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Buildings
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Large-Scale AI Models Across the Construction Lifecycle
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Large-Scale AI Models Across the Construction Lifecycle

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4450

Special Issue Editors

Dr. Shuai Han

E-Mail Website
Guest Editor
Faculty of Construction and Environment, Hong Kong Polytechnic University, Kowloon 100872, Hong Kong
Interests: construction informatics; artificial intelligence; building information modeling; automation in construction
Special Issues, Collections and Topics in MDPI journals
Dr. Guozong Zhang

E-Mail Website
Guest Editor
School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: project Integrated management; value management; urban renewal; large-scale AI models
Dr. Yingwei Cui

E-Mail Website
Guest Editor Assistant
Department of Architecture and Art, Hebei University of Architecture, Zhangjiakou 075024, China
Interests: urban and rural planning; intelligent building and smart city; urban renewal planning and design; space information technology of urban planning
Dr. Suhong Li

E-Mail Website
Guest Editor Assistant
School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
Interests: project management and risk control; urban renewal; AI in construction industry; real estate operation and management
Dr. Yan Zhao

E-Mail Website
Guest Editor Assistant
School of Civil Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Interests: construction economy; urban renewal; public-private partnership

Special Issue Information

Dear Colleagues,

As the construction industry progressively shifts towards greater intelligence, digitalization, and sustainable development, the application of large-scale AI models, represented by large language models (LLMs), has emerged as a pivotal technological force driving this transformation. These models are capable of processing and analyzing vast amounts of complex data, enabling prediction, optimization, and automated decision-making, which is revolutionizing various stages of building design, construction, operation, and maintenance. The potential of large-scale AI models to enhance productivity, optimize resource allocation, reduce environmental impact, and improve safety is increasingly being recognized and applied across the industry.

This Special Issue aims to explore the contributions of large-scale AI models in areas such as generative design, project management, construction robotics, BIM, digital twins, and urban renewal. The key topics of this Special Issue include, but are not limited to, the following:

  • Large language model;
  • AI-driven urban renewal planning and design;
  • Automation and robotics in construction;
  • Integration of BIM and digital twins;
  • Applications of AI in construction project management;
  • Social impact assessment in urban renewal;
  • AI-driven sustainable development of aging communities;
  • Adoption of digital technologies in the construction industry;
  • Industrialized construction.

Dr. Shuai Han
Dr. Guozong Zhang
Guest Editors

Dr. Yingwei Cui
Dr. Suhong Li
Dr. Yan Zhao
Guest Editor Assistants

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

  • large AI models
  • deep learning
  • natural language processing
  • BIM
  • digital twin
  • construction project management
  • full lifecycle

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

Published Papers (5 papers)

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Research

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28 pages, 3291 KB  
Article
Harnessing Large Language Models for Digital Building Logbook Implementation
by Alon Urlainis, Yahel Giat and Amichai Mitelman
Buildings 2025, 15(18), 3399; https://doi.org/10.3390/buildings15183399 - 19 Sep 2025
Abstract
Digital Building Logbooks (DBLs) have been proposed to preserve lifecycle data across the design, construction, operation, and renovation phases of buildings. Yet, implementation has been hindered by the absence of standardized data models across jurisdictions and stakeholder practices. This paper argues that Large [...] Read more.
Digital Building Logbooks (DBLs) have been proposed to preserve lifecycle data across the design, construction, operation, and renovation phases of buildings. Yet, implementation has been hindered by the absence of standardized data models across jurisdictions and stakeholder practices. This paper argues that Large Language Models (LLMs) offer a solution that reduces reliance on rigid standardization. To test this approach, we first draw on parallels from the healthcare sector, where LLMs have extracted structured information from unstructured electronic health records. Second, we present an LLM-based workflow for processing unstructured building inspection reports. The workflow encompassed three tasks: (1) qualitative summary, (2) quantitative summary, and (3) risk level assessment. Sixteen inspection reports were processed through GPT-4o across 320 runs via a Python script. Results showed perfect consistency for categorical fields and Boolean indicators, minimal variability for ordinal severity ratings (σ ≤ 0.6), and stable risk assessments with 87.5% of reports showing low standard deviations. Each report was processed in under 10 s, representing up to a 100-fold speed improvement over manual review. These findings demonstrate the feasibility of post hoc standardization, positioning DBLs to evolve into large-scale knowledge bases that can substantially advance research on the built environment. Full article
(This article belongs to the Special Issue Large-Scale AI Models Across the Construction Lifecycle)
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19 pages, 3302 KB  
Article
Topic Mining and Evolutionary Analysis of Urban Renewal Policy Texts in China
by Guozong Zhang, Xijing Liu and Qianmai Luo
Buildings 2025, 15(18), 3324; https://doi.org/10.3390/buildings15183324 - 14 Sep 2025
Viewed by 316
Abstract
In the context of China’s rapid urbanization and the era of stock planning, urban renewal policies play a significant role in enhancing urban quality and promoting sustainable development. To reveal the thematic structure and evolution of China’s urban renewal policy system, this study [...] Read more.
In the context of China’s rapid urbanization and the era of stock planning, urban renewal policies play a significant role in enhancing urban quality and promoting sustainable development. To reveal the thematic structure and evolution of China’s urban renewal policy system, this study applies the BERTopic model to conduct semantic mining and evolutionary analysis on 1144 policy documents issued by central and local governments. Research findings: The study identifies 34 distinct themes in urban renewal policies, grouped into five main directions: Spatial Improvement and Facility Upgrades, Project Collaboration and Approval, Land Acquisition and Compensation, Fiscal Incentives and Funding Support, and Institutional Guarantees and Governance. Each of these directions exhibits distinct evolutionary trends over time. While urban renewal policies in the Central, Western, Eastern, and Northeastern regions share common characteristics in key aspects such as land acquisition and compensation, funding assurance, and residential quality enhancement, they also reflect regional differences due to varying stages of development, economic conditions, and geographic factors. This demonstrates both the shared and distinct policy focus areas across different regions of China. By identifying underlying themes and their trajectories, this study provides critical insights into the structural characteristics of urban renewal policies and offers valuable references for government authorities to improve and optimize policy systems. At the same time, it provides the Chinese experience for urban renewal in other countries. Full article
(This article belongs to the Special Issue Large-Scale AI Models Across the Construction Lifecycle)
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19 pages, 1297 KB  
Article
A Novel Method for Named Entity Recognition in Long-Text Safety Accident Reports of Prefabricated Construction
by Qianmai Luo, Guozong Zhang and Yuan Sun
Buildings 2025, 15(17), 3063; https://doi.org/10.3390/buildings15173063 - 27 Aug 2025
Viewed by 412
Abstract
Prefabricated construction represents an advanced approach to sustainable development, and safety issues in prefabricated construction projects have drawn widespread attention. Safety accident case reports contain a wealth of safety knowledge, and extracting and learning from such historical reports can significantly enhance safety management [...] Read more.
Prefabricated construction represents an advanced approach to sustainable development, and safety issues in prefabricated construction projects have drawn widespread attention. Safety accident case reports contain a wealth of safety knowledge, and extracting and learning from such historical reports can significantly enhance safety management capabilities. However, these texts are often semantically complex and lengthy, posing challenges for traditional Information Extraction (IE) methods. This study focuses on the challenge of Named Entity Recognition (NER) in long texts under complex engineering contexts and proposes a novel model that integrates Modern Bidirectional Encoder Representations from Transformers (ModernBERT),Bidirectional Long Short-Term Memory (BiLSTM), andConditional Random Field (CRF). A comparative analysis with current mainstream methods is conducted. The results show that the proposed model achieves an F1 score of 0.6234, outperforming mainstream baseline methods. Notably, it attains F1 scores of 0.95 and 0.92 for the critical entity categories “Consequence” and “Type,” respectively. The model maintains stable performance even under semantic noise interference, demonstrating strong robustness in processing unstructured and highly heterogeneous engineering texts. Compared with existing long-text NER models, the proposed method exhibits superior semantic parsing ability in engineering contexts. This study enhances information extraction methods and provides solid technical support for constructing safety knowledge graphs in prefabricated construction, thereby advancing the level of intelligence in the construction industry. Full article
(This article belongs to the Special Issue Large-Scale AI Models Across the Construction Lifecycle)
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26 pages, 9932 KB  
Article
Evolutionary Game Analysis on the Promotion of Green Buildings in China Under the “Dual Carbon” Goals: A Multi-Stakeholder Perspective
by Yongbo Su and Zhichao Zhang
Buildings 2025, 15(8), 1392; https://doi.org/10.3390/buildings15081392 - 21 Apr 2025
Cited by 1 | Viewed by 627
Abstract
The promotion of green buildings offers an effective solution to climate change and resource scarcity. This study employs game theory to study the evolutionary decision-making processes and stable strategies among three principal stakeholders in the green building sector: the government, construction enterprises, and [...] Read more.
The promotion of green buildings offers an effective solution to climate change and resource scarcity. This study employs game theory to study the evolutionary decision-making processes and stable strategies among three principal stakeholders in the green building sector: the government, construction enterprises, and consumers. By analyzing the primary factors that shape these stakeholders’ strategies, we propose a tripartite evolutionary game model. We utilize MATLAB R2016a to simulate the evolutionary decision-making processes and stable strategies of the three stakeholders, verifying the effectiveness of our approach. Our findings indicate that the government, in its regulatory capacity, plays a critical role in influencing the green building market. Government subsidies and penalties significantly affect the decision-making behavior of enterprises and consumers; in addition, dynamic rewards and punishments can effectively restrain the fluctuation of the game process. The development of the green building market correlates with increased consumer willingness and capacity to purchase green buildings, coupled with significantly reduced construction costs. Throughout this progression, the government gradually withdraws its incentives and shifts toward a more relaxed regulatory stance. Our research also indicates that the cooperative behavior and evolution of the three stakeholders are heavily influenced by key parameters, regardless of their initial states. Full article
(This article belongs to the Special Issue Large-Scale AI Models Across the Construction Lifecycle)
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Review

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28 pages, 1163 KB  
Review
Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges
by Guozong Zhang, Chenyuan Lu and Qianmai Luo
Buildings 2025, 15(11), 1944; https://doi.org/10.3390/buildings15111944 - 4 Jun 2025
Viewed by 1743
Abstract
As projects in the architecture, engineering, construction, and operations (AECO) industry grow in complexity and scale, there is an urgent need for more effective information management and intelligent decision-making. This study investigates the potential of large language models (LLMs) to address these challenges [...] Read more.
As projects in the architecture, engineering, construction, and operations (AECO) industry grow in complexity and scale, there is an urgent need for more effective information management and intelligent decision-making. This study investigates the potential of large language models (LLMs) to address these challenges by systematically reviewing their core technologies, application scenarios, and integration approaches in AECO. Using a literature-based review methodology, this paper examines how LLMs—built on Transformer architecture and powered by deep learning and natural language processing—can process complex unstructured data and support a wide range of tasks, including contract analysis, construction scheduling, risk assessment, and operations and maintenance. This study finds that while LLMs offer substantial promise for enhancing productivity and automation in AECO workflows, several obstacles remain, such as data quality issues, computational demands, limited adaptability, integration barriers, and ethical concerns. The paper concludes that future research should focus on improving model efficiency, enabling multimodal data fusion, and enhancing compatibility with existing industry tools to realize the full potential of LLMs and support the digital transformation of the AECO sector. Full article
(This article belongs to the Special Issue Large-Scale AI Models Across the Construction Lifecycle)
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