Enhancing the predictive accuracy of marshall design tests using generative adversarial networks and advanced machine learning techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.8444
- @Article{Asif:2025:mtcomm,
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author = "Usama Asif and Waseem Akhtar Khan and
Khawaja Atif Naseem and Syed Abdul Sami Rizvi",
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title = "Enhancing the predictive accuracy of marshall design
tests using generative adversarial networks and
advanced machine learning techniques",
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journal = "Materials Today Communications",
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year = "2025",
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volume = "45",
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pages = "112379",
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keywords = "genetic algorithms, genetic programming, Hot mixed
asphalt, Generative adversarial networks, Ensemble
learning with stacking, Marshall stability, Marshall
flow, gene expression programming",
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ISSN = "2352-4928",
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URL = "
https://www.sciencedirect.com/science/article/pii/S2352492825008918",
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DOI = "
doi:10.1016/j.mtcomm.2025.112379",
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abstract = "Experimentally determining the Marshall design test
results for Air voids (Va), Marshall Stability (MS),
and Marshall Flow (MF) in hot mixed asphalt (HMA) is
often expensive, time-consuming, and requires skilled
personnel. To address these challenges, various
traditional machine learning (ML) models have been
employed to optimise the mix design of HMA. However,
their performance is significantly limited by the size
and quality of the training dataset. To address these
limitations, this study employed Generative Adversarial
Networks (GANs) to augment the dataset, which consisted
of 184 samples gathered from four construction projects
in Pakistan. The augmented dataset was then used to
train two advanced ML models: Gene Expression
Programming (GEP) and ensemble learning with stacking
(ELS). A thorough comparison of the models trained on
both original and GAN-augmented datasets was conducted
using a range of statistical metrics to evaluate their
predictive performance. Additionally, sensitivity and
parametric analysis were performed to assess the impact
of input variables on the outputs. The results
demonstrate that GAN-augmented data significantly
improved model accuracy, with GEP and ELS achieving
Rsquared values exceeding 0.93 in all cases.
Furthermore, GEP models provided interpretable
equations for HMA predictions. Sensitivity analysis
identified binder content (Pbpercent) as the most
influential variable, contributing over 55percent to
the variance in Va and MF predictions and 61.56percent
in MS. In contrast, other inputs had minimal influence,
which was consistent with the experimental findings.
This study highlights the potential of advanced ML
techniques and data augmentation in developing reliable
predictive models for Marshall design test results,
advancing efficient HMA design practices",
- }
Genetic Programming entries for
Usama Asif
Waseem Akhtar Khan
Khawaja Atif Naseem
Syed Abdul Sami Rizvi
Citations