CrackNet-VGG: A Deep Learning Framework for Automated Detection of Surface Cracks in Concrete Structures Cover

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CrackNet-VGG: A Deep Learning Framework for Automated Detection of Surface Cracks in Concrete Structures

Applied Computer Systems

Volume 30 (2025): Issue 1 (January 2025)

By: Saiqa Murtaza , Muhammad Imran and Muhammad Rizwan Rashid Rana

Open Access

|Dec 2025

Authors

Saiqa Murtaza

Department of Robotics & Artificial Intelligence, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

Muhammad Imran

dr.imran@szabist-isb.edu.pk

Department of Robotics & Artificial Intelligence, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

Muhammad Rizwan Rashid Rana

rizwanrana315@gmail.com

Department of Robotics & Artificial Intelligence, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

DOI: https://doi.org/10.2478/acss-2025-0020 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683

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Language: English

Page range: 187 - 194

Submitted on: Jul 28, 2025

|

Accepted on: Nov 27, 2025

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Published on: Dec 23, 2025

Published by: Riga Technical University

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Concrete crack detection,

convolutional neural network,

structural health monitoring,

VGG16 architecture

Related subjects:

Computer sciences,

Artificial intelligence,

Information technology,

Project management,

Software development

© 2025 Saiqa Murtaza, Muhammad Imran, Muhammad Rizwan Rashid Rana, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

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