Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.1
Journals
Materials
Special Issues
Repair and Strengthening of Existing Reinforced Concrete Structures (Second Edition)
materials-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Materials Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Repair and Strengthening of Existing Reinforced Concrete Structures (Second Edition)

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 3874

Special Issue Editor

Dr. Andreas Lampropoulos

E-Mail Website
Guest Editor
Assistant Professor, Laboratory of Reinforced Concrete, School of Civil Engineering, National Technical University of Athens, 15772 Athens, Greece
Interests: novel construction materials; seismic strengthening of existing structures; sustainability and resilience engineering; reinforced concrete
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent devastating earthquakes have highlighted the urgent need for the structural upgrade of existing Reinforced Concrete (RC) structures that are prone to structural failures and collapses.

At the same time, the need for immediate actions to mitigate the dramatic consequences of the climate emergency is a key priority, and novel cost-effective solutions are required for the enhancement of energy efficiency and the thermal insulation of existing structures using sustainable resources.

This second volume of the Special Issue “Repair and Strengthening of Existing Reinforced Concrete Structures” is focused on the development of novel repair and strengthening techniques using sustainable materials for the enhancement of structural performance and energy efficiency.

Authors are welcome to submit original contributions in the following areas:

  • Low-cost repair and strengthening techniques.
  • The development of novel repair and strengthening techniques using sustainable materials.
  • Enhancing the structural performance, resilience, and longevity of existing RC structures.
  • Structural and energy upgrade of existing RC structures.
  • Multi-hazard protection of existing RC structures.

Dr. Andreas Lampropoulos
Guest Editor

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. Materials 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

  • repair and strengthening
  • reinforced concrete structures
  • structural strengthening
  • sustainability
  • energy upgrade
  • multi-hazard protection

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (3 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

16 pages, 2857 KiB  
Article
Fatigue Life Prediction of FRP-Strengthened Reinforced Concrete Beams Based on Soft Computing Techniques
by Zhimei Zhang and Xiaobo Wang
Materials 2025, 18(2), 230; https://doi.org/10.3390/ma18020230 - 7 Jan 2025
Cited by 1 | Viewed by 854
Abstract
This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets [...] Read more.
This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams. Full article
(This article belongs to the Special Issue Repair and Strengthening of Existing Reinforced Concrete Structures (Second Edition))
►▼ Show Figures

Figure 1

15 pages, 5070 KiB  
Article
Numerical Investigation of Reinforced Concrete (RC) Columns Strengthened with Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) Jackets
by Andreas Lampropoulos, Spyridon Paschalis, Ourania Tsioulou and Stephanos Dritsos
Materials 2024, 17(14), 3380; https://doi.org/10.3390/ma17143380 - 9 Jul 2024
Cited by 2 | Viewed by 1152
Abstract
The strengthening of existing columns using additional reinforced concrete (RC) jackets is one of the most popular techniques for the enhancement of a column’s stiffness, load-bearing capacity and ductility. Important parameters affecting the effectiveness of this method are the strength of the additional [...] Read more.
The strengthening of existing columns using additional reinforced concrete (RC) jackets is one of the most popular techniques for the enhancement of a column’s stiffness, load-bearing capacity and ductility. Important parameters affecting the effectiveness of this method are the strength of the additional concrete, concrete shrinkage and the connection between the old and the new concrete. In this study, the application of Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) jackets for the structural upgrade of RC columns has been examined. Extensive numerical studies have been conducted to evaluate the effect of parameters such as the thickness of the jacket, concrete shrinkage and the addition of steel bars, and comparisons have been made with conventional RC jackets. The results of this study indicate that the use of UHPFRC can considerably improve the strength and the stiffness of existing reinforced concrete columns. The combination of UHPFRC and steel bars in the jacket leads to the most effective strengthening technique as a significant enhancement in the stiffness and the ultimate load capacity has been achieved. Full article
(This article belongs to the Special Issue Repair and Strengthening of Existing Reinforced Concrete Structures (Second Edition))
►▼ Show Figures

Figure 1

15 pages, 4963 KiB  
Article
Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams
by Sheng Zheng, Tianyu Hu and Yong Yu
Materials 2024, 17(9), 1957; https://doi.org/10.3390/ma17091957 - 23 Apr 2024
Cited by 2 | Viewed by 1036
Abstract
This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and [...] Read more.
This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and XGBoost algorithms. Secondly, the most suitable model for predicting CCS was identified based on the evaluation metrics and compared with the codes and the researcher’s model. Finally, a parametric study based on SHapley Additive exPlanations (SHAP) was carried out, and the following conclusions were obtained: XGBoost is best-suited for the prediction of CCS and codes, and researchers’ model accuracy needs to be improved and suffers from over or conservative estimation. The contributions of the concrete to the shear force and the yield strength of the reinforcement are the most important parameters for the CCS, where the shear force at the onset of CCS is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement. Full article
(This article belongs to the Special Issue Repair and Strengthening of Existing Reinforced Concrete Structures (Second Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop