Enhancing the reliability of building crack detection using convolutional neural networks via leveraging robust dataset design

  • Sohanur Rahman orcid

    Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
  • Md. Masudur Rahman orcid

    Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
  • Apurba Adikary orcid

    Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
  • Mehedi Hasan Talukder orcid

    Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
  • Minoru W. Yoshida orcid

    Department of Information System Creation, Kanagawa University, Yokohama 221-8686, Japan
Article ID: 4163
DOI: https://doi.org/10.59400/be4163
Keywords: structural maintenance, convolutional neural networks, dataset labeling, performance evaluation, structural health monitoring, comparative analysis, image classification, building inspection

Abstract

The detection of cracks is important in the maintenance of structures like concrete and brick walls, because the appearance of cracks is considered an initial sign of deterioration of structures, ensuring the safety and durability of structures. Traditionally, crack detection is performed by a maintenance engineer manually, which is laborious and time-consuming. Structural maintenance has seen the emergence of automated crack detection methods as a major goal. Convolutional neural network (CNN)-based methods have been superior to other existing methods. But they are not always good in different environments, like shadows, colour changes, or noise, and only work well if the training data is labelled correctly. Thus, CNN-based crack detection requires high-quality labelled datasets. In this research, we assembled comprehensive datasets (captured and online) and employed them in CNN-based techniques (e.g., AlexNet, ResNet-50, GoogLeNet, and VGG16), followed by a comparative analysis to evaluate their performance in structural maintenance. In comparing the performance of the AlexNet, ResNet-50, GoogLeNet, and VGG16 models for crack detection in buildings, ResNet-50 emerged as the top-performing model. All four models achieved high accuracy; however, ResNet-50 consistently demonstrated superior precision, recall, and F1-score. With a test accuracy of 99.88% for ResNet-50, 99.56% for GoogLeNet, 99.25% for VGG16, and 95.31% for AlexNet, ResNet-50 proved more adept at interpreting complex data patterns and minimizing classification errors. This highlights ResNet-50’s stronger ability to enhance classification performance, positioning it as a preferred model for structural crack identification.

Published
2026-06-17
How to Cite
Rahman, S., Rahman, M. M., Adikary, A., Talukder, M. H., & Yoshida, M. W. (2026). Enhancing the reliability of building crack detection using convolutional neural networks via leveraging robust dataset design. Building Engineering, 4(2). https://doi.org/10.59400/be4163
Issue
Vol. 4 No. 2 (2026)
Section
Article

Copyright (c) 2026 Sohanur Rahman, Md. Masudur Rahman, Apurba Adikary, Mehedi Hasan Talukder, Minoru W. Yoshida

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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