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Optimizing compressive strength prediction using adversarial learning and hybrid regularization.

Tamoor Aziz1, Haroon Aziz2, Srijidtra Mahapakulchai3

  • 1Sirindhorn International Institute of Technology, Thammasat University, Pathum-Thani, Thailand. tamoor.azi@dome.tu.ac.th.

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Summary
This summary is machine-generated.

This study introduces a novel method using generative adversarial networks to predict concrete compressive strength with limited data. The approach enhances sustainable construction by accurately forecasting material performance from recycled aggregates.

Keywords:
Compressive strength predictionConcrete strength estimationGenerative adversarial networksMachine learning in constructionOptimizationSustainable construction materials

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Infrastructure development significantly consumes natural resources and generates construction waste, impacting the environment.
  • Repurposing waste materials for sustainability is challenged by the deterioration of concrete's intrinsic properties when using recycled aggregates.
  • Accurate prediction of concrete compressive strength with recycled aggregates is crucial but data acquisition is costly and time-consuming.

Purpose of the Study:

  • To develop a novel approach for predicting concrete compressive strength using limited data.
  • To leverage generative adversarial networks (GANs) for synthetic data generation to overcome data scarcity.
  • To enhance the accuracy and reliability of compressive strength predictions for sustainable construction materials.

Main Methods:

  • Generative adversarial network (GAN) employed for synthetic data generation.
  • Hybrid training strategy utilizing conventional or heuristic loss functions to prevent model overfitting.
  • Embedding random noise from a multivariate normal distribution into training samples for capturing data variations.
  • Sensitivity analysis to identify key features influencing compressive strength predictions.

Main Results:

  • Recycled coarse aggregate size and water content identified as the most significant predictive features.
  • Superplasticizer, recycled coarse aggregate density, and water absorption ratio showed significant predictive contributions despite low correlations.
  • The proposed GAN-based method outperformed Random Forest, Support Vector Regression, Artificial Neural Network, and Adaptive Boosting.
  • Achieved a mean squared error of 7.97, root mean squared error of 2.82, mean absolute error of 2.13, and a coefficient of determination of 0.96.

Conclusions:

  • The proposed hybrid training GAN method effectively predicts concrete compressive strength with limited data.
  • This technique supports sustainable construction by enabling accurate performance assessment of recycled aggregates.
  • The findings provide a valuable tool for optimizing the use of recycled materials in infrastructure projects.