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Advances in Methods for Performance Characterization and Prediction of Reinforced...
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Advances in Methods for Performance Characterization and Prediction of Reinforced Concrete—2nd Edition

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1641

Special Issue Editors

Dr. Jiandong Huang

E-Mail Website
Guest Editor
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
Interests: AI prediction; green civil materials
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaolong Ren

E-Mail Website
Guest Editor
School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China
Interests: fatigue and fracture; data-driven method; green civil materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reinforced concrete is essential for buildings and construction. However, conventional concepts and approaches to the performance characterization and prediction of reinforced concrete are not always appropriate for the current requirements of durability and toughness in civil engineering. Notwithstanding the enormous efforts of academic researchers and the industry, a general solution for performance characterization under exceptional conditions (e.g., dynamic loads, freeze-thawing) and high-efficiency performance prediction (e.g., big data, uncertainty, and self-adaption) needs to be discussed further. Several relevant studies have already been published in the first volume of this Special Issue. You can find them at the following link: [https://www.mdpi.com/journal/buildings/special_issues/0F862UJ9JK]. This Special Issue aims to collect both original research and review articles regarding innovative methods for the performance characterization and prediction of reinforced concrete materials and structures.

Potential topics include but are not limited to the following:

  • Multiscale analysis;
  • Thermal analysis;
  • Machine learning methods;
  • Data-driven methods;
  • Uncertainty quantification;
  • Special loading conditions (dynamic loads, freeze-thawing, etc.).

Dr. Jiandong Huang
Dr. Jiaolong Ren
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. 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

  • reinforced concrete
  • durability
  • mechanical property
  • performance characterization
  • performance prediction
  • multiscale analysis
  • thermal analysis
  • data-driven method
  • special loading condition

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Related Special Issue

Published Papers (1 paper)

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Research

40 pages, 7476 KiB  
Article
Soft Computing Techniques to Model the Compressive Strength in Geo-Polymer Concrete: Approaches Based on an Adaptive Neuro-Fuzzy Inference System
by Zhiguo Chang, Xuyang Shi, Kaidan Zheng, Yijun Lu, Yunhui Deng and Jiandong Huang
Buildings 2024, 14(11), 3505; https://doi.org/10.3390/buildings14113505 - 1 Nov 2024
Cited by 1 | Viewed by 1291
Abstract
Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute [...] Read more.
Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R2, VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R2 of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R2 of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications. Full article
(This article belongs to the Special Issue Advances in Methods for Performance Characterization and Prediction of Reinforced Concrete—2nd Edition)
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