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Innovations in Static and Dynamic Performance of Steel and Composite...
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Innovations in Static and Dynamic Performance of Steel and Composite Structures

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3488

Special Issue Editors

Prof. Dr. Xiaoxiong Zha

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: steel and concrete composite structure; steel structures; modular steel building; sandwich panel; non linear analysis of steel structures; new form of steel-concrete compo-site structures; fire structural analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Wentao Li

E-Mail
Guest Editor
School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: steel and concrete composite structure; steel structures; modular steel building; nonlinear analysis of steel structures; new form of steel-concrete composite structures; fire structural analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urbanization and diverse construction needs have increased the importance of steel and composite structures in modern architecture and civil engineering. These structures must perform well under static conditions and remain extremely safe and reliable under dynamic loads such as seismic, wind, and other loads. This Special Issue aims to gather recent innovative research on the static and dynamic performance of steel and composite structures, covering advancements in their materials, design methods, analysis techniques, construction practices, and maintenance technologies. The goal is to provide a scientific basis and technical support for engineering practice, thereby promoting the development and application of these structures in various fields.

Prof. Dr. Xiaoxiong Zha
Dr. Wentao Li
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-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

  • steel and concrete structures
  • composite structures
  • vibration comfort
  • seismic performance
  • fire performance
  • post-fire rehabilitation
  • sustainable architecture
  • prefabricated structures

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

Published Papers (4 papers)

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Research

24 pages, 3819 KB  
Article
Improved Rapid Assessment on Bending Property of Laminated Channel Beams for Reinforcement Using Explainable Machine-Learning Method
by Bo Xu, Junyi Li, Suhang Chen, Jianfang Zhou, Ronggui Liu and Feifei Jiang
Buildings 2026, 16(11), 2074; https://doi.org/10.3390/buildings16112074 (registering DOI) - 23 May 2026
Abstract
The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, [...] Read more.
The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R2) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R2 = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article
(This article belongs to the Special Issue Innovations in Static and Dynamic Performance of Steel and Composite Structures)
20 pages, 7188 KB  
Article
Machine Learning-Based Method for Predicting the Mechanical Response of Prestressed Cable Tensioning in Aqueduct Structures
by Yanke Shi, Xufang Liu, Yanjun Chang, Jie Chen, Duoxin Zhang and Yuping Kuang
Buildings 2026, 16(8), 1624; https://doi.org/10.3390/buildings16081624 - 20 Apr 2026
Viewed by 282
Abstract
The mechanical behavior of aqueduct structures exhibits highly complex characteristics during prestress tensioning, making it difficult for the traditional double-control method to accurately predict and real-time control the key stresses. To improve the construction safety of prestressed tensioning and the prediction accuracy of [...] Read more.
The mechanical behavior of aqueduct structures exhibits highly complex characteristics during prestress tensioning, making it difficult for the traditional double-control method to accurately predict and real-time control the key stresses. To improve the construction safety of prestressed tensioning and the prediction accuracy of structural prestress responses, this study develops a rapid structural mechanical property prediction method based on machine learning. Taking prestressed aqueducts as the research object, a system of “finite element simulation—sample generation—machine learning prediction” is established. Firstly, multiple groups of tensioning parameter combinations are designed via Latin hypercube sampling, and the stress responses are obtained through finite element analysis to form a high-quality training sample library. Subsequently, critical structural features are extracted based on mesh reconstruction, and stress prediction models are established using the K-Nearest Neighbors (KNN) and Random Forest algorithms respectively; the prediction performance of both models is compared and validated against finite element simulation results. Furthermore, the prediction outputs of the optimal machine learning model were used to analyze the stress distribution and potential stress concentration issues of the structure during the tensioning process. The comparative analysis results indicate that the Random Forest model performs best in terms of stress prediction accuracy and stability, and its prediction results are highly consistent with those of the finite element method. Compared with traditional finite element condition analysis, the machine learning model can complete multi-condition stress prediction in a shorter time. Leveraging its high-efficiency prediction capability, local high-stress areas of the structure in the tensioning construction scheme can be identified, thereby providing effective optimization schemes to improve the stress distribution. The mechanical response prediction method for the prestress tensioning process of aqueducts, with machine learning as the core, constructed in this paper realizes the rapid and reliable prediction of key stresses throughout the entire prestress tensioning process. This method can be applied to assist in optimizing tensioning construction schemes and construction monitoring, providing a practical technical solution for safety control of aqueduct structures during the prestress construction stage. Full article
(This article belongs to the Special Issue Innovations in Static and Dynamic Performance of Steel and Composite Structures)
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25 pages, 9220 KB  
Article
Investigation of Stress Intensity Factors in Welds of Steel Girders Within Steel–Concrete Composite Structures
by Da Wang, Pengxin Zhao, Yuxin Shao, Wenping Peng, Junxin Yang, Chenggong Zhao and Benkun Tan
Buildings 2025, 15(15), 2653; https://doi.org/10.3390/buildings15152653 - 27 Jul 2025
Viewed by 1196
Abstract
Fatigue damage in steel–concrete composite structures frequently initiates at welded joints due to stress concentrations and inherent defects. This study investigates the stress intensity factors (SIFs) associated with fatigue cracks in the welds of steel longitudinal beams, employing the FRANC3D–ABAQUS interactive technique. A [...] Read more.
Fatigue damage in steel–concrete composite structures frequently initiates at welded joints due to stress concentrations and inherent defects. This study investigates the stress intensity factors (SIFs) associated with fatigue cracks in the welds of steel longitudinal beams, employing the FRANC3D–ABAQUS interactive technique. A finite element model was developed and validated against experimental data, followed by the insertion of cracks at both the weld root and weld toe. The influences of stud spacing, initial crack size, crack shape, and lack-of-penetration defects on Mode I SIFs were systematically analyzed. Results show that both weld root and weld toe cracks are predominantly Mode I in nature, with the toe cracks exhibiting higher SIF values. Increasing the stud spacing, crack depth, or crack aspect ratio significantly raises the SIFs. Lack of penetration defects further amplifies the SIFs, especially at the weld root. Based on the computed SIFs, fatigue life predictions were conducted using a crack propagation approach. These findings highlight the critical roles of crack geometry and welding quality in fatigue performance, providing a numerical foundation for optimizing welded joint design in composite structures. Full article
(This article belongs to the Special Issue Innovations in Static and Dynamic Performance of Steel and Composite Structures)
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19 pages, 5790 KB  
Article
Fire Resistance of Prefabricated Steel Tubular Columns with Membrane Protections
by Xinxin Zhang, Xiang Yuan Zheng and Wentao Li
Buildings 2025, 15(10), 1730; https://doi.org/10.3390/buildings15101730 - 20 May 2025
Viewed by 1338
Abstract
With the acceleration of construction industrialization and carbon reduction goals, prefabricated steel structures are widely used for their efficiency and strength. However, steel’s poor fire resistance limits its use. At high temperatures, steel weakens, leading to collapse risks. Common fire protection methods like [...] Read more.
With the acceleration of construction industrialization and carbon reduction goals, prefabricated steel structures are widely used for their efficiency and strength. However, steel’s poor fire resistance limits its use. At high temperatures, steel weakens, leading to collapse risks. Common fire protection methods like rock wool, fire-resistant boards, and coatings focus on single materials, leaving composite systems for modular steel columns understudied. This study systematically examines the fire resistance of modular steel columns with composite protective layers through tests and simulations. It finds that rock wool shrinks under heat, reducing its effectiveness by approximately 66.7%, and suggests construction improvements to mitigate this issue. A simplified fire resistance formula is proposed, showing that the total fire resistance of multi-layer systems approximates the sum of each layer’s resistance. These insights offer practical design guidance and fill a key research gap in composite fire protection for modular steel structures. Full article
(This article belongs to the Special Issue Innovations in Static and Dynamic Performance of Steel and Composite Structures)
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