Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning for Crack Detection on Concrete Structures by Vss

Machine Learning for Crack Detection on Concrete Structures by Vss

Swaroop, A. H. L.
Dey, Subhashish
Saathvik, R. Noga

Paperback

General Law

ISBN10: 6208451485
ISBN13: 9786208451486
Publisher: LAP Lambert Academic Publishing
Published: Jun 17 2025
Pages: 88
Weight: 0.28
Height: 0.21 Width: 6.00 Depth: 9.00
Language: English
Concrete structures and the associated cracking represent a persistent challenge within the fields of construction and civil engineering, a dilemma that has persisted for decades. Generally, cracks arise from the expansion and contraction of materials, which can lead to various forms of damage within buildings. Engineers typically address these damages and irregularities through manual inspections or by employing predictive models developed using machine learning techniques, thereby facilitating a comprehensive assessment of the structural health of the edifices. This research aims to implement tools such as the VGG 16 network model alongside other machine learning methodologies to identifycracks in concrete structures. We propose a model that integrates Convolutional Neural Networks (CNN) with a VGG-based architecture. For image processing and segmentation, we have employed the gradient boosting algorithm. The datasets utilized in this study were sourced from the Kaggle platform, and the Hugging Face Transformers library was leveraged for implementation. To assess the performance of the developed models, metrics including precision, accuracy, recall, and F1 score were employed.

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