Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.1
Journals
Buildings
Special Issues
Long-Term Durability Performance of Steel-Reinforced Concrete and Steel-Fiber-Reinforced Concrete
share announcement

Long-Term Durability Performance of Steel-Reinforced Concrete and Steel-Fiber-Reinforced Concrete

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1246

Special Issue Editors

Dr. Renjie Wu

E-Mail
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: stainless-clad steel rebar; FRP rebar; fiber-reinforced concrete; durability; corrosion; mechanical properties
Prof. Dr. Xiaoping Zhong

E-Mail
Guest Editor
College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China
Interests: stainless-clad steel rebar; FRP rebar; fiber-reinforced concrete; durability; corrosion; mechanical properties
Dr. Yixue Zhang

E-Mail
Guest Editor
College of Civil Engineering and Architecture, Ningbo Tech University, Ningbo 315100, China
Interests: high-performance concrete; creep; durability; bridge engineering
Dr. Xipeng Wang

E-Mail
Guest Editor
Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
Interests: corrosion; mechanical properties; buckling of offshore pipelines; mechanics of composite structures; machine learning

Special Issue Information

Dear Colleagues,

The long-term durability of steel-reinforced concrete in marine environments is fundamental to the sustainability and resilience of coastal and offshore infrastructure. With growing demand stemming from global population increase and urbanization, ensuring the long service life of these structures against chloride-induced corrosion is a critical challenge. Durability in this context specifically denotes the capability of the concrete and its reinforcement system to resist chemical attack and environmental stress while retaining its key mechanical properties over an extended period. The service life of a marine structure is largely determined by the materials used—for instance, the concrete composition and the type of reinforcement—together with the design and construction techniques applied. Neglecting these factors can lead to premature degradation, resulting in significant safety hazards, economic costs, and environmental consequences.

For this Special Issue, we welcome the submission of high-quality original research articles, case studies, ongoing project reports, and review papers that address the long-term durability of concrete and steel structures in marine conditions. We encourage contributions that focus on innovative materials, including stainless-clad steel rebar, FRP rebar, and fiber-reinforced concrete. Of relevance are their degradation mechanisms, corrosion processes, associated mechanical property evolution, and novel design or construction methodologies developed to enhance durability.

Dr. Renjie Wu
Prof. Dr. Xiaoping Zhong
Dr. Yixue Zhang
Dr. Xipeng Wang
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 250 words) can be sent to the Editorial Office for assessment.

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. Buildings 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 2600 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

  • stainless-clad steel rebar
  • FRP rebar
  • fiber-reinforced concrete
  • durability
  • corrosion
  • mechanical properties

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 2273 KB  
Article
Structural Damage Identification Method and Experimental Verification Based on Multi-Head Convolutional Autoencoder
by Shuai Jiang, Jun Zhang, Meng Wang, Xinting Chen and Qiang Li
Buildings 2026, 16(5), 954; https://doi.org/10.3390/buildings16050954 - 28 Feb 2026
Viewed by 359
Abstract
To address the prevalent challenges of limited labelled data and indistinct damage features in the domain of damage identification, an unsupervised damage identification method has been developed. The method is based on a multi-head convolutional autoencoder, which introduces multi-scale convolution kernels to extract [...] Read more.
To address the prevalent challenges of limited labelled data and indistinct damage features in the domain of damage identification, an unsupervised damage identification method has been developed. The method is based on a multi-head convolutional autoencoder, which introduces multi-scale convolution kernels to extract key features from structural vibration response data. The method combines vibration signal reconstruction with difference analysis, thereby enabling automatic identification of structural damage. The validity of the proposed method is confirmed through the execution of a concrete beam hammering vibration test. The multi-head convolutional autoencoder demonstrates a high degree of accuracy in the reconstruction of vibration signals and the subsequent identification of damage. Furthermore, the multi-head one-dimensional convolution structure has been shown to outperform traditional one-dimensional convolution structures with regard to both detection accuracy and sensitivity. It is asserted that this method has the capacity to serve as a valuable reference point for the intelligent analysis of engineering Structural Health Monitoring data. Full article
(This article belongs to the Special Issue Long-Term Durability Performance of Steel-Reinforced Concrete and Steel-Fiber-Reinforced Concrete)
►▼ Show Figures

Figure 1

23 pages, 5850 KB  
Article
Durability Assessment of Marine Steel-Reinforced Concrete Using Machine Vision: A Case Study on Corrosion Damage and Geometric Deformation in Shield Tunnels
by Yanzhi Qi, Xipeng Wang, Zhi Ding and Yaozhi Luo
Buildings 2026, 16(1), 107; https://doi.org/10.3390/buildings16010107 - 25 Dec 2025
Viewed by 496
Abstract
The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the [...] Read more.
The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article
(This article belongs to the Special Issue Long-Term Durability Performance of Steel-Reinforced Concrete and Steel-Fiber-Reinforced Concrete)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop