Authors : Omkar More; Saurabh Patil; Avinash Pawade; Yashodhari Dodmani

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/2e6p8bc9

Scribd : https://tinyurl.com/mjnw8ujk

DOI : https://doi.org/10.38124/ijisrt/25nov715

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : The transition to sustainable construction, utilizing low-carbon materials such as geopolymer concrete (GPC) reinforced with non-corrosive fiber-reinforced polymer (FRP) bars, necessitates advanced, quantitative structural health monitoring (SHM). Current automated crack inspection often relies on traditional machine learning (ML) classification models (e.g., SVM), which, while achieving high accuracy in categorizing failure modes, inherently fail to provide the quantitative parameters (crack width, length, and area) essential for engineering assessment and maintenance prioritization. To address this critical utility gap, this study proposes an enhanced Deep Learning Semantic Segmentation framework: an Attention-based U-Net architecture. This model is specifically designed with a residual encoder and optimized with a hybrid Dice and Focal loss function to counteract extreme class imbalance and enhance the detection of fine, hairline microcracks characteristic of fiber-bridged GPC systems. The framework achieves high segmentation fidelity, evidenced by a mean Intersection over Union (mIoU) score ranging from 85%–95% on complex GPC/FRP crack patterns. This pixel-level accuracy enables the robust post-processing extraction of maximum crack width (Wmax) and total crack length (Ltotal). This methodological shift from qualitative classification to verifiable quantitative segmentation provides the necessary empirical foundation to track damage evolution, assess serviceability limits, and inform predictive maintenance schedules for novel GPC/FRP composites where standard structural codes are currently lacking.

References :

1. B. Shan et al.A stereovision-based crack width detection approach for concrete surface assessment KSCE J. Civ. Eng. (2016)
2. Hassan et al. A review of properties and behaviour of reinforced geopolymer concrete structural elements - A clean technology option for sustainable development J. Clean. Prod. (2020)
3. D. McCann et al.Review of NDT methods in the assessment of concrete and masonry structures NDT & E Int. (2001)
4. G. Li et al.Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine Automat. Constr.(2017)
5. Tomoyuki Yamaguchi, Shingo Nakamura, RyoSaegusa and Shuji Hashimoto, Image-Based Crack Detection for Real Concrete...
6. H. Nakamura, R. Sato. K. Kawamura, A. Miyamoto. Proposal of a crack pattern extraction method from digital image sing...
7. T. Yamaguchi et al.Percolation approach to image-based crack detection
8. Amer Hassan, Mohammed Arif and M. Shariq, Effect of Curing Condition on the Mechanical Properties of Fly Ash based...
9. Amer Hassan, Mohammed Arif, M. Shariq, Use of geopolymer concrete for a cleaner and sustainable environment - A review...
10. Amer Hassan, Mohammed Arif, M. Shariq, Mechanical behaviour and microstructural investigation of geopolymer concrete...
11. Hassan et al.Age-dependent compressive strength and elastic modulus of fly ash-based geopolymer concrete, structural concrete J. Fib.(2020)
12. Hassan et al.Structural performance of ambient cured reinforced geopolymer concrete beams with steel fibres, structural concrete J. Fib.(2020)
13. Influence of Microstructure of Geopolymer Concrete on its Mechanical Properties— A Review (2020)
14. W.T. Zhang, J.Y. Dai, H. Xu, B.C. Sun, Y.L. Du. Distributed fiber optic crack sensor for concrete structures.” Proc. of... Ikhlas Abdel-Qader et al. Analysis of edge detection techniques for crack identification in bridges J. Comput. Civ. Eng. Am. Soc. Civ. Eng. (2003)