TensorFlow-Native Implementation for Crack Detection in Concrete Structures

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dc.contributor.authorAyebare, Memory
dc.contributor.authorChavula, Petros
dc.contributor.authorMugisha, Simon
dc.contributor.authorByamukama, Willbroad
dc.date.accessioned2026-01-03T14:58:24Z
dc.date.available2026-01-03T14:58:24Z
dc.date.issued2025
dc.description.abstractThis paper presents a TensorFlow-native implementation for automated crack detection in concrete structures, addressing the critical need for efficient and objective infrastructure monitoring. Leveraging a Convolutional Neural Network architecture with 24.8 million parameters, the model was trained on a large-scale dataset of 40,000 images, each with a 227x227 RGB resolution. The methodology, incorporating specific framework optimizations and a rigorous training configuration, achieved a remarkable overall classification accuracy of 99.375% on the validation dataset. The model demonstrated balanced performance with precision values of 0.993 and 0.994, recall values of 0.994 and 0.993, and F1-scores of 0.994 and 0.994 for both "No Crack" and "Crack" classes. This high accuracy, coupled with balanced metrics, underscores the model's effectiveness and reliability for practical applications. The proposed solution significantly enhances real-time structural health monitoring systems, mitigating the limitations of traditional manual inspections and facilitating proactive maintenance strategies for concrete infrastructure
dc.identifier.citationAyebare, M., Chavula, P., Mugisha, S., & Willbroad, B. (2025). TensorFlow-Native Implementation for Crack Detection in Concrete Structures. Mesopotamian Journal of Civil Engineering, 2025, 97-108.
dc.identifier.issn3006-1148
dc.identifier.urihttp://hdl.handle.net/20.500.12493/3053
dc.language.isoen
dc.publisherMesopotamian Journal of Civil Engineering
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectConcrete crack detection
dc.subjectConvolutional Neural Network
dc.subjectTensorFlow
dc.subjectStructural health monitoring
dc.subjectDeep learning.
dc.titleTensorFlow-Native Implementation for Crack Detection in Concrete Structures
dc.typeArticle

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