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The Application of Intelligence Techniques in Construction Materials
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The Application of Intelligence Techniques in Construction Materials

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

Deadline for manuscript submissions: closed (20 August 2025) | Viewed by 2128

Special Issue Editors

Prof. Dr. Xiaorui Zhang

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Guest Editor
School of Transportation Engineering, Nanjing Technology University, Nanjing 211800, China
Interests: long-life asphalt pavement (pavement performance prediction/intelligent road infrastructure); intelligent transportation and transportation planning; asphalt pavement materials (mechanism analysis/recycled materials)
Special Issues, Collections and Topics in MDPI journals
Dr. Weijie Zhang

E-Mail Website
Guest Editor
School of Science, Chongqing University of Technology, Chongqing 400054, China
Interests: the recycling of waste asphalt and its application in new energy devices (supercapacitors, lithium-ion batteries, zinc-ion batteries); nanomaterials; materials characterization
Dr. Jingmin Xu

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: Intelligent and sustainable geotechnical engineering; smart infrastructure and construction
Dr. Songqiang Chen

E-Mail Website
Guest Editor
Department of Civil Engineering, School of Engineering, Hangzhou City University, Hangzhou 310015, China
Interests: pavement structure design; aggregate adhesion evaluation

Special Issue Information

Dear Colleagues,

With the rapid development of urbanization and advancements in technology, the concept of intelligent buildings has become a focus of modern architecture and construction. These buildings integrate innovative building materials, energy-efficient systems, and intelligent structural designs to optimize functionality, sustainability, and occupant comfort. In addition, energy-saving solutions have become critical as the world transitions to greener, more resilient infrastructure. This Special Issue seeks to explore the intersection between innovative materials, intelligent structural designs, and energy-efficient technologies in order to address the challenges and opportunities associated with the construction of sustainable, smart urban environments.

This Special Issue will address a wide range of topics related to the development of intelligent building technologies, sustainable building materials, structural innovations, and energy management systems. By focusing on recent research and practical applications, this collection will provide valuable insights into how these fields converge to create future-ready, smart structures.

We welcome the submission of original research articles, review papers, case studies, and technical notes that address the following topics:

  • Smart materials and their role in intelligent building, the integration of smart sensors, and AI technologies in building systems.
  • The development and application of advanced construction materials; Sustainable materials, nanomaterials, and functional materials for improved durability, insulation, and energy efficiency.
  • New structural designs and construction techniques that enhance building performance, safety, and resilience.
  • Energy storage systems, renewable energy integration, and the optimization of energy consumption in intelligent buildings.

Green building strategies, life cycle assessment, and eco-friendly construction techniques.

Prof. Dr. Xiaorui Zhang
Dr. Weijie Zhang
Dr. Jingmin Xu
Dr. Songqiang 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 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

  • intelligent building system
  • artificial intelligence
  • advanced building material
  • construction structure
  • renewable energy integration
  • green building strategy
  • building assessment and management

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

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Research

14 pages, 3176 KB  
Article
Acoustic Emission Assisted Inspection of Punching Shear Failure in Reinforced Concrete Slab–Column Structures
by Xinchen Zhang, Zhihong Yang and Guogang Ying
Buildings 2025, 15(17), 3226; https://doi.org/10.3390/buildings15173226 - 7 Sep 2025
Viewed by 624
Abstract
Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column [...] Read more.
Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column specimens, analyzing basic AE parameters (hits, amplitude, energy), improved b-value (Ib-value), and RA–AF correlation, while introducing a Gaussian Mixture Model (GMM) to establish a unified index integrating crack type identification and energy information. Experimental results show that AE parameters can effectively track different stages of crack development, with Ib-value reflecting the transition from micro-crack to macro-crack growth. The correlation between AE energy and structural strain energy enables quantitative damage assessment, while RA–AF analysis and GMM clustering reveal the shift from bending-dominated to shear-dominated failure modes. This study provides a comprehensive framework for real-time damage evaluation and failure mode prediction in slab–column structures, demonstrating that AE-based multi-parameter analysis and data-driven clustering methods can characterize damage evolution and improve the reliability of structural health monitoring. Full article
(This article belongs to the Special Issue The Application of Intelligence Techniques in Construction Materials)
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15 pages, 2924 KB  
Article
Influence of Interlayer Bonding Conditions Between Base and Surface Layers on Structural Mechanics Response of Asphalt Pavements
by Weijun Guo, Zhanjun Bai, Qunfeng Zhang, Daizhou Tong and Songqiang Chen
Buildings 2025, 15(16), 2922; https://doi.org/10.3390/buildings15162922 - 18 Aug 2025
Viewed by 372
Abstract
The interlayer bonding strength between a cement-stabilized macadam (CSM) base and an asphalt surface layer significantly influences asphalt pavement performance. This study analyzes the calculation method for the interlayer bonding coefficient, investigates its impact on pavement structural response, and proposes a threshold value. [...] Read more.
The interlayer bonding strength between a cement-stabilized macadam (CSM) base and an asphalt surface layer significantly influences asphalt pavement performance. This study analyzes the calculation method for the interlayer bonding coefficient, investigates its impact on pavement structural response, and proposes a threshold value. Pavement mechanics software was first employed to analyze horizontal displacement at the CSM-asphalt interface, leading to a proposed method for calculating the bonding coefficient using initial stress and displacement derived from interlayer shear tests. Subsequently, the bonding coefficient was evaluated under three interface conditions: untreated, emulsified asphalt-treated, and SBS-modified hot asphalt-treated. Results reveal substantial inherent bonding strength even in untreated interfaces. SBS-modified hot asphalt increased bonding strength by 40–50% compared to untreated interfaces and by 15–20% relative to emulsified asphalt-treated interfaces. Analysis of varying bonding coefficients demonstrates that insufficient CSM-asphalt bonding readily induces asphalt layer fatigue cracking, with bonding strength exerting the dominant influence on fatigue life. Pavements with SBS-modified hot asphalt interlayers exhibited approximately 70% longer fatigue life than untreated interfaces and 30% longer than emulsified asphalt-treated interfaces. Crucially, an interlayer bonding coefficient exceeding 5000 MPa/cm is required for layers to be considered fully bonded. Full article
(This article belongs to the Special Issue The Application of Intelligence Techniques in Construction Materials)
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18 pages, 10583 KB  
Article
Large AI Models for Building Material Counting Task: A Comparative Study
by Yutao Chen, Yang Li, Siyuan Liu, Qian Huang, Zekai Fan and Jun Chen
Buildings 2025, 15(16), 2900; https://doi.org/10.3390/buildings15162900 - 15 Aug 2025
Viewed by 532
Abstract
The rapid advancement of general large models has significantly impacted and introduced new concepts to the traditional “one task, one model” research paradigm in construction automation. In this paper, we evaluate the performance of existing large models and those developed on large model [...] Read more.
The rapid advancement of general large models has significantly impacted and introduced new concepts to the traditional “one task, one model” research paradigm in construction automation. In this paper, we evaluate the performance of existing large models and those developed on large model platforms, using building material counting as an example. We compare three categories of large AI models for building material counting, including multimodal large models, purely visual large models, and secondary models developed on platforms. Through this research, we aim to explore the accuracy and practicality of these models in real-world construction scenarios. The results indicate that directly applying general large models faces challenges in processing photos with complex shapes or backgrounds, failing to provide accurate counting results. Additionally, while purely visual large models excel in instance segmentation tasks, their application to the specific counting of building materials requires additional programming work. To address these issues, this study explores solutions based on large model secondary development platforms and trains a model using EasyDL as an example. Leveraging deep learning techniques, this model achieves effective counting of building materials through five steps: data preparation, model type selection, model training, model validation, and model deployment. Although models developed based on large model platforms are presently less accurate than specialized models, they still represent a highly promising approach. Full article
(This article belongs to the Special Issue The Application of Intelligence Techniques in Construction Materials)
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