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Applied Sciences
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Application of Intelligent Materials in Inspection, Repair and Reinforcement of...
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Application of Intelligent Materials in Inspection, Repair and Reinforcement of Infrastructure

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 3964

Special Issue Editors

Dr. Xu Jiang

E-Mail Website
Guest Editor
Department of Bridge Engineering, Tongji University, Shanghai 200092, China
Interests: evaluation; rehabilitation; steel bridges; SMAs; CFRP; smart materials
Dr. Xuhong Qiang

E-Mail Website
Guest Editor
Department of Building Engineering, Tongji University, Shanghai 200092, China
Interests: steel structures; high-performance materials; SMAs; fire resistance; repair

Special Issue Information

Dear Colleagues,

Civil engineering infrastructures often face great demands for strengthening or repairing during their service lives, especially due to fatigue and corrosion damages or exposed to fire and earthquake disaster. Nowadays, intelligent materials such as shape-memory alloys, smart materials, and fiber-reinforced composites have great potential in the inspecting, repairing and upgrading of infrastructures, and in turn for the enhancement of their long-term performance. Taking Fe–Mn–Si alloys as an example, the martensitic transformation and its reverse transformation which produces considerable recovery stress (300~500 MPa), can be utilized as prestress for the local repairing of fatigue cracks in orthotropic steel bridge decks and also global upgrading of down-wrapped concrete/steel/composite beams. In this Special Issue, in comparison with traditional inspection and strengthening methods, mechanisms, techniques and applications of intelligent materials on the rehabilitation of infrastructures will be introduced and classified in detail.

Dr. Xu Jiang
Dr. Xuhong Qiang
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • steel bridge
  • intelligent material
  • fatigue
  • corrosion
  • inspection
  • rehabilitation

Published Papers (2 papers)

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Research

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21 pages, 9208 KiB  
Article
A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns
by Xu Jiang, Hao Qi, Xuhong Qiang, Bosen Zhao and Hao Dong
Appl. Sci. 2023, 13(15), 8883; https://doi.org/10.3390/app13158883 - 01 Aug 2023
Cited by 2 | Viewed by 1165
Abstract
As one of the most common forms of corrosion in the marine environment, pitting corrosion can have a detrimental impact on the ultimate strength of steel columns. Pitting pits are usually covered by corrosion products, and the detection of pitting is very difficult, [...] Read more.
As one of the most common forms of corrosion in the marine environment, pitting corrosion can have a detrimental impact on the ultimate strength of steel columns. Pitting pits are usually covered by corrosion products, and the detection of pitting is very difficult, so how to effectively identify random pitting corrosion on steel columns has become a very vital issue. In this paper, a deep-learning-based pitting damage determination method for steel columns is investigated by combining numerical simulation and theoretical analysis, which was validated by experimental results. First, a multi-parameter localized pitting corrosion model was proposed that considered the pitting corrosion randomness in time and space distribution. Second, the relationship between the ultimate strength and corrosion rate of steel columns was analyzed. Finally, a steel column damage determination framework was constructed based on the convolutional neural network. Results showed that the ultimate strength and corrosion rate developed different trends in various corrosion regions, and a damage determination accuracy of 90.2% could be achieved by the neural network after training, which satisfied the practical engineering requirements. This study lays the groundwork for further application of deep learning to the research on the pitting damage to steel structures. Full article
(This article belongs to the Special Issue Application of Intelligent Materials in Inspection, Repair and Reinforcement of Infrastructure)
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Review

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22 pages, 12880 KiB  
Review
Research Progress and Applications of Fe-Mn-Si-Based Shape Memory Alloys on Reinforcing Steel and Concrete Bridges
by Xuhong Qiang, Yapeng Wu, Yuhan Wang and Xu Jiang
Appl. Sci. 2023, 13(6), 3404; https://doi.org/10.3390/app13063404 - 07 Mar 2023
Cited by 16 | Viewed by 2360
Abstract
In civil engineering, beam structures such as bridges require reinforcement to increase load-bearing capacity and extend service life due to damage, aging, and capacity degradation under long-time services and disasters. The utilization of Fe-based shape memory alloys (Fe-SMA) to reinforce structures has been [...] Read more.
In civil engineering, beam structures such as bridges require reinforcement to increase load-bearing capacity and extend service life due to damage, aging, and capacity degradation under long-time services and disasters. The utilization of Fe-based shape memory alloys (Fe-SMA) to reinforce structures has been proven efficient and reliable, and the recovery stress of activated Fe-SMA can satisfy the reinforcement requirements. This article overviews the material characteristics and mechanical properties of Fe-SMA. Furthermore, the principle of thermal activation for reinforcing beams using Fe-SMA is described. On this basis, the joining methods between Fe-SMA members and reinforced components are reviewed, and the existing reinforcement research and applications are analyzed for steel and concrete beams. Finally, given the current shortcomings, this paper puts forward the perspectives that need to be studied to promote Fe-SMA’s reinforcement application in civil engineering. Full article
(This article belongs to the Special Issue Application of Intelligent Materials in Inspection, Repair and Reinforcement of Infrastructure)
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