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Advanced Methods for Estimating Mechanical Properties, Constructional and Performance of Fiber-Reinforced Concrete Materials

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 74

Special Issue Editor


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Guest Editor
Department of Building Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gdansk, Poland
Interests: machine learning; data science; data-driven methods; optimization; seismic design; innovative building materials; retrofitting buildings

Special Issue Information

Dear Colleagues,

Nowadays, artificial intelligence, specifically machine learning methods, can help to provide a surrogate prediction model for estimating the mechanical properties, performance and stability of concrete materials such as high-performance concrete, ultra-high-performance concrete, and alkali-activated ultra-high-performance concrete. Moreover, these data-driven techniques have the potential to be used by civil engineer/designers in various types of structures, bridges, and infrastructures. However, understanding numerous fundamental mechanisms in this field continues to pose a challenge, and the lack of industrial applications remains prevalent, highlighting the gap between theoretical research and real-world implementation in this area.

This Special Issue aims to focus on recent advancements, developments, and emerging trends related to machine learning methods for predicting mechanical properties of both fresh and hardened-state concrete material, long-term performance on the compressive or (tensile) strength of concrete material, and durability of eco-friendly cementitious materials with reduced carbon emissions. We welcome submissions of original research papers and review articles. Topics of interest include, but are not limited to:

  • Innovative Ensemble Machine Learning Model;
  • Data-Driven Techniques;
  • High-Performance Concrete (HPC);
  • Ultra-High-Performance Concrete (UHPC);
  • Alkali-Activated Ultra-High-Performance Concrete (AA-UHPC);
  • Green Concrete;
  • Advancements in Concrete Materials;
  • Fiber-Reinforced Concrete;
  • Fiber-Reinforced Concrete Beam;
  • Recycling and Upcycling in Building Materials;
  • Smart and Responsive Materials for Building;
  • Biocompatible and Biodegradable Building Materials;
  • Low-Carbon Footprint Materials;
  • Nano and Micro Technologies in Construction Materials;
  • Fire-Resistant and Flame-Retardant Concrete Materials;
  • Natural-Fiber-Reinforced Composites;
  • 3D Printing in Construction;
  • Durability and Longevity of Concrete Materials.

Dr. Farzin Kazemi
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

  • machine learning method
  • data science
  • data-driven techniques
  • mechanical properties
  • concrete materials
  • fiber-reinforced concrete
  • fiber-reinforced concrete beam
  • optimization algorithms
  • construction and building materials

Published Papers

This special issue is now open for submission.
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