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Innovative Research Applied to Building Structures: From Materials Development to...
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Innovative Research Applied to Building Structures: From Materials Development to Structural Application

A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 4879

Editors

Dr. Chong Rong

E-Mail Website
Guest Editor
School of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, China
Interests: engineering structural resilience; structural repair and renovation; structural optimization design; composite structure; energy storage engineering structure; intelligent variable structure; intelligent disaster mitigation and prevention
Dr. Marco Carlo Rampini

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Interests: structural engineering; strengthening and retrofitting; high-performance cement-based materials; fiber-reinforced concrete (FRC); fabric-reinforced cementitious matrix (FRCM) composites; distributed fiber optic sensors; materials and structural testing; bridge engineering and monitoring
Dr. Liangkun Wang

E-Mail Website
Guest Editor
College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: structural control; dynamic vibration absorber; signal processing; deep learning; earthquake mitigation
Dr. Yue Liu

E-Mail Website
Guest Editor
UniSA STEM, University of South Australia, Adelaide, SA 5000, Australia
Interests: low carbon concrete; cement composites; waste recycling; self-healing concrete; CO2 curing; biochar concrete
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Chen

E-Mail Website
Guest Editor
School of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, China
Interests: seismic resistance and isolation of engineering structures; composite material reinforcement; novel materials and innovative structures

Special Issue Information

Dear Colleagues,

In civil engineering, frontier areas such as innovative building materials, new structural systems, and renovation technologies are developing rapidly and require validation through real-world case studies and pilot projects. These include low-carbon concrete, self-healing concrete, high-performance composite components, high seismic-resilient composite structures, intelligent detection and reinforcement technologies, renovation and upgrading of old buildings, and smart disaster prevention. These technologies in civil engineering not only enhance the strength, durability, and seismic performance of structures but also promote the sustainable development of the industry. Furthermore, they ensure the safety of existing buildings, optimize urban functions, and strengthen the engineering capacity to withstand natural disasters. Case studies of recent applications demonstrate how these innovations perform under real-world conditions, offering valuable insights for practical implementation.

This Special Issue aims to provide an open forum to discuss the various novel technologies in civil engineering. Topics of interest include, but are not limited to, material development (e.g., low carbon concrete, cement composites, waste recycling,  high-performance materials), component design (e.g., high performance composite structural component, high performance damper, low carbon recycled concrete component, reinforcement technology), structural design (e.g., novel structural system, seismic isolation and damping, structural repair, upgrading and resilience), applications of artificial intelligence (e.g. intelligent design, inspection and monitoring, operation and maintenance), as well as field validation, experimental testing, and case studies demonstrating the application of these technologies in real-world projects.

Dr. Chong Rong
Dr. Marco Carlo Rampini
Dr. Liangkun Wang
Dr. Yue Liu
Dr. Xuan Chen
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • innovative building materials
  • structural component design
  • structural system
  • artificial intelligence technology
  • inspection and monitoring
  • retrofitting and upgrading
  • architectural structural resilience
  • application to case studies

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

Published Papers (6 papers)

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Research

26 pages, 5946 KB  
Article
Intelligent Recognition and Restoration of Mural Damage Based on DeepLabv3 and Stable Diffusion
by Chong Rong, Dashuai Yang, Wenkai Tian, Yi Tao, Qiuwei Wang and Peng Wang
Buildings 2026, 16(10), 2012; https://doi.org/10.3390/buildings16102012 - 20 May 2026
Viewed by 475
Abstract
Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced [...] Read more.
Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced peeling, due to long-term exposure to environmental factors and geological changes. As the progressive deterioration of these murals hastens the loss of mural value, professional assessment and restoration are urgently required. To tackle the issues of low efficiency in traditional structural damage detection and the absence of predictable repair plans, this paper presents a semi-automatic building-mural protection solution that integrates morphological assessment of mural deterioration with computer vision technology. This study establishes an image prediction system that integrates intelligent damage identification with virtual restoration. First, employing the PaddleSeg deep learning framework and the DeepLabv3 semantic segmentation model, this study used existing mural damage datasets to build a recognition model. The model allows for intelligent identification and labeling of multiple damage types. Subsequently, relying on the ComfyUI platform, Stable Diffusion was used to construct a virtual restoration model. LoRA (low-rank adaptation) technology was introduced to fine-tune the model specifically for the mural style, thus enhancing the directivity and accuracy of virtual restoration. Finally, by applying the results of the recognition model to the virtual restoration model, this study built an integrated system for mural damage diagnosis and virtual restoration. The results show that the damage recognition model achieved a mean intersection over union (mIoU) of 47.8% and a pixel accuracy of 77.97% on the test set, validating the feasibility of using semantic segmentation for mural damage detection. This study presents an integrated workflow framework integrating automatic damage identification and intelligent repair. As an expert-assisted tool, this framework shows application potential for preliminary exploration of mural disease diagnosis and virtual restoration plans, providing technical references for the digital protection of cultural heritage. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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19 pages, 5953 KB  
Article
Synergistic Optimization of Thermal and Mechanical Properties in SiO2-Aerogel- and Vitrified-Microsphere-Modified Cementitious Materials
by Jianbo Dai, Dong Liu, Chuang Rui, Shaokun He and Meimei Song
Buildings 2026, 16(4), 853; https://doi.org/10.3390/buildings16040853 - 20 Feb 2026
Viewed by 655
Abstract
To address the integrated demands of structural reinforcement and energy-efficient retrofitting for existing buildings, a cementitious material modified with vitrified microspheres and SiO2 aerogel was developed to realize the synergistic enhancement of thermal insulation and mechanical strength. By substituting fine sand with [...] Read more.
To address the integrated demands of structural reinforcement and energy-efficient retrofitting for existing buildings, a cementitious material modified with vitrified microspheres and SiO2 aerogel was developed to realize the synergistic enhancement of thermal insulation and mechanical strength. By substituting fine sand with equal mass fractions of SiO2 aerogel and vitrified microspheres in the cement matrix, this study systematically investigated the synergistic regulatory effects of this binary modification on two core performance metrics—thermal conductivity and compressive strength. All performance tests were conducted in triplicate, and the results are presented as the mean values. The results indicated that the thermal conductivity of the composite exhibited a trend of decreasing first and then increasing with the rise in aerogel content. At an aerogel dosage of 6%, the thermal conductivity dropped to 0.2237 W/(m·K), achieving optimal thermal insulation performance while retaining a compressive strength of 17.96 MPa. The subsequent incorporation of 15% vitrified microspheres further reduced the thermal conductivity to 0.1642 W/(m·K) while maintaining a compressive strength of 15.34 MPa, thereby achieving an optimal balance between thermal insulation and mechanical performance. Microstructural characterization revealed that the incorporation of aerogel significantly increased the internal porosity of the composite, effectively reducing thermal conductivity by obstructing heat transfer pathways. Vitrified microspheres enhance thermal resistance via their closed-cell structure and promote the formation and densification of C-S-H gel. Synergistically with SiO2 aerogel, they construct a multi-scale porous composite system. By optimizing the interfacial bonding state and pore structure, this system achieves the synergistic optimization of mechanical strength and thermal insulation of cement-based composites, providing new materials and a theoretical basis for the functional integrated retrofitting of existing building structures. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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26 pages, 8716 KB  
Article
Axial Compression Behavior of Concrete Columns Strengthened with UHPC-Filled Steel Tubes
by Jing Du, Qingxuan Shi and Xuemei Li
Buildings 2026, 16(4), 812; https://doi.org/10.3390/buildings16040812 - 16 Feb 2026
Cited by 1 | Viewed by 951
Abstract
To enhance the load-bearing capacity of conventional reinforced concrete (RC) columns and address the issue of longitudinal reinforcement buckling, this study proposes a novel composite column strengthened with small-diameter ultra-high-performance concrete-filled steel tubes (UHPCFST), in which the UHPCFST members replace the traditional longitudinal [...] Read more.
To enhance the load-bearing capacity of conventional reinforced concrete (RC) columns and address the issue of longitudinal reinforcement buckling, this study proposes a novel composite column strengthened with small-diameter ultra-high-performance concrete-filled steel tubes (UHPCFST), in which the UHPCFST members replace the traditional longitudinal reinforcement. First, the mechanical behavior of UHPCFST was experimentally investigated. Results show that its stress–strain curve exhibits steel-like elastoplastic or strain-hardening characteristics after yielding. Subsequently, the axial compressive performance of the proposed column was studied through numerical simulation, with emphasis on the failure process, load–displacement response, and contribution of each constituent material at different loading stages. By comparing the longitudinal stress in concrete and the strain development in longitudinal reinforcement, steel tubes, and stirrups between conventional RC columns and the composite column, and by systematically varying parameters such as the steel ratio and the number of steel tubes, the influence of these parameters on the axial performance of the composite column was revealed. The results indicate that replacing longitudinal reinforcement with UHPCFST significantly improves the column performance. Compared to a conventional RC column with an equivalent reinforcement ratio, the proposed composite column exhibits an approximately 10% higher peak load capacity, a 182% increase in peak displacement, and a distinct biphasic response characterized by a double-peak pattern in its load–displacement curve. The first peak is contributed jointly by the surrounding concrete and the UHPCFST, while the second peak is mainly provided by the UHPCFST skeleton. This study offers a new perspective for improving the seismic resistance and load-carrying capacity of RC columns. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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21 pages, 1678 KB  
Article
Seismic-Resilience Evaluation Method for Urban–Rural Building Clusters: A Fuzzy Comprehensive Evaluation-Based Study of Weinan City
by Hao Ren, Dan Shao, Rui Duan, Qinhu Tian, Tao Zhao and Lele Chen
Buildings 2026, 16(2), 307; https://doi.org/10.3390/buildings16020307 - 11 Jan 2026
Cited by 1 | Viewed by 689
Abstract
In order to meet the demand for the seismic-resilience assessment of urban–rural building clusters, a new classification method is proposed by integrating national risk census results and evaluated using a large-scale dataset. This study initially identifies and analyzes the key factors that influence [...] Read more.
In order to meet the demand for the seismic-resilience assessment of urban–rural building clusters, a new classification method is proposed by integrating national risk census results and evaluated using a large-scale dataset. This study initially identifies and analyzes the key factors that influence seismic-resilience. The reason for considering both internal and external factors is that they comprehensively reflect the characteristics and influencing conditions of urban–rural building clusters in terms of seismic-resilience. Subsequently, a comprehensive evaluation index system is constructed, encompassing both internal and external factors. Based on this system, criteria for seismic-resilience grading are proposed to classify the resilience levels of different building clusters. This is crucial for differentiating the seismic resilience capabilities of various building clusters. The evaluation index weights are determined by means of a robust method, and a seismic resilience evaluation method for urban–rural building clusters is established on the basis of the fuzzy comprehensive evaluation theory. This method incorporates various internal and external influencing factors to offer a comprehensive assessment. Moreover, by leveraging the ArcGIS platform, the evaluation method is successfully applied to urban–rural building clusters. Taking Weinan City, China, as a case study, an empirical evaluation of the seismic resilience of Weinan City is carried out. The results indicate that the proposed method effectively reflects the seismic resilience of the building clusters and offers valuable insights for enhancing resilience. The research findings provide a solid theoretical foundation and practical reference for enhancing the seismic resilience of urban–rural building clusters, promoting resilient city construction, and supporting post-earthquake disaster-area recovery and reconstruction. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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18 pages, 11545 KB  
Article
Multi-Factor Coupled Assessment of Seismic Disaster Risk for Buildings: A Case Study of Ankang City
by Dan Shao, Hao Ren, Rui Duan, Qinhu Tian and Weichao Zhang
Buildings 2025, 15(24), 4515; https://doi.org/10.3390/buildings15244515 - 14 Dec 2025
Viewed by 676
Abstract
This study presents a multi-factor coupled assessment of seismic disaster risk for approximately 635,000 individual building units in Ankang City, Shaanxi Province, China, utilizing a high-resolution dataset. The assessment methodology innovatively integrates the three core components of risk: seismic vulnerability V of load-bearing [...] Read more.
This study presents a multi-factor coupled assessment of seismic disaster risk for approximately 635,000 individual building units in Ankang City, Shaanxi Province, China, utilizing a high-resolution dataset. The assessment methodology innovatively integrates the three core components of risk: seismic vulnerability V of load-bearing structures, site-specific seismic hazards R, and potential consequences C of damage, to formulate the Seismic Resilience Index ISR = C·R·V. Crucially, the approach advances established risk assessment frameworks by enhancing the spatial resolution of the site influence coefficient R using a high-resolution national site classification map and detailed local geological data. The results reveal that the areas with the lowest ISR values (indicating the lowest resilience and thus the highest risk) are predominantly concentrated in older residential districts of counties such as Ningshan, Hanyin, and Ziyang, where unreinforced masonry structures built prior to 1989 are widespread. The model assessment results align with expected structural performance characteristics, and the study concludes by offering quantified, priority-based recommendations for targeted structural intervention and seismic retrofitting in the identified highest-risk regions and building typologies. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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27 pages, 3695 KB  
Article
A Lightweight Multi-Layer Perceptron Approach for Carbon Emission Prediction of Public Buildings Under Low-Dimensional Data Scenarios
by Yang Wang, Qiming Wang and Shutong Zhang
Buildings 2025, 15(24), 4508; https://doi.org/10.3390/buildings15244508 - 12 Dec 2025
Cited by 3 | Viewed by 835
Abstract
Amid global efforts toward carbon neutrality, carbon emission accounting in the construction sector has become essential for sustainable design. Public buildings, with complex energy systems and high operational loads, are major carbon emitters. However, early design stages often provide only low-dimensional parameters—such as [...] Read more.
Amid global efforts toward carbon neutrality, carbon emission accounting in the construction sector has become essential for sustainable design. Public buildings, with complex energy systems and high operational loads, are major carbon emitters. However, early design stages often provide only low-dimensional parameters—such as floor area, number of floors, and location—limiting conventional regression methods. This study develops a lightweight prediction framework using a multilayer perceptron (MLP) neural network. Feature engineering constructs composite indicators—layers per unit area (LPA) and height-to-area ratio (HAR)—to quantify spatial compactness and vertical density. A three-layer MLP with Swish activation, adaptive L2 regularization, and Dropout reduces overfitting and improves generalization. Tests show the model achieves a mean absolute error of 4160 tCO2 and R2 of 0.966, reducing prediction error by 54.7% compared to linear regression. For high-rise buildings (>15 floors), error remains below 8.1%. SHAP analysis highlights floor area as the dominant factor (51.2%), while HAR and LPA jointly improve accuracy by 5.8%. A Python-based tool is developed for rapid emission estimation during design. Using 150 samples and 10-fold cross-validation, this work demonstrates the potential of deep learning in low-dimensional carbon prediction, offering a practical reference for early-stage green building design, though generalizability requires further validation with larger datasets. Full article
(This article belongs to the Special Issue Innovative Research Applied to Building Structures: From Materials Development to Structural Application)
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