepts related to flexible pavement structures. Fatigue endurance limit (FEL) is defined as the strain limit under which no damage will be accumulated in the pavement and is directly related to asphalt healing. Pavement section designed to handle a strain value equivalent to the endurance limit (EL) strain will be considered as a perpetual pavement. All four-point bending beam fatigue testing results from the NCHRP 944-A project were extracted and utilized in the development of artificial neural network (ANN) EL strain predictive model based on mixture volumetric properties and loading conditions. ANN model architecture, as well as the prediction process of the EL strain utilizing the generated model, were presented and explained. Furthermore, a stand-alone equation that predicts the EL strain value was extracted from the developed ANN model utilizing the eclectic approach. Moreover, the EL strain value was predicted utilizing the new equation and compared with the EL strain value predicted by other prediction models available in literature. A total of 705 beam fatigue lab test data points were utilized in model training and evaluation at ratios of 70%, 15%, and 15% for training, testing, and validation, respectively. The developed model is capable of predicting the EL strain value as a function of binder grade, temperature, air void content, asphalt content, SR, failure cycles number, and rest period. The reliability of the developed stand-alone equation and the ANN model was presented by reasonable coefficient of determination (R2) value and significance value (F).

Aggregates constitute a major part of pavement construction. The strength, durability, and quality of the aggregate affects the overall performance of the pavement structure. Materials sourced near a construction site do not always meet the strength required for pavement construction, however, and haulage of aggregates of the required quality is often costly. For better use of locally available materials, stabilizing agents such as lime, cement, asphalt cement, and fly ash are often used to enhance the strength of the local aggregates. Pavement performance is influenced by both the structure itself and the layer materials present in it. The stiffness of the base layer, for instance, influences the tensile strain in the asphalt layer and compressive strains in the subgrade soil. The tensile strains at the bottom of the asphalt layer and compressive strains in the top zone of the subgrade soils are the main response components affecting fatigue cracking and rutting, respectively. In this study, field performance [rutting, cracking, and roughness measured in relation to the International Roughness Index (IRI)] of pavement sections with treated and untreated base layers were compared to determine the effects of stabilizing agents. In relation to fatigue cracking and pavement surface roughness, the treated sections outperformed the untreated sections. The average values of all three distresses showed better performance for the treated base layer sections with fatigue cracking averaging 2.2 times lower than the untreated sections. The combined rutting and IRI of the treated base layer sections averaged about 0.10 in. and 1.4 times lower than those of the untreated base layer sections, respectively.