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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

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A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with

Ludia Eka Feri¹, Jaehun Ahn², Shahrullohon Lutfillohonov³

¹Department of Big Data, Pusan National University, Busan 46241, Korea.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This study introduces a new computational method to analyze permeable pavement clogging. The 3D-IDWGAN model reconstructs pavement microstructures from 2D images, improving design and durability assessments.

Keywords:

3D microstructure reconstruction deep learning generative adversarial networks permeable pavement

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Area of Science:

Civil Engineering
Materials Science
Computational Modeling

Background:

Permeable pavements are increasingly used, but clogging reduces their functionality.
Traditional clogging analysis methods are costly and time-consuming.
There's a need for efficient methods to study permeable pavement design and durability.

Purpose of the Study:

To propose an image-based computational system for analyzing permeable pavement clogging.
To overcome the limitations of traditional physical experiments.
To improve the design and durability of permeable pavements.

Main Methods:

Developed a 3D microstructure reconstruction framework using 3D-IDWGAN with an enhanced gradient penalty.
Input: two-dimensional (2D) image of pavement microstructure.
Output: 3D microstructure image generated via a generative adversarial network (GAN).

Main Results:

The proposed system effectively generates 3D microstructure images.
Achieved improved 3D image generation compared to other GAN methods.
Extracted physical properties from the reconstructed 3D model closely matched those from real pavement samples.

Conclusions:

The 3D-IDWGAN framework offers a cost-effective and time-efficient alternative for permeable pavement clogging analysis.
This computational approach aids in understanding and improving permeable pavement performance.
The method shows promise for enhancing the design and durability of permeable pavement systems.