AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects
Abstract
1. Introduction
2. Materials and Methods
2.1. Research Design
2.2. Stage 1 Data Collection and Processing
2.2.1. Data Collection
- (1)
- AI technology (e.g., “artificial intelligence”, “deep learning”);
- (2)
- green building (e.g., “green building”, “energy-efficient construction”);
- (3)
- technology innovation (e.g., “technology innovation”, “knowledge diffusion”).
2.2.2. Stage 1: Data Screening and Processing
2.3. Stage 2: Bibliometric Analysis
2.4. Stage 3: Core Literature Screening
2.5. Stage 4: Literature Topic Analysis
2.5.1. Text Preprocessing
2.5.2. Dynamic Topic Modeling
3. Results
3.1. Bibliometric Analysis
3.1.1. Temporal Characteristics Analysis
3.1.2. Research Hotspot Analysis: Keyword Co-Occurrence Network Analysis
- (1)
- The red cluster includes terms such as “compressive strength”, “fly ash”, and “geopolymer concrete”, highlighting the role of AI in developing green building materials and evaluating their performance.
- (2)
- The blue cluster features terms such as “cooling”, “mean square error”, and “performance evaluation”, focusing on optimizing building energy performance.
- (3)
- The green cluster centers on terms such as “green building” and “sustainability”, reflecting the conceptual foundation of sustainable construction.
- (4)
- The yellow cluster includes terms such as “thermal comfort”, “air quality”, and “ventilation”, highlighting concerns regarding indoor environmental quality and occupant health.
3.1.3. Research Frontier Capture: Burst Term Analysis
3.2. Literature Topic Analysis
3.2.1. Core Literature Screening
3.2.2. Horizontal Analysis of Research Topic Distribution
- (1)
- Determination of Optimal Topic Number
- (2)
- Topic Analysis
3.2.3. Longitudinal Analysis of Research Topic Evolution
- (1)
- Topic Heat Analysis
- (2)
- Topic Evolution Path Analysis
4. Discussion
4.1. Research Paradigms of AI-Driven GBTI
4.1.1. Performance-Oriented Intelligent Design Paradigm
4.1.2. Data-Driven Prediction and Simulation Paradigm
4.1.3. Evolution-Driven Intelligent Optimization Paradigm
4.1.4. Knowledge Mining-Based Systemic Decision Paradigm
4.2. Future Research Directions
4.2.1. AI-Powered Digital Twins for Life-Cycle Management of Green Building
4.2.2. Generative Artificial Intelligence Promoting Intelligent Decision Support
4.2.3. Intelligent Construction Management Integrated with Green Construction
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Search String |
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(TITLE-ABS-KEY (“green build*” OR “sustainable build*” OR “eco build*” OR “low-carbon build*” OR “carbon-neutral build*” OR “net-zero build*” OR “circular build*” OR “sustainable construct*” OR “green construct*” OR “eco construct*” OR “green architect*” OR “low-carbon architect*” OR “energy-efficient architect*” OR “sustainable architect*”) AND TITLE-ABS-KEY (“artificial intelligence” OR “AI” OR “machine learning” OR “deep learning” OR “neural network*” OR “computer vision” OR “natural language processing” OR “reinforcement learning” OR “big data” OR “data mining” OR “predictive model*” OR “natural language processing”) AND TITLE-ABS-KEY (“technological innovation” OR “tech* advancement” OR “novel application*” OR “emerging technolog*” OR “digital transformation” OR “innovat*” OR “novel*” OR “new approach*” OR “propos*”)) AND PUBYEAR > 2014 AND PUBYEAR < 2026 AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “ENER”) OR LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “COMP”)) AND (LIMIT-TO (LANGUAGE, “English”)) |
Keywords | Year | Strength | Begin | End | 2015–2025 |
---|---|---|---|---|---|
energy utilization | 2018 | 5.42 | 2022 | 2025 | ▂▂▂▂▂▂▂▃▃▃▃ |
sustainable development | 2018 | 3.85 | 2022 | 2025 | ▂▂▂▂▂▂▂▃▃▃▃ |
genetic algorithms | 2022 | 3.26 | 2022 | 2025 | ▂▂▂▂▂▂▂▃▃▃▃ |
decision trees | 2015 | 3.12 | 2022 | 2023 | ▂▂▂▂▂▂▂▃▃▂▂ |
green buildings | 2015 | 3.11 | 2022 | 2025 | ▂▂▂▂▂▂▂▃▃▃▃ |
energy efficiency | 2015 | 6.74 | 2023 | 2025 | ▂▂▂▂▂▂▂▂▃▃▃ |
intelligent buildings | 2018 | 3.17 | 2023 | 2025 | ▂▂▂▂▂▂▂▂▃▃▃ |
Topic No. (Percentage) | Top 20 Salient Terms | Theme |
---|---|---|
Topic-1 (40.71%) | prediction, machine learning, evaluation, propose, building, project, green building, neural network, compressive, artificial neural network, carbon emission, environment, artificial intelligence, efficient, employ, development, comprehensive evaluation, design, building industry, energy | AI-based Building Performance Prediction and Carbon Emission Assessment |
Topic-2 (30.23%) | multiobjective optimization, machine learning, energy, energy consumption, heat loss, green building, building, neural network, cool load, prediction, efficient, carbon emission, efficiency, evaluation, apply, environment, estimate, artificial neural network, novel, improve | Intelligent Multi-objective Optimization of Building Energy Systems |
Topic-3 (29.07%) | building, design, multiobjective optimization, neural network, artificial intelligence, energy efficiency, propose, material, artificial neural network, sustainable development, management, environment, energy, resource, address, genetic, efficiency, combine, green building, development | Sustainable Building System Design and Intelligent Resource Management |
Time Slice | Topic 1–Word | Topic 1–Weight | Topic 2–Word | Topic 2–Weight | Topic 3–Word | Topic 3–Weight |
---|---|---|---|---|---|---|
2015–2021 | prediction | 0.123 | multiobjective optimization | 0.217 | building | 0.177 |
evaluation | 0.119 | machine learning | 0.121 | design | 0.127 | |
neural network | 0.07 | energy | 0.077 | artificial intelligence | 0.066 | |
propose | 0.067 | propose | 0.062 | ANN | 0.053 | |
project | 0.06 | green building | 0.042 | neural network | 0.045 | |
green building | 0.059 | building | 0.041 | sustainable development | 0.042 | |
machine learning | 0.057 | energy consumption | 0.04 | material | 0.04 | |
building | 0.04 | heat loss | 0.039 | propose | 0.035 | |
compressive | 0.033 | neural network | 0.035 | multiobjective optimization | 0.034 | |
ANN | 0.03 | thermal comfort | 0.028 | energy efficiency | 0.029 | |
2022–2023 | prediction | 0.132 | multiobjective optimization | 0.154 | building | 0.121 |
machine learning | 0.125 | machine learning | 0.111 | design | 0.099 | |
evaluation | 0.090 | energy consumption | 0.072 | neural network | 0.074 | |
project | 0.060 | propose | 0.066 | multiobjective optimization | 0.051 | |
building | 0.056 | heat loss | 0.063 | artificial intelligence | 0.051 | |
green building | 0.053 | energy | 0.054 | propose | 0.048 | |
propose | 0.048 | green building | 0.052 | energy efficiency | 0.046 | |
neural network | 0.039 | neural network | 0.046 | material | 0.040 | |
compressive | 0.031 | thermal comfort | 0.037 | ANN | 0.036 | |
sustainable development building | 0.022 | building | 0.035 | sustainable development | 0.032 | |
2024–2025 | prediction | 0.198 | machine learning | 0.172 | multiobjective optimization | 0.099 |
machine learning | 0.117 | multiobjective optimization | 0.120 | energy efficiency | 0.073 | |
evaluation | 0.060 | propose | 0.061 | building | 0.058 | |
building | 0.051 | cool load | 0.055 | neural network | 0.052 | |
carbon emission | 0.038 | prediction | 0.050 | design | 0.052 | |
compressive | 0.036 | building | 0.049 | propose | 0.047 | |
propose | 0.032 | green building | 0.049 | environment | 0.042 | |
artificial intelligence | 0.031 | energy | 0.043 | material | 0.040 | |
ANN | 0.028 | heat loss | 0.043 | artificial intelligence | 0.038 | |
sustainable development building | 0.027 | energy consumption | 0.038 | management | 0.035 |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wu, J.; Wang, Q.; Guo, Z.; Peng, C. AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects. Buildings 2025, 15, 1754. https://doi.org/10.3390/buildings15101754
Wu J, Wang Q, Guo Z, Peng C. AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects. Buildings. 2025; 15(10):1754. https://doi.org/10.3390/buildings15101754
Chicago/Turabian StyleWu, Jie, Qinge Wang, Zhenxu Guo, and Chunyan Peng. 2025. "AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects" Buildings 15, no. 10: 1754. https://doi.org/10.3390/buildings15101754
APA StyleWu, J., Wang, Q., Guo, Z., & Peng, C. (2025). AI-Driven Green Building Technology Innovation: Knowledge Structure, Evolution Trends, Research Paradigms and Future Prospects. Buildings, 15(10), 1754. https://doi.org/10.3390/buildings15101754