Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges
Abstract
1. Introduction
- (1)
- Review the development status of LLMs in the AECO industry, analyze their core technologies and typical application scenarios, and explore existing technical challenges and industry demands.
- (2)
- Evaluate the practical value of LLMs in AECO, summarizing their advantages in information processing, intelligent decision-making, and project management while providing key technical references for real-world deployment.
- (3)
- Explore the future development of LLMs in AECO, investigating technological innovations, system integration, and interdisciplinary collaborations to provide insights for future research and drive the intelligent transformation of the industry.
2. Materials and Methods
3. LLMs in the AECO Industry: Core Technology, Application Scenarios, and System Integration
3.1. Core Technologies
3.1.1. From Traditional NLP to LLMs
3.1.2. Deep Learning and Machine Learning Technologies
3.2. Application Scenarios of LLMs in AECO Industry
3.2.1. AI-Driven Information and Interaction Systems
- (1)
- Building Information Modeling (BIM)
- (2)
- Expert Systems and Human–Computer Interaction
- (3)
- Knowledge Sharing and Educational Integration
- (4)
- Data and Corpus Development
3.2.2. Engineering Management and Construction Optimization
- (5)
- Contract Management
- (6)
- Construction Scheduling and Task Optimization
- (7)
- Construction Safety Management
- (8)
- Risk Management
3.2.3. Operations and Sustainability
- (9)
- Energy Management
- (10)
- Material Innovation
- (11)
- Geotechnical Engineering
- (12)
- Operations and Maintenance
3.3. Data and Information Processing
3.3.1. Data Collection and Preprocessing
3.3.2. Information Extraction and Knowledge Graph Construction
3.4. Core Components and Key Technologies of the Toolchain
4. Existing Challenges
4.1. Data Layer
4.1.1. Data Quality and Corpus Construction
4.1.2. Data Heterogeneity and Interoperability Issues
4.2. Technical Layer
4.2.1. Collaborative Limitations in Technology Integration
4.2.2. Real-Time and Accuracy Issues in AI Interaction
4.3. Application Layer
4.3.1. Task Specificity: Weak Generalization Ability in Complex Scenarios
4.3.2. Multi-Role Collaboration: Conflicting Interests Leading to Information Bias
4.3.3. Economic Constraints: High Computational Resources and Maintenance Costs
4.3.4. Human–Machine Trust Gap: Trust and Ethical Risks
5. Future Development Directions
5.1. Data Layer Innovations
5.1.1. High-Quality Data Construction and Multilingual Adaptation
5.1.2. Data Enhancement and Multimodal Integration
5.2. Technical Layer Advancements
5.2.1. Technology Integration and System Expansion
5.2.2. Algorithm and Model Optimization
5.3. Application Layer Expansion
5.3.1. User Participation and Scenario Adaptability
5.3.2. Economic Challenges and Efficiency Optimization
5.3.3. Ethics and Explainability
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | AECO Phase | Applications | Core Technologies | Papers |
---|---|---|---|---|
1 | A—Architecture | Design Assistance and Optimization | Prompt engineering; generative design; natural language interfaces | [8,29,30,31,32,33] |
Cost Estimation of Civil Engineering Works | Domain-specific fine-tuning; information extraction | [34] | ||
Information Retrieval and Integration | Semantic search; knowledge embedding; retrieval-augmented generation | [11,35,36,37,38,39,40] | ||
2 | E—Engineering | Geotechnical Engineering | Multimodal input (text + code); technical document parsing | [41,42] |
Structural Engineering | LLM-to-code generation; parameterized prompting | [43] | ||
3 | C—Construction | Construction Management and Optimization | Scheduling LLMs; dialogue-based assistants; task planning | [44,45,46,47] |
Risk Management | Knowledge graph integration; temporal and causal reasoning | [48,49,50,51,52,53] | ||
Safety Management | Vision-language models; scene graph fusion; hazard recognition | [54,55,56,57,58,59,60,61] | ||
Construction Automation and Robotics Technology | LLM-to-robot code pipelines; real-time prompt adaptation; human–robot collaboration | [62,63,64,65] | ||
4 | O—Operation | Facility Management and Optimization | Decision-support via text-to-action; RAG; semantic enrichment | [66] |
Quality Control and Defect Management | NLP-based defect detection; sentiment analysis for stakeholder feedback | [67] | ||
Energy Management | Data-to-text generation; energy ontology integration; semantic similarity | [68] | ||
5 | Cross-Phase Applications | Knowledge Management and Information Sharing | Retrieval-augmented generation; ontology-grounded LLMs; human-in-the-loop learning | [69,70] |
Contract Management | Legal text mining; clause classification; domain adaptation | [16,71,72,73] | ||
Construction Activity Monitoring | Video captioning; spatio-temporal reasoning; scene understanding | [74] | ||
Strategic Technology Integration across AECO Phases | Multiphase AI frameworks; LangChain orchestration; model interoperability | [3,13,75,76,77,78] | ||
Intelligent Q&A and Interaction | Task-oriented dialogue; semantic parsing; BIM-integrated Q&A interfaces | [79] |
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Zhang, G.; Lu, C.; Luo, Q. Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges. Buildings 2025, 15, 1944. https://doi.org/10.3390/buildings15111944
Zhang G, Lu C, Luo Q. Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges. Buildings. 2025; 15(11):1944. https://doi.org/10.3390/buildings15111944
Chicago/Turabian StyleZhang, Guozong, Chenyuan Lu, and Qianmai Luo. 2025. "Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges" Buildings 15, no. 11: 1944. https://doi.org/10.3390/buildings15111944
APA StyleZhang, G., Lu, C., & Luo, Q. (2025). Application of Large Language Models in the AECO Industry: Core Technologies, Application Scenarios, and Research Challenges. Buildings, 15(11), 1944. https://doi.org/10.3390/buildings15111944