A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fatigue Endurance Limit Model Utilizing Artificial Neural Network for Asphalt Concrete Pavements
Asphalt healing is directly related to the endurance limit strain and if the pavement experiences this value of strain, or lower, no fatigue damage would develop. The purpose of this paper is to extract the beam fatigue tests data that was collected under the NCHRP Project 9-44A and utilize it to create an artificial neural network predictive model (ANN) to determine the endurance limit values for conventional asphalt concrete pavements. The paper demonstrates the created ANN model architecture as well as how to utilize it to predict the endurance limit. Also, a stand-alone equation that is capable to predict the strain values, separate from the ANN model environment, was derived. The model training and validation data included 934 beam fatigue laboratory data points conducted under NCHRP Project 9-44A. The developed model is able to determine the endurance limit (strain) as a function of the stiffness ratio, number of cycles to failure, initial stiffness, and rest period. The created model had a reasonable R2 and significance values indicating the reliability of both the developed ANN model and the stand-alone equation.
Fatigue Endurance Limit Model Utilizing Artificial Neural Network for Asphalt Concrete Pavements
Asphalt healing is directly related to the endurance limit strain and if the pavement experiences this value of strain, or lower, no fatigue damage would develop. The purpose of this paper is to extract the beam fatigue tests data that was collected under the NCHRP Project 9-44A and utilize it to create an artificial neural network predictive model (ANN) to determine the endurance limit values for conventional asphalt concrete pavements. The paper demonstrates the created ANN model architecture as well as how to utilize it to predict the endurance limit. Also, a stand-alone equation that is capable to predict the strain values, separate from the ANN model environment, was derived. The model training and validation data included 934 beam fatigue laboratory data points conducted under NCHRP Project 9-44A. The developed model is able to determine the endurance limit (strain) as a function of the stiffness ratio, number of cycles to failure, initial stiffness, and rest period. The created model had a reasonable R2 and significance values indicating the reliability of both the developed ANN model and the stand-alone equation.
Fatigue Endurance Limit Model Utilizing Artificial Neural Network for Asphalt Concrete Pavements
Isied, Mayzan (author) / Souliman, Mena (author)
International Airfield and Highway Pavements Conference 2019 ; 2019 ; Chicago, Illinois
2019-07-18
Conference paper
Electronic Resource
English
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