research-article

Deep learning‐based segmentation model for permeable concrete meso‐structures

Authors:

Zhongrong ZhouAuthors Info & Claims

Computer‐Aided Civil and Infrastructure Engineering, Volume 39, Issue 23

Pages 3626 - 3645

https://doi.org/10.1111/mice.13300

Published: 17 November 2024 Publication History

Abstract

The meso‐structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso‐structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso‐structures of pervious concrete, a method utilizing deep learning image semantic segmentation techniques is proposed in this study. First, based on the classical deep learning model, three models, namely, Res‐UNet, ED‐SegNet, and G‐ENet, are proposed for recognizing pervious concrete meso‐structure using deep learning image semantic segmentation techniques. These models introduce a residual module, a hybrid loss function, and a differential recognition branching structure to enhance the ability to recognize detailed information within pervious concrete meso‐structure and small targets. Second, the respective recognition performances of these methods on the meso‐structure of pervious concrete were thoroughly analyzed by experiment. The results indicate that the proposed three recognition methods for recognizing the meso‐structure of permeable concrete outperform conventional techniques not only in terms of efficiency but also in recognition accuracy and the ability to distinguish and identify aggregates, pores, and cement binders. In terms of comprehensive recognition effectiveness, the Res‐UNet model outperforms, followed by ED‐SegNet and G‐ENet. Furthermore, the computational efficiency of these three recognition methods meets the requirements of engineering applications.

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Cited By

Lin CAbe SZheng SLi XChun P(2025)A structure‐oriented loss function for automated semantic segmentation of bridge point cloudsComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1342240:6(801-816)Online publication date: 13-Feb-2025
https://dl.acm.org/doi/10.1111/mice.13422
Wang DZheng JGao LChen SZhong JMiao Y(2025)Evaluation of aggregate distribution uniformity using Vision Mamba-based dual networks for concrete aggregate segmentationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.126076266:COnline publication date: 25-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.126076

Index Terms

Deep learning‐based segmentation model for permeable concrete meso‐structures
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
        Object detection
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image Computer-Aided Civil and Infrastructure Engineering

Computer-Aided Civil and Infrastructure Engineering Volume 39, Issue 23

1 December 2024

162 pages

EISSN:1467-8667

DOI:10.1111/mice.v39.23

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© 2024 Computer‐Aided Civil and Infrastructure Engineering.

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John Wiley & Sons, Inc.

United States

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Published: 17 November 2024

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Cited By

Lin CAbe SZheng SLi XChun P(2025)A structure‐oriented loss function for automated semantic segmentation of bridge point cloudsComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1342240:6(801-816)Online publication date: 13-Feb-2025
https://dl.acm.org/doi/10.1111/mice.13422
Wang DZheng JGao LChen SZhong JMiao Y(2025)Evaluation of aggregate distribution uniformity using Vision Mamba-based dual networks for concrete aggregate segmentationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.126076266:COnline publication date: 25-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.126076

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