Integrated predictive artificial neural network fatigue endurance limit model for asphalt concrete pavements

Abstract

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.

Predictive Artificial Neural Network Laboratory Fatigue Endurance Limit Model for Asphalt Concrete Pavements Based on the Volumetric Properties and Loading Conditions

“…In addition, the coefficient of determination (R 2 ) was higher for the newly developed ANN than the one developed under the NCHRP project 944-A (0.891). Also, a good correlation between the newly developed model and the ANN model previously developed by Isied and Souliman was found, as demonstrated in Figure 8 (25). This indicates the ability of the developed model to predict EL strain values.…”

“…The newly developed EL strain prediction model was compared with the EL prediction model previously developed under the NCHRP project 944-A, as well as the ANN prediction model developed by Isied and Souliman (25). Both models had EL strain prediction equations as shown in Equations 4 and 5, respectively.…”

Damage Mechanism of Asphalt Concrete Pavement under Different Immersion Environments

“…By investigating the water damage degree of several expressways in China, some researchers analyzed the internal and external causes of water damage of pavement, and put forward measures to reduce the water damage of pavement from the point of design and construction. Some researchers proposed to use finite element analysis method to study the mechanical mechanism of internal structural failure of asphalt concrete pavement under the combined action of driving load and hydrodynamic pressure [8][9][10][11][12][13]. By comparing the water stability test methods at home and abroad, some researchers put forward the immersion rut test, and tested several commonly used mix structure types at present.…”

Fatigue modeling using neural networks: A comprehensive review

“…The predicted maximum relative errors for FNN and RBFNN are 19.79% and 31.25%, respectively. Other similar research predicting the fatigue strength as the output of the FNN can be found in previous works 239–249 …”