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Infrastructures
Special Issues
Advances in Artificial Intelligence for Infrastructures
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Advances in Artificial Intelligence for Infrastructures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 429

Special Issue Editors

Prof. Dr. Ahmed A. Ibrahim

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Idaho, 875 Perimeter Dr. MS 1022, Moscow, ID 83844, USA
Interests: non-linear behavior and modeling of reinforced and prestressed concrete elements; mechanistic damage modeling of reinforced concrete elements under blast loading; experimental testing of reinforced concrete members; field testing of highway bridges; application of advanced materials in strengthening of structures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohamed ElGawady

E-Mail Website
Guest Editor
Department of Civil Engineering, Missouri University of Science and Technology, Rolla, MO, USA
Interests: seismic behavior of unreinforced masonry (URM) structures; application of fiber reinforced polymers (FRP) in strengthening and repair of masonry/reinforced concrete structures; seismic behavior of reinforced concrete bridges; damage-free bridge columns; segmental construction; rocking mechanics and the use of sustainable materials in seismic prone regions

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a potent instrument that may assist humans in comprehending and resolving challenging issues. AI offers a special set of tools for innovating infrastructure, enabling quicker, more automated decision-making and better network efficiency. Specifically, AI is able to manage power usage, track changes in traffic patterns, and keep an eye on security thresholds. Large volumes of data can be swiftly processed and analyzed by AI, which allows it to make judgments faster than a human operator could. Cities are becoming safer, more connected, and more efficient thanks to AI-driven infrastructure innovation. In the realm of infrastructure innovation, artificial intelligence (AI) is a priceless tool that may greatly aid cities striving to uphold their status quo and enhance their standard of living. This Special Issue of Advances in Artificial Intelligence for Infrastructures covers a wide range of topics of the application of AI in the structural and transportation engineeering field.   

Prof. Dr. Ahmed A. Ibrahim
Prof. Dr. Mohamed ElGawady
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • neural network
  • infrastructure

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Published Papers (1 paper)

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Research

18 pages, 5677 KiB  
Article
Computer Vision-Based Concrete Crack Identification Using MobileNetV2 Neural Network and Adaptive Thresholding
by Li Hui, Ahmed Ibrahim and Riyadh Hindi
Infrastructures 2025, 10(2), 42; https://doi.org/10.3390/infrastructures10020042 - 18 Feb 2025
Viewed by 213
Abstract
Concrete is widely used in different types of buildings and bridges; however, one of the major issues for concrete structures is crack formation and propagation during its service life. These cracks can potentially introduce harmful agents into concrete, resulting in a reduction in [...] Read more.
Concrete is widely used in different types of buildings and bridges; however, one of the major issues for concrete structures is crack formation and propagation during its service life. These cracks can potentially introduce harmful agents into concrete, resulting in a reduction in the overall lifespan of concrete structures. Traditional methods for crack detection primarily hinge on manual visual inspection, which relies on the experience and expertise of inspectors using tools such as magnifying glasses and microscopes. To address this issue, computer vision is one of the most innovative solutions for concrete cracking evaluation, and its application has been an area of research interest in the past few years. This study focuses on the utilization of the lightweight MobileNetV2 neural network for concrete crack detection. A dataset including 40,000 images was adopted and preprocessed using various thresholding techniques, of which adaptive thresholding was selected for developing the crack evaluation algorithm. While both the convolutional neural network (CNN) and MobileNetV2 indicated comparable accuracy levels in crack detection, the MobileNetV2 model’s significantly smaller size makes it a more efficient selection for crack detection using mobile devices. In addition, an advanced algorithm was developed to detect cracks and evaluate crack widths in high-resolution images. The effectiveness and reliability of both the selected method and the developed algorithm were subsequently assessed through experimental validation. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)
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