Ecological benefit evaluation and design optimization of urban architectural complexes based on fuzzy logic
DOI: https://doi.org/10.3846/jeelm.2026.28324Abstract
Assessing ecological advantages and refining urban architectural designs are essential for sustainable urban growth. A lot of study has been done on energy efficiency, resource conservation, and green materials, but there is still no complete framework for systematic ecological assessment and improvement. This paper presents the Fuzzy Logic-Based Ecological Design Optimization Framework (FLEDOF), which amalgamates environmental, economic, and social sustainability while tackling the uncertainties intrinsic to urban design. FLEDOF gathers information about energy use, carbon emissions, air quality, and the effects on ecosystems. It then uses fuzzy multi-criteria decision-making methods to rank and improve design options. The simulation findings show that energy efficiency has improved by 8–10% compared to traditional approaches, carbon emissions have gone down, and resources have been used more efficiently. The accuracy of data processing has even gone up to 98.5% under different building situations. The framework gives architects, urban planners, and policymakers useful information, such as ecological scores, performance measures, and implementation plans, so they may make smart choices. FLEDOF uses advanced fuzzy logic, iterative optimization, and multi-database integration to create a methodical, data-driven way to construct cities that will last. Its practical consequences support eco-friendly urban infrastructures and align with global sustainability goals, providing a strong basis for assessing and improving the ecological performance of urban architectural complexes.
Keywords:
energy efficiency, sustainable urban growth, urban architectural complexes, carbon emission, multi-criteria decision-making, eco-friendly infrastructures, ecological benefitsHow to Cite
Share
Funding data
-
Anhui Provincial Quality Engineering Project
Grant numbers: 2025zytsct06
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alghassab, M. A. (2024). Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study. Energy Reports, 11, 1212–1224. https://doi.org/10.1016/j.egyr.2023.12.039
Ali-Fakulti, M. F., & Alsayayaydeh, J. A. J. (2024). Exploring pattern mining with FCM algorithm for predicting female athlete behaviour in sports analytics. PatternIQ Mining, 1(1), 45–56. https://doi.org/10.70023/piqm245
Ang, Y. Q., Berzolla, Z. M., Letellier-Duchesne, S., & Reinhart, C. F. (2023). Carbon reduction technology pathways for existing buildings in eight cities. Nature Communications, 14, Article 1689. https://doi.org/10.1038/s41467-023-37131-6
Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air-pollution prediction in smart city, deep learning approach. Journal of Big Data, 8, Article 161. https://doi.org/10.1186/s40537-021-00548-1
Bressane, A., Fengler, F. H., de Castro Medeiros, L. C., Urban, R. C., & Negri, R. G. (2024). Enhancing energy sustainability of building projects through nature-based solutions: A fuzzy-based decision support system. Nature-Based Solutions, 5, Article 100107. https://doi.org/10.1016/j.nbsj.2023.100107
Calo, S., Bistaffa, F., Jonsson, A., Gómez, V., & Viana, M. (2024). Spatial air quality prediction in urban areas via message passing. Engineering Applications of Artificial Intelligence, 133, Article 108191. https://doi.org/10.1016/j.engappai.2024.108191
Ćesić, M., Rogulj, K., Kilić Pamuković, J., & Krtalić, A. (2024). A systematic review on fuzzy decision support systems and multi-criteria analysis in urban heat island management. Energies, 17(9), Article 2013. https://doi.org/10.3390/en17092013
Das, S., Choudhury, M. R., Chatterjee, B., Das, P., Bagri, S., Paul, D., Bera, M., & Dutta, S. (2024). Unraveling the urban climate crisis: Exploring the nexus of urbanization, climate change, and their impacts on the environment and human well-being – A global perspective. AIMS Public Health, 11(3), 963–1001. https://doi.org/10.3934/publichealth.2024050
Di Santo, N., Guante Henriquez, L., Dotelli, G., & Imperadori, M. (2023). Holistic approach for assessing buildings’ environmental impact and user comfort from early design: A method combining life cycle assessment, BIM, and active house protocol. Buildings, 13(5), Article 1315. https://doi.org/10.3390/buildings13051315
Ding, D., Wu, J., Zhu, S., Mu, Y., & Li, Y. (2021). Research on AHP-based fuzzy evaluation of urban green building planning. Environmental Challenges, 5, Article 100305. https://doi.org/10.1016/j.envc.2021.100305
Dong, X., Yi, W., Yuan, P., & Song, Y. (2023). Optimization and trade-off framework for coupled green-grey infrastructure considering environmental performance. Journal of Environmental Management, 329, Article 117041. https://doi.org/10.1016/j.jenvman.2022.117041
Google Research. (n.d.). Open Buildings 2.5D Temporal Dataset. https://sites.research.google/gr/open-buildings/temporal/
Hafez, F. S., Sa’di, B., Safa-Gamal, M., Taufiq-Yap, Y. H., Alrifaey, M., Seyedmahmoudian, M., Stojcevski, A., Horan, B., & Mekhilef, S. (2023). Energy efficiency in sustainable buildings: A systematic review with taxonomy, challenges, motivations, methodological aspects, recommendations, and pathways for future research. Energy Strategy Reviews, 45, Article 101013. https://doi.org/10.1016/j.esr.2022.101013https://doi.org/10.1016/j.esr.2023.101013
Han, J., Liu, H., Xiong, H., & Yang, J. (2023). Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network. IEEE Transactions on Knowledge and Data Engineering, 35(5), 5230–5243. https://doi.org/10.1109/TKDE.2022.3149815
Hsueh, S.-L., Sun, Y., Zhang, Y., Xiao, N., & Meen, T.-H. (2022). Decision-making model based on discriminant analysis fuzzy method for low-carbon and eco-friendly residence design: Case study of Conghua District, Guangzhou, China. Buildings, 12(6), Article 815. https://doi.org/10.3390/buildings12060815
Ji, Y., Sheng, Q., & Zhu, Z. (2023). Assessment of ecological benefits of urban green spaces in Nanjing City, China, based on the entropy method and the coupling harmonious degree model. Sustainability, 15(13), Article 10516. https://doi.org/10.3390/su151310516
Jones, J., & Adewumi, A. S. (2025). Exploring the values of sustainability and the cost of going green: A case of Building Research Establishment Environmental Assessment Method (BREEAM). Ecological Civilization, 2(1), Article 10018. https://doi.org/10.70322/ecolciviliz.2024.10018
Joshua, S. R., Yeon, A. N., Park, S., & Kwon, K. (2024). A hybrid machine learning approach: Analyzing energy potential and designing solar fault detection for an AIoT-based solar–hydrogen system in a university setting. Applied Sciences, 14(18), Article 8573. https://doi.org/10.3390/app14188573
Lazarević, D., Popović, Đ., Çodur, M. Y., & Dobrodolac, M. (2024). Fuzzy logic approach for evaluating electromobility alternatives in last-mile delivery: Belgrade as a case study. Energies, 17(24), Article 6307. https://doi.org/10.3390/en17246307
Li, X., Peng, L., Hu, Y., Shao, J., & Chi, T. (2016). Deep learning architecture for air quality predictions. Environmental Science and Pollution Research International, 23, 22408–22417. https://doi.org/10.1007/s11356-016-7812-9
Lu, Y.-J., & Li, C.-T. (2020, November 17–20). AGSTN: Learning attention-adjusted graph spatio-temporal networks for short-term urban sensor value forecasting. In Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1148–1153). Sorrento, Italy. IEEE. https://doi.org/10.1109/ICDM50108.2020.00140
Mao, W., Jiao, L., Wang, W., Wang, J., Tong, X., & Zhao, S. (2021). A hybrid integrated deep learning model for predicting various air pollutants. GIScience & Remote Sensing, 58(8), 1395–1412. https://doi.org/10.1080/15481603.2021.1988429
Mastrucci, A., Niamir, L., Boza-Kiss, B., Bento, N., Wiedenhofer, D., Streeck, J., Pachauri, S., Wilson, C., Chatterjee, S., Creutzig, F., Dukkipati, S., Feng, W., Grubler, A., Jupesta, J., Kumar, P., Marangoni, G., Saheb, Y., Shimoda, Y., Shoai-Tehrani, B., ... van Ruijven, B. (2023). Modeling low energy demand futures for buildings: Current state and research needs. Annual Review of Environment and Resources, 48(1), 761–792. https://doi.org/10.1146/annurev-environ-112321-102921
Mrówczyńska, M., Skiba, M., Sztubecka, M., Bazan-Krzywoszańska, A., Kazak, J. K., & Gajownik, P. (2021). Scenarios as a tool supporting decisions in urban energy policy: The analysis using fuzzy logic, multi-criteria analysis and GIS tools. Renewable and Sustainable Energy Reviews, 137, Article 110598. https://doi.org/10.1016/j.rser.2020.110598
Naghibi Iravani, S., Karimimansoob, V., Sohrabi, S. A., Gheitarani, N., & Dehghan, S. (2024). Applying fuzzy logic and analysis hierarchy process (AHP) in the design of residential spaces: Case of study: Arak city. European Online Journal of Natural and Social Sciences, 13(2), 144–160.
Ouyang, X., Yang, Y., Zhang, Y., & Zhou, W. (2021). Spatial-temporal dynamic graph convolution neural network for air quality prediction. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Shenzhen, China. IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534167
Parekh, R., Smith, C., & Brown, N. (2024). Deep reinforcement learning for multi-criteria optimization in BIM-supported sustainable building design. International Journal of Science and Research Archive, 13(1), 1030–1048. https://doi.org/10.30574/ijsra.2024.13.1.1775
Saez, M., & Barceló, M. A. (2022). Spatial prediction of air pollution levels using a hierarchical Bayesian spatiotemporal model in Catalonia, Spain. Environmental Modelling & Software, 151, Article 105369. https://doi.org/10.1016/j.envsoft.2022.105369
Sahoo, S. K., & Choudhury, B. B. (2023). Evaluating material alternatives for low-cost robotic wheelchair chassis: A combined CRITIC, EDAS, and COPRAS framework. Jordan Journal of Mechanical & Industrial Engineering, 17(4), 653–669. https://doi.org/10.59038/jjmie/170419
Shaamala, A., Yigitcanlar, T., Nili, A., & Nyandega, D. (2024). Algorithmic green infrastructure optimisation: Review of artificial intelligence driven approaches for tackling climate change. Sustainable Cities and Society, 101, Article 105182. https://doi.org/10.1016/j.scs.2024.105182
Uzair, M., & Kazmi, S. A. A. (2023). A multi-criteria decision model to support sustainable building energy management system with intelligent automation. Energy and Buildings, 301, Article 113687. https://doi.org/10.1016/j.enbuild.2023.113687
Wang, P., Liu, Z., Zhang, X., Hu, M., Zhang, L., & Fan, J. (2023a). Adaptive dynamic building envelope integrated with phase change material to enhance the heat storage and release efficiency: A state-of-the-art review. Energy and Buildings, 286, Article 112928. https://doi.org/10.1016/j.enbuild.2023.112928
Wang, Y., Peng, H., Wang, G., Tang, X., Wang, X., & Liu, C. (2023b). Monitoring industrial control systems via spatio-temporal graph neural networks. Engineering Applications of Artificial Intelligence, 122, Article 106144. https://doi.org/10.1016/j.engappai.2023.106144
Wang, L., Sun, C., & Wang, M. (2024). Optimization strategies for waterfront plant landscapes in traditional villages: A scenic beauty estimation–entropy weighting method analysis. Sustainability, 16(16), Article 7140. https://doi.org/10.3390/su16167140
Wu, Y., Zhuang, D., Labbe, A., & Sun, L. (2021). Inductive graph neural networks for spatiotemporal kriging. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4478–4485. https://doi.org/10.1609/aaai.v35i5.16575
Xayasouk, T., Lee, H., & Lee, G. (2020). Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models. Sustainability, 12(6), Article 2570. https://doi.org/10.3390/su12062570
Zeinalnezhad, M., Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2020). Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System. Journal of Cleaner Production, 261, Article 121218. https://doi.org/10.1016/j.jclepro.2020.121218
Zhang, K., Zheng, Y., & Qi, Y. (2017). Deep air: Forecasting air pollution in Beijing, China. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM ‘17), 2715–2723.
Zhang, Z., Zhang, S., Zhao, X., Chen, L., & Yao, J. (2022). Temporal difference-based graph transformer networks for air quality PM2.5 prediction: A case study in China. Frontiers in Environmental Science, 10, Article 924986. https://doi.org/10.3389/fenvs.2022.924986
Zhao, W., Peng, P., Guo, B., Deng, X., & Wu, W. (2023). Comprehensive social cultural and economic benefits of green buildings based on improved AHP–FCE method. Buildings, 13(2), Article 311. https://doi.org/10.3390/buildings13020311
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Share
Funding data
-
Anhui Provincial Quality Engineering Project
Grant numbers: 2025zytsct06