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Buildings
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Research on Durability, Resilience and Stability of Building Structures
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Research on Durability, Resilience and Stability of Building Structures

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1138

Special Issue Editors

Dr. Yang Liu

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Guest Editor
School of Urban Planning and Municipal Engineering, Xi’an Polytechnic University, Xi'an 710048, China
Interests: seismic and control of building structures; engineering materials; geotechnical engineering; disaster prevention & mitigation
Dr. Bo Lu

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Guest Editor
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: underground structure; tunnel engineering; pipe roofing method; soil structure interaction; structural health monitoring; pipelines and trenchless technology
Dr. Songbo Ren

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
Interests: tunnel engineering; durability of engineering structure; seismic performance; fatigue and fracture; progressive destruction; multifractal analysis; refined finite element modeling and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Zhao

E-Mail Website
Guest Editor
Center of Steel Bridge, Zhejiang Institute of Communications, Hangzhou 311112, China
Interests: steel structures and composite structures bridges; high-strength bolt connections; welded connections; steel bridge construction and inspection

Special Issue Information

Dear Colleagues,

Durability, resilience and stability are essential attributes that civil engineering structures possess to ensure their long-term performance. For structures such as marine facilities, bridge tunnel systems, buildings, supporting structures, and underground facilities, durability is not only affected by complex environmental factors (including atmospheric conditions, groundwater, and soil properties) but also by human factors such as construction quality and material characteristics. Therefore, in the design and construction stages, we must fully consider various factors related to durability, toughness, and stability. Throughout the engineering process, low-carbon, eco-friendly, and intelligent materials and construction technologies with excellent durability should be selected to strengthen structural protection and maintenance and ultimately extend the service life of the structure. In addition, by optimizing structural design and construction practices through the improvement of the durability, toughness, and stability of structures, the functional recovery of engineering structures after disasters could be accelerated, thereby mitigating the impact of such events on existing infrastructure. Thus, to advance this field, we launched the project "Durability, Resilience and Stability of Building Structures" in the context of low carbon materials, environmental protection, and intelligent technology. This initiative aims to bring together the expertise of industry leaders, academics, and practitioners in order to drive overall improvements in the performance of building structures. 

Dr. Yang Liu
Dr. Bo Lu
Dr. Songbo Ren
Prof. Dr. Wei Zhao
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

  • structural engineering
  • sustainable design
  • low-carbon, eco-friendly, and intelligent materials
  • durability
  • resilience
  • stability
  • structural protection

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

Published Papers (2 papers)

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Research

25 pages, 4965 KiB  
Article
Towards Selecting an Optimal Bonding Test Method for Rebar–Concrete: Comparison Between Pull-Out Test and Full-Beam Test
by Sisi Chao, Chenghua Li, Jiahong Dong and Ziliang Lu
Buildings 2025, 15(13), 2375; https://doi.org/10.3390/buildings15132375 - 7 Jul 2025
Viewed by 224
Abstract
There are many methods for evaluating the bond behavior between rebar and concrete. For certain experimental purposes, selecting the ideal method for testing the rebar–concrete bonding properties is often a controversial problem. The most representative single-end pull-out test method and the full-beam test [...] Read more.
There are many methods for evaluating the bond behavior between rebar and concrete. For certain experimental purposes, selecting the ideal method for testing the rebar–concrete bonding properties is often a controversial problem. The most representative single-end pull-out test method and the full-beam test method were applied in this work to conduct bonding tests between rebar and concrete. Considering the influence of the concrete strength, bonding length, stirrup, and rebar slotting, these two testing strategies are compared and analyzed in terms of the specimen failure mode, bonding strength, bond–slip curve, and rebar stress distribution. Suggestions are offered regarding the selection of an appropriate method for evaluating the bond behavior between rebar and concrete based on an comparative analysis of the two tested approaches. The results presented herein provide a basis for the preparation of relevant test method standards. Full article
(This article belongs to the Special Issue Research on Durability, Resilience and Stability of Building Structures)
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20 pages, 3597 KiB  
Article
Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning
by Jitao Zhao, Miaomiao Zhu, Lidan Xu, Ming Chen and Mingfang Shi
Buildings 2025, 15(11), 1908; https://doi.org/10.3390/buildings15111908 - 1 Jun 2025
Viewed by 449
Abstract
To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). [...] Read more.
To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. Finally, the prediction accuracy of the ML model was further verified by comparing it with the domestic and foreign design codes. The results manifest that the shear capacity exhibits a strong positive correlation with the beam width and effective height. Compared to the ANN model, the XGBoost-based prediction model achieves determination coefficients (R2) of 0.999 and 0.879 for the training and test sets, respectively, indicating superior predictive accuracy. Furthermore, the shear capacity calculations from design codes show significant variability, demonstrating the superior predictive capability of ML algorithms. These findings offer a guideline for the design and implementation of FRP reinforcement in actual bridge engineering. Full article
(This article belongs to the Special Issue Research on Durability, Resilience and Stability of Building Structures)
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