Special Issue "Concrete in Structural Engineering: Fabrication and Mechanical Behavior"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Seong Tae Yi
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Inha Technical College, Incheon, Korea
Interests: concrete and composite materials; fracture mechanics; finite element analysis; reinforced concrete design; seismic qualification
Prof. Dr. Jong Wan Hu
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea
Interests: seismic design; smart structures; concrete materials; reinforced concrete; structural experiments; performance evaluation; finite element analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to publish papers that advance the field of concrete materials and structures through the approach of numerical analyses and experimental tests. The proposed approaches should include new or enhanced insights into construction for reinforced concrete, pre-stressed concrete, cementitious material fabrication, and mechanical behavior of concrete members.

Aware of the comprehensiveness of the suggested topic, we encourage you to send manuscripts containing scientific findings within the broad field of concrete research, which can be combined into the following topics: material effects, material behaviors, structural analysis, seismic design, earthquake engineering, structural monitoring, composite structures, lab and field testing, hazard reduction systems, and smart structures. Both theoretical and practice-oriented papers, including case studies and reviews, are also encouraged.

Prof. Dr. Seong Tae Yi
Prof. Dr. Jong Wan Hu
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 papers will be 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 2000 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

  • nano concrete
  • FRP concrete
  • self-healing concrete
  • multi-functional concrete
  • reinforced concrete
  • pre-stressed concrete
  • composite materials
  • cementitious materials
  • concrete fabrication
  • mechanical behavior
  • concrete design
  • concrete test
  • fracture mechanics
  • concrete frame (building)

Published Papers (2 papers)

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Research

Article
Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP
Materials 2021, 14(17), 4861; https://doi.org/10.3390/ma14174861 - 26 Aug 2021
Viewed by 327
Abstract
In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. [...] Read more.
In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs. Full article
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Article
A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns
Materials 2021, 14(7), 1792; https://doi.org/10.3390/ma14071792 - 05 Apr 2021
Viewed by 579
Abstract
Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction [...] Read more.
Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames. Full article
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