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Advances in Smart Construction and Intelligent Buildings
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Advances in Smart Construction and Intelligent Buildings

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 6883

Special Issue Editors

Dr. Na Xu

E-Mail Website
Guest Editor
School of Mechanics and Civil Engineering, China University of Mining & Technology, Xuzhou 221000, China
Interests: safety risk management; knowledge management; artificial intelligence; big data
Prof. Dr. Wei Chen

E-Mail Website
Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: mine communication; artificial intelligence; industrial Internet of Things; safety monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to curate groundbreaking research at the intersection of digital transformation, automation, and sustainability in the built environment. As urbanization and climate imperatives intensify, the integration of smart technologies—including IoT, AI-driven analytics, digital twins, robotics, and self-healing materials—into construction and building management systems has become pivotal. We invite contributions that transcend traditional disciplinary boundaries, addressing challenges such as real-time adaptive infrastructure design, energy-autonomous buildings, resilient urban systems, and human-centric intelligent environments. Submissions should emphasize novel theoretical frameworks, experimental validations, or scalable case studies that demonstrate transformative impacts on efficiency, safety, and environmental performance. Of particular interest are studies leveraging generative AI for design optimization, blockchain-enabled lifecycle management, autonomous robotic systems for construction, and bio-inspired adaptive materials. Cross-disciplinary approaches bridging civil engineering, computer science, materials science, and behavioral economics are strongly encouraged. This Special Issue will prioritize works that not only advance technical frontiers but also critically address ethical, regulatory, and socio-economic dimensions of smart construction ecosystems. By fostering dialog between academia and industry, we aim to establish a roadmap for next-generation intelligent buildings that harmonize technological innovation with planetary stewardship and occupant well-being.

Dr. Na Xu
Prof. Dr. Wei 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • digital twins
  • IoT-enabled infrastructure
  • sustainable urban resilience
  • generative AI in architecture
  • autonomous construction robotics
  • self-healing building materials
  • blockchain for lifecycle management
  • cognitive building automation
  • energy-positive structures
  • big data analytics in BIM

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Published Papers (6 papers)

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Research

28 pages, 5016 KB  
Article
A Lightweight Improved YOLOv8-Based Method for Rebar Intersection Detection
by Rui Wang, Fangjun Shi, Yini She, Li Zhang, Kaifeng Lin, Longshun Fu and Jingkun Shi
Appl. Sci. 2025, 15(24), 12898; https://doi.org/10.3390/app152412898 - 7 Dec 2025
Viewed by 252
Abstract
As industrialized construction and smart building continue to advance, rebar-tying robots place higher demands on the real-time and accurate recognition of rebar intersections and their tying status. Existing deep learning-based detection methods generally rely on heavy backbone networks and complex feature-fusion structures, making [...] Read more.
As industrialized construction and smart building continue to advance, rebar-tying robots place higher demands on the real-time and accurate recognition of rebar intersections and their tying status. Existing deep learning-based detection methods generally rely on heavy backbone networks and complex feature-fusion structures, making it difficult to deploy them efficiently on resource-constrained mobile robots and edge devices, and there is also a lack of dedicated datasets for rebar intersections. In this study, 12,000 rebar mesh images were collected and annotated from two indoor scenes and one outdoor scene to construct a rebar-intersection dataset that supports both object detection and instance segmentation, enabling simultaneous learning of intersection locations and tying status. On this basis, a lightweight improved YOLOv8-based method for rebar intersection detection and segmentation is proposed. The original backbone is replaced with ShuffleNetV2, and a C2f_Dual residual module is introduced in the neck; the same improvements are further transferred to YOLOv8-seg to form a unified lightweight detection–segmentation framework for joint prediction of intersection locations and tying status. Experimental results show that, compared with the original YOLOv8L and several mainstream detectors, the proposed model achieves comparable or superior performance in terms of mAP@50, precision and recall, while reducing model size and computational cost by 51.2% and 58.1%, respectively, and significantly improving inference speed. The improved YOLOv8-seg also achieves satisfactory contour alignment and regional consistency for rebar regions and intersection masks. Owing to its combination of high accuracy and low resource consumption, the proposed method is well suited for deployment on edge-computing devices used in rebar-tying robots and construction quality inspection, providing an effective visual perception solution for intelligent construction. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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16 pages, 11667 KB  
Article
Terrain Surface Interpolation from Large-Scale 3D Point Cloud Data with Semantic Segmentation in Earthwork Sites
by Suyeul Park, Yonggun Kim and Seok Kim
Appl. Sci. 2025, 15(23), 12831; https://doi.org/10.3390/app152312831 - 4 Dec 2025
Viewed by 239
Abstract
Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making [...] Read more.
Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making them highly applicable to construction environments that require precise operations. Accordingly, this research developed a new terrain surface interpolation method that leverages diverse information embedded in large-scale 3D point cloud data acquired from earthwork sites, as part of a foundational study for construction automation. In particular, the proposed terrain surface interpolation method was designed to be integrated with semantic segmentation based on 3D point cloud data, with a focus on enhancing the accuracy of earthwork volume estimation. Furthermore, field experiments were conducted using heavy construction equipment to compare terrain change and earthwork volume analyses between 3D point cloud data with and without the application of the proposed interpolation method. The analysis results of earthwork volumes indicated that the application of the terrain interpolation method to 3D point cloud data for construction equipment reduced estimation errors by approximately 94% compared to non-interpolated data. These findings demonstrate the effectiveness of the proposed method and are expected to contribute to future research in artificial intelligence and robotics utilizing 3D point cloud data within the construction industry. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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20 pages, 1930 KB  
Article
Knowledge Support for Emergency Response During Construction Safety Accidents
by Han Tong, Xinyu Li, An Shi, Na Xu and Jin Guo
Appl. Sci. 2025, 15(21), 11760; https://doi.org/10.3390/app152111760 - 4 Nov 2025
Viewed by 592
Abstract
Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application [...] Read more.
Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field. Subsequently, 4877 entities and 5783 relationships were extracted by means of the BERT-BiLSTM-CRF model and the BERT-CNN model, with F1 values reaching approximately 0.8. Finally, the Neo4j graph database was adopted for data storage, and a domain knowledge graph was constructed. Based on this graph, services such as knowledge association, knowledge retrieval, and intelligent question-answering were implemented. These services effectively addressed key challenges in information acquisition and decision support for on-site safety management, thereby significantly enhancing response efficiency and quality while strengthening overall safety management practices within the construction industry. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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27 pages, 3503 KB  
Article
Structure-Aware and Format-Enhanced Transformer for Accident Report Modeling
by Wenhua Zeng, Wenhu Tang, Diping Yuan, Hui Zhang, Pinsheng Duan and Shikun Hu
Appl. Sci. 2025, 15(14), 7928; https://doi.org/10.3390/app15147928 - 16 Jul 2025
Cited by 1 | Viewed by 1174
Abstract
Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, [...] Read more.
Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, this study proposes SAFE-Transformer, a Structure-Aware and Format-Enhanced Transformer designed for long-document modeling in the emergency safety context. SAFE-Transformer adopts a dual-stream encoding architecture to separately model symbolic section features and heading text, integrates hierarchical depth and format types into positional encodings, and introduces a dynamic gating unit to adaptively fuse headings with paragraph semantics. We evaluate the model on a multi-label accident intelligence classification task using a real-world corpus of 1632 official reports from high-risk industries. Results demonstrate that SAFE-Transformer effectively captures hierarchical semantic structure and outperforms strong long-text baselines. Further analysis reveals an inverted U-shaped performance trend across varying report lengths and highlights the role of attention sparsity and label distribution in long-text modeling. This work offers a practical solution for structurally complex safety documents and provides methodological insights for downstream applications in safety supervision and risk analysis. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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25 pages, 1544 KB  
Article
Research on the Influence of Anxiety Psychology on Unsafe Behavior Among Construction Workers
by Aiguo Xiong, Rongwei Hu, Na Xu, Durong Huang, Hong Fan and Yu Zhang
Appl. Sci. 2025, 15(10), 5735; https://doi.org/10.3390/app15105735 - 20 May 2025
Cited by 2 | Viewed by 1344
Abstract
Unsafe behaviors among construction workers constitute a primary cause of safety accidents, with unsafe psychological states often triggering such behaviors. Focusing on collective anxiety issues, this study introduces the standardized anxiety scale (SAS) into the construction domain to investigate the influence mechanism of [...] Read more.
Unsafe behaviors among construction workers constitute a primary cause of safety accidents, with unsafe psychological states often triggering such behaviors. Focusing on collective anxiety issues, this study introduces the standardized anxiety scale (SAS) into the construction domain to investigate the influence mechanism of anxiety psychology on construction workers’ unsafe behaviors. A hypothesized model was established, incorporating organizational climate, safety competency, and job satisfaction as mediating variables, and demographic characteristics (gender, age, educational background, work experience, anxiety level) as moderating variables. Data collected from field surveys were analyzed using SPSS 22.0 and AMOS 24.0 for correlation analysis, variance analysis, and regression analysis, while structural equation modeling (SEM) was employed to validate the theoretical model and hypotheses. The results indicate that (1) anxiety psychology exhibits a significant positive correlation with unsafe behaviors; (2) organizational climate, safety competency, and job satisfaction mediate the transmission pathways between anxiety psychology and unsafe behaviors; (3) enhancing organizational climate, improving safety competency, and ensuring job satisfaction effectively reduce safety incident rates. Additionally, this study proposes preventive measures targeting anxiety psychology across three dimensions: external environmental controls, internal self-regulation, and direct anxiety intervention. These measures provide novel perspectives for effectively reducing the occurrence of unsafe behaviors among construction workers and advancing safety governance frameworks in the construction industry. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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16 pages, 2935 KB  
Article
LLM-Enhanced Framework for Building Domain-Specific Lexicon for Urban Power Grid Design
by Yan Xu, Tao Wang, Yang Yuan, Ziyue Huang, Xi Chen, Bo Zhang, Xiaorong Zhang and Zehua Wang
Appl. Sci. 2025, 15(8), 4134; https://doi.org/10.3390/app15084134 - 9 Apr 2025
Cited by 1 | Viewed by 1668
Abstract
Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient [...] Read more.
Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional methods, and supporting the development of semantically intelligent systems for smart urban power grid design, with significant practical application value. Full article
(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)
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