AL-Jarazi, R., Rahman, A. orcid.org/0000-0003-3076-7942, Ai, C. et al. (2 more authors) (2024) Evaluation and prediction of interface fatigue performance between asphalt pavement layers: application of supervised machine learning techniques. International Journal of Pavement Engineering, 25 (1). 2370551. ISSN 1029-8436
Abstract
Assessing interlayer fatigue performance is crucial for the durability and structural integrity of asphalt pavements. This study introduces supervised machine learning techniques to estimate interface fatigue life (IFL) and interface shear stiffness (ISS) between asphalt layers. Using 84 lab-prepared specimens tested under varying conditions-temperature, normal pressure, loading frequency and shear stress, with a constant tack coat type and application rate-predictive models were trained and validated. The influence of each factor on fatigue indices was evaluated before employing genetic expression programming (GEP) and artificial neural networks (ANNs) for prediction. Model efficacy was quantified using statistical metrics, highlighting the robustness of ANN models (R² = 0.980, RMSE = 0.826, MAE = 0.636 for ISS and R² = 0.972, RMSE = 0.498, MAE = 0.383 for IFL) over GEP models (R² = 0.921, RMSE = 1.402, MAE = 1.079 for ISS and R² = 0.913, RMSE = 0.623, MAE = 0.479 for IFL). Sensitivity analysis confirmed alignment with experimental data, identifying temperature as the most critical factor and frequency as the least influential on interface fatigue performance. These findings could lead to more reliable and efficient designs of interlayer bonding in asphalt pavement layers.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of an article published in International Journal of Pavement Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Asphalt pavement, interface fatigue life, interface shear stiffness, machine learning, genetic expression programming, artificial neural networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > SWJTU Joint School (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Nov 2024 10:44 |
Last Modified: | 20 Nov 2024 09:34 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/10298436.2024.2370551 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219611 |
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