A systematic review on artificial intelligence applications in architecture
Keywords:algorithm, architectural design, architecture, artificial intelligence, computitional design
Since the advent and usage of artificial intelligence approaches in architecture, a significant number of studies have focused on integrating technological solutions to architectural issues. Artificial intelligence applications in architectural design range from intelligent material design to architectural plan solutions. The ubiquity and distribution of research in this field, as well as the rising use of artificial intelligence techniques to solve design challenges, require an analytical classification of the essential literature review. This article presents a descriptive and analytical review of the work on artificial intelligence applications in architecture. A strong review has been made that identifies and addresses the gaps in artificial intelligence and architecture; and the literature review is transformed into statistical plots. The study's findings indicate a growing interest in artificial intelligence in the field of architecture. There is a need for novel research to be conducted in these areas using advanced technology and techniques.
- Abediniangerabi, B., Shahandashti, S. M., & Makhmalbaf, A. (2020). A data-driven framework for energy-conscious design of building facade systems. Journal of Building Engineering, 29, 101172. https://doi.org/10.1016/j.jobe.2020.101172
- Aksoy, Y., & Çağdaş, G. (2014). A model for sustainable site layout design with Pareto genetic algorithm: SSPM. Journal of Cleaner Production, 64, 436-447. https://doi.org/10.1016/j.jclepro.2013.09.032
- Aldemir, B. C. (2014). Bina Kabuğunun Biçimlenmesinde Doğal Süreçlere Dayalı Üretken Yaklaşımlar [Productive Approaches Based on Natural Processes for the Formation of Building Shells] (Doctoral dissertation). Fen Bilimleri Enstitüsü, İstanbul Teknik Üniversitesi.
- Artut, S. (2019). Yapay zeka olgusunun güncel sanat çalışmalarındaki açılımları [The implications of artificial intelligence on contemporary art practices]. İnsan ve İnsan, 6(22), 767-783. https://dergipark.org.tr/tr/download/article-file/832049
- Aslan, Y. (2016). The effect of cross-curricular instruction on reading comprehension. Universal Journal of Educational Research, 4(8), 1797-1801. https://doi.org/10.13189/ujer.2016.040822
- Atalay, M., & Çelik, E. (2017). Büyük veri analizinde yapay zekâ ve makine öğrenmesi uygulamaları [Applications of artificial intelligence and machine learning in big data analysis]. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172. https://doi.org/10.20875/sb.27868
- Baghdadi, A., Heristchian, M., & Kloft, H. (2020). Design of prefabricated wall-floor building systems using meta-heuristic optimization algorithms. Automation in Construction, 114, 103156.
- Banihashemi, S., Ding, G., & Wang, J. (2017). Developing a hybrid model of prediction and classification algorithms for building energy consumption. Energy Procedia, 110, 371-376.
- Bao, D. W., Yan, X., & Xie, Y. M. (2022). Encoding topological optimisation logical structure rules into multi-agent system for architectural design and robotic fabrication. International Journal of Architectural Computing, 20(1), 7-17.
- Baydoğan, M. Ç., & Şener, S. M. (2014). Tip imar yönetmeliğine uygun vaziyet planlarının yapay arı kolonisi yöntemiyle eniyilenmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(2), 133-140.
- Bei, W., Guo, M., & Huang, Y. (2019). A spatial adaptive algorithm framework for building pattern recognition using graph convolutional networks. Sensors, 19(24), 5518.
- Bingöl, K., Er Akan, A., Örmecioğlu, H. T., & Er, A. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210.
- Bohanec, M., & Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica, 1(1), 145-157.
- Boshernitsan, M., & Downes, M. S. (2004). Visual programming languages: A survey. Computer Science Division, University of California.
- Buruzs, A., Šipetić, M., Blank-Landeshammer, B., & Zucker, G. (2022). IFC BIM Model Enrichment with Space Function Information Using Graph Neural Networks. Energies, 15(8), 2937.
- Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9(2), 287-300.
- Cai, C., & Li, B. (2021). Training deep convolution network with synthetic data for architectural morphological prototype classification. Frontiers of Architectural Research, 10(2), 304-316.
- Carbonari, A., Corneli, A., Di Giuda, G. M., Ridolfi, L., & Villa, V. (2019). A decision support system for multi-criteria assessment of large building stocks. Journal of civil engineering and management, 25(5), 477-494.
- Chardon, S., Brangeon, B., Bozonnet, E., & Inard, C. (2016). Construction cost and energy performance of single-family houses: From integrated design to automated optimization. Automation in Construction, 70, 1-13.
- Chardon, S., Brangeon, B., Bozonnet, E., Inard, C., Montecot, R., Développement, P. R. O. G. E. M. I., & Saujon, F. (2015, December). A Multi-Objective Design Tool for the French Detached House Market: Cost and Energy Performance Optimization. Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association, Hyderabad, India.
- Chatzikonstantinou, I., & Sariyildiz, I. S. (2017). Addressing design preferences via auto-associative connectionist models: Application in sustainable architectural Façade design. Automation in Construction, 83, 108-120.
- Chen, X., & Yang, H. (2017). A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios. Applied energy, 206, 541-557.
- Chen, L., & Pan, W. (2015). A BIM-integrated fuzzy multi-criteria decision-making model for selecting low-carbon building measures. Procedia engineering, 118, 606-613.
- Chokwitthaya, C., Zhu, Y., Dibiano, R., & Mukhopadhyay, S. (2019). Combining context-aware design-specific data and building performance models to improve building performance predictions during design. Automation in construction, 107, 102917.
- Chou, J. S., & Bui, D. K. (2014). Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy and Buildings, 82, 437-446.
- Cichocka, J. M., Migalska, A., Browne, W. N., & Rodriguez, E. (2017, July). SILVEREYE–the implementation of Particle Swarm Optimization algorithm in a design optimization tool. In International Conference on Computer-Aided Architectural Design Futures (pp. 151-169). Springer, Singapore.
- Conrads, U., & Bullock, M. (1976). Programs and manifestoes on 20th-century architecture.
- Corbusier, L. (2007). Toward an architecture. Getty Publications.
- Doukari, O., & Greenwood, D. (2020). Automatic generation of building information models from digitized plans. Automation in Construction, 113, 103129.
- ElBatran, R. M., & Ismaeel, W. S. (2021). Applying a parametric design approach for optimizing daylighting and visual comfort in office buildings. Ain Shams Engineering Journal, 12(3), 3275-3284.
- Gade, A. N., Larsen, T. S., Nissen, S. B., & Jensen, R. L. (2018). REDIS: A value-based decision support tool for renovation of building portfolios. Building and Environment, 142, 107-118.
- Gallo, G., Tuzzolino, G., & Fulvio, W. (2020). The role of Artificial intelligence in architectural design: conversation with designer and researchers. In Conference proceedings of the 7th International Conference on Architecture and Build Environment S. ARCH (pp. 1-8). S. Arch.
- Gan, V. J., Wong, H. K., Tse, K. T., Cheng, J. C., Lo, I. M., & Chan, C. M. (2019). Simulation-based evolutionary optimization for energy-efficient layout plan design of high-rise residential buildings. Journal of Cleaner Production, 231, 1375-1388.
- Guo, Y. (2022). The microscopic visual forms in architectural art design following deep learning. The Journal of Supercomputing, 78(1), 559-577.
- Guo, Z., & Li, B. (2017). Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system. Frontiers of Architectural Research, 6(1), 53-62.
- Güleç, D. (2014). Mimari Tasarım Alanında Kullanıcı Erişilebilirliğinin Genetik Algoritma Ile Optimizasyonu-Ada: Sağlık Kampüsü Uygulaması [Optimization of user accessibility in architectural design using genetic algorithm - Ada: Health campus application] (Doctoral dissertation).
- Gülşen, İ. (2019). İşletmelerde yapay zeka uygulamaları ve faydaları: Perakende sektöründe bir derleme [Artificial intelligence applications and benefits in business: A compilation in the retail sector]. Tüketici ve Tüketim Araştırmaları Dergisi, 11(2), 407-436.
- Han, J. M. (2022). A New Interoperability Framework for Data-Driven Building Performance Simulation (Doctoral dissertation).
- He, Q., Li, Z., Gao, W., Chen, H., Wu, X., Cheng, X., & Lin, B. (2021). Predictive models for daylight performance of general floorplans based on CNN and GAN: a proof-of-concept study. Building and Environment, 206, 108346.
- Jalaei, F., Jrade, A., & Nassiri, M. (2015). Integrating decision support system (DSS) and building information modeling (BIM) to optimize the selection of sustainable building components. Journal of Information Technology in Construction (ITcon), 20(25), 399-420.
- Jia, M. (2021). Daylight Prediction Using GAN: General Workflow, Tool Development and Case Study on Manhattan, New York.
- Jiang, Y., Han, S., & Bai, Y. (2022). Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI. Journal of Architectural Engineering, 28(4), 04022031.
- Kamari, A., Laustsen, C., Peterson, S., & Kirkegaard, P. H. (2018). A BIM-based decision support system for the evaluation of holistic renovation scenarios. Journal of Information Technology in Construction (ITcon), 23(18), 354-380.
- Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
- Karadoğan, A. (2021). Yeraltı Metro İstasyon Yapılarında Algoritmik Tasarım ile Mekan Yerleşim Kararlarının Geliştirilmesi. Yapı Bilgi Modelleme, 3(1), 1-11.
- Kerdan, I. G., & Gálvez, D. M. (2022). ANNEXE: An open-source building energy design optimisation framework using artificial neural networks and genetic algorithms. Journal of Cleaner Production, 371, 133500.
- Kim, H., & Clayton, M. J. (2020). A multi-objective optimization approach for climate-adaptive building envelope design using parametric behavior maps. Building and Environment, 185, 107292.
- Kong, X., Lu, S., & Wu, Y. (2012). A review of building energy efficiency in China during “Eleventh Five-Year Plan” period. Energy policy, 41, 624-635.
- Li, Z., Chen, H., Lin, B., & Zhu, Y. (2018, August). Fast bidirectional building performance optimization at the early design stage. In Building Simulation (Vol. 11, No. 4, pp. 647-661). Tsinghua University Press.
- Li, Z., Dai, J., Chen, H., & Lin, B. (2019, August). An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage. In Building Simulation (Vol. 12, No. 4, pp. 665-681). Tsinghua University Press.
- Liang, R., Ding, W., Zandi, Y., Rahimi, A., Pourkhorshidi, S., & Khadimallah, M. A. (2022). Buildings’ internal heat gains prediction using artificial intelligence methods. Energy and Buildings, 258, 111794.
- Lin, B., Chen, H., Yu, Q., Zhou, X., Lv, S., He, Q., & Li, Z. (2021). MOOSAS–A systematic solution for multiple objective building performance optimization in the early design stage. Building and Environment, 200, 107929.
- Link-1. Wikipedia. (2022, December 13). Evolutionary algorithm. https://en.wikipedia.org/wiki/Evolutionary_algorithm
- Link-2. Wikipedia. (2022, December 13). Generative design. https://en.wikipedia.org/wiki/Generative_design
- Liu, C. (2022). Influencing Factors for an Integrated Model of Green Building Energy Consumption Using BIM Dynamic Simulation and Multiobjective Decision-Making. Mobile Information Systems, 2022.
- Liu, C. P., & Lee, Y. C. (2022). Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN). Automation in Construction, 139, 104234.
- Mangan, S. (2021). Development of a web-based decision support tool for sustainable residential building design and retrofit. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(4), 2153-2172.
- Marcolino, L. S., Gerber, D. J., Kolev, B., Price, S., Pantazis, E., Tian, Y., & Tambe, M. (2015, January). Agents Vote for the Environment: Designing Energy-Efficient Architecture. In AAAI Workshop: Computational Sustainability.
- Mirarchi, C., Lucky, M. N., Ciuffreda, S., Signorini, M., Spagnolo, S. L., Bolognesi, C., & Pavan, A. (2020). An approach for standardization of semantic models for building renovation processes. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 24, 69-76.
- Morbidoni, C., Pierdicca, R., Paolanti, M., Quattrini, R., & Mammoli, R. (2020). Learning from synthetic point cloud data for historical buildings semantic segmentation. Journal on Computing and Cultural Heritage (JOCCH), 13(4), 1-16.
- Mukkavaara, J., & Shadram, F. (2021). An integrated optimization and sensitivity analysis approach to support the life cycle energy trade-off in building design. Energy and Buildings, 253, 111529.
- Mulero-Palencia, S., Álvarez-Díaz, S., & Andrés-Chicote, M. (2021). Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models. Sustainability, 13(12), 6576.
- Müezzinoğlu, C. (2022). Genetik Algoritmalar Aracılığıyla Çevreye Duyarlı Kinetik Yapı Tasarımı Önerisi. Journal of Computational Design, 3(2), 173-196.
- Naji, S., Keivani, A., Shamshirband, S., Alengaram, U. J., Jumaat, M. Z., Mansor, Z., & Lee, M. (2016). Estimating building energy consumption using extreme learning machine method. Energy, 97, 506-516.
- Natephra, W., Yabuki, N., & Fukuda, T. (2018). Optimizing the evaluation of building envelope design for thermal performance using a BIM-based overall thermal transfer value calculation. Building and Environment, 136, 128-145.
- Ng, J. M. Y., Khean, N., Madden, D., Fabbri, A., Gardner, N., Haeusler, M. H., & Zavoleas, Y. (2019). Optimising Image Classification-Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry.
- Nicolson, A.M., & Ross, G.F. (1970). Measurement of the Intrinsic Properties of Materials by Time-Domain Techniques. IEEE Transactions on Instrumentation and Measurement, 19, 377-382.
- Nisztuk, M., & Myszkowski, P. B. (2019). Hybrid evolutionary algorithm applied to automated floor plan generation. International Journal of Architectural Computing, 17(3), 260-283.
- Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Machine learning for energy performance prediction at the design stage of buildings. Energy for Sustainable Development, 66, 12-25.
- Paterson, G., Mumovic, D., Das, P., & Kimpian, J. (2017). Energy use predictions with machine learning during architectural concept design. Science and Technology for the Built Environment, 23(6), 1036-1048.
- Piira, K., Kantorovitch, J., Kannari, L., Piippo, J., & Vu Hoang, N. (2022). Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design. Energies, 15(15), 5408.
- Pilechiha, P., Mahdavinejad, M., Rahimian, F. P., Carnemolla, P., & Seyedzadeh, S. (2020). Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency. Applied Energy, 261, 114356.
- Płoszaj-Mazurek, M., Ryńska, E., & Grochulska-Salak, M. (2020). Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning, convolutional neural network, and parametric design. Energies, 13(20), 5289.
- Pirim, A. G. H. (2006). Yapay zeka. Yaşar Üniversitesi E-Dergisi, 1(1), 81-93.
- Radziszewski, K. (2017, October). Artificial neural networks as an architectural design tool-generating new detail forms based on the Roman Corinthian order capital. In IOP Conference Series: Materials Science and Engineering (Vol. 245, No. 6, p. 062030). IOP Publishing.
- Rahimian, M. (2022). A Data Mining Model and a Real-Time Predictive Software Prototype for the Spatial Design and Planning of High Energy Performance Solar Community Microgrids.
- Rasmussen, S. E., & Doruk, B. (1964). Yaşanan mimari. İstanbul Teknik Üniversitesi Mimarlık Fakültesi.
- Raman, R., & D'Souza, M. (2019). Decision learning framework for architecture design decisions of complex systems and system‐of‐systems. Systems Engineering, 22(6), 538-560.
- Razmi, A., Rahbar, M., & Bemanian, M. (2022). PCA-ANN integrated NSGA-III framework for dormitory building design optimization: Energy efficiency, daylight, and thermal comfort. Applied Energy, 305, 117828.
- Ruiz, E., Pacheco-Torres, R., & Casillas, J. (2017, September). Energy consumption modeling by machine learning from daily activity metering in a hospital. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-7). IEEE.
- Sartori, T., Drogemuller, R., Omrani, S., & Lamari, F. (2021). A schematic framework for life cycle assessment (LCA) and green building rating system (GBRS). Journal of Building Engineering, 38, 102180.
- Seghier, T. E., Lim, Y. W., Harun, M. F., Ahmad, M. H., Samah, A. A., & Majid, H. A. (2022). BIM-based retrofit method (RBIM) for building envelope thermal performance optimization. Energy and Buildings, 256, 111693.
- Seyedzadeh, S., Rahimian, F. P., Rastogi, P., & Glesk, I. (2019). Tuning machine learning models for prediction of building energy loads. Sustainable Cities and Society, 47, 101484.
- Scherz, M., Hoxha, E., Kreiner, H., Passer, A., & Vafadarnikjoo, A. (2022). A hierarchical reference-based know-why model for design support of sustainable building envelopes. Automation in Construction, 139, 104276.
- Schwartz, Y., Raslan, R., Korolija, I., & Mumovic, D. (2021). A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach. International Journal of Architectural Computing, 19(3), 401-430.
- Shen, J., Liu, C., Ren, Y., & Zheng, H. (2020). Machine learning assisted urban filling.
- Si, B., Wang, J., Yao, X., Shi, X., Jin, X., & Zhou, X. (2019). Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms. Advanced Engineering Informatics, 40, 93-109.
- Singh, M. M., Deb, C., & Geyer, P. (2022). Early-stage design support combining machine learning and building information modelling. Automation In Construction, 136, 104147.
- Singaravel, S., Suykens, J., & Geyer, P. (2018). Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. Advanced Engineering Informatics, 38, 81-90.
- Sun, K., Li, S., Chu, S., Zheng, S., & Guo, S. (2015, June). The optimum design of high-rise building structure based on the strength and stiffness of genetic algorithm. In International Conference in Swarm Intelligence (pp. 50-57). Springer, Cham.
- Su, Z., & Yan, W. (2015). A fast genetic algorithm for solving architectural design optimization problems. Ai Edam, 29(4), 457-469.
- Şen, E. (2022). Özgün mimari dokuların ön tasarım sürecinde yeni dokular üretilmesinde tasarım aracı olarak kullanılması: Bursa örneği. (Doctoral dissertation).
- Toffolo, A., & Benini, E. (2003). Genetic diversity as an objective in multi-objective evolutionary algorithms. Evolutionary computation, 11(2), 151-167.
- Toniolo, A., & Leon, M. (2017, November). Towards computational dialogue types for BIM collaborative design: an initial study. CEUR Workshop Proceedings.
- Uzun, C. (2020). Yapay zeka ve mimarlık etkileşimi üzerine bir çalışma: Üretken çekişmeli ağ algoritması ile otonom mimari plan üretimi ve değerlendirmesi (Doctoral dissertation, Fen Bilimleri Enstitüsü).
- Vitruvius, P. (2005). Vitruvius: mimarlık üzerine on kitap, trans.
- Wang, H., Ma, C., & Zhou, L. (2009, December). A brief review of machine learning and its application. In 2009 international conference on information engineering and computer science (pp. 1-4). IEEE.
- Wang, L., Janssen, P., Chen, K. W., Tong, Z., & Ji, G. (2019). Subtractive building massing for performance-based architectural design exploration: a case study of daylighting optimization. Sustainability, 11(24), 6965.
- Wang, Z., Sacks, R., & Yeung, T. (2022). Exploring graph neural networks for semantic enrichment: Room type classification. Automation in Construction, 134, 104039.
- Wortmann, T. (2019). Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization. Journal of Computational Design and Engineering, 6(3), 414-428.
- Xia, B., Li, X., Shi, H., Chen, S., & Chen, J. (2020). Style classification and prediction of residential buildings based on machine learning. Journal of Asian Architecture and Building Engineering, 19(6), 714-730.
- Xia, X., & Tong, Z. (2020). A Machine Learning-Based Method for Predicting Urban Land Use.
- Xiong, W., Zhang, P., Sander, P. V., & Joneja, A. (2022). ShapeArchit: Shape-Inspired Architecture Design with Space Planning. Computer-Aided Design, 142, 103120.
- Xu, M. (2022). Simulation? Machine Learning? Simulation X Machine Learning?: A decision system for research integrating building physic simulation and machine learning methods in the early design stage (Doctoral dissertation).
- Yan, X., Bao, D., Zhou, Y., Xie, Y., & Cui, T. (2022). Detail control strategies for topology optimization in architectural design and development. Frontiers of Architectural Research, 11(2), 340-356.
- Yi, Y. K. (2019). Building facade multi-objective optimization for daylight and aesthetical perception. Building and Environment, 156, 178-190.
- Yang, L., & Chibiao, H. (2022). A generative design method of building layout generated by path. Applied Mathematics and Nonlinear Sciences, 7(2), 825-848.
- Yousif, S., Bolojan, D., Anastasia, G., Jeroen, A., & Adam, F. (2021). Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems. In The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) (Vol. 1, pp. 151-160).
- Zhang, R., Tong, H., Huang, W., & Zhang, R. (2019). A generative design method for the functional layout of town planning based on multi-agent system. INTELLIGENT & INFORMED, 15, 231
- Zhang, W. J., Yang, G., Lin, Y., Ji, C., & Gupta, M. M. (2018, June). On definition of deep learning. In 2018 World automation congress (WAC) (pp. 1-5). IEEE.
- Zhao, S., Wang, L., Qian, X., & Chen, J. (2022). Enhancing performance-based generative architectural design with sketch-based image retrieval: a pilot study on designing building facade fenestrations. The Visual Computer, 38(8), 2981-2997.
- Zheng, H., Keyao, A. N., Jingxuan, W. E. I., & Yue, R. E. N. (2020, August). Apartment floor plans generation via generative adversarial networks. In 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2020): RE: Anthropocene, Design in the Age of Humans (pp. 601-610). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA).
- Zheng, H. (2022). Geometry and Topology: Building Machine Learning Surrogate Models with Graphic Statics Method (Doctoral dissertation, University of Pennsylvania).
- Zheng, H., & Yuan, P. F. (2021). A generative architectural and urban design method through artificial neural networks. Building and Environment, 205, 108178.
- Zhou, Y., & Park, H. J. (2021). Sketch with Artificial Intelligence (AI)-A Multimodal AI Approach for Conceptual Design.
- Zou, Y., Zhan, Q., & Xiang, K. (2021). A comprehensive method for optimizing the design of a regular architectural space to improve building performance. Energy Reports, 7, 981-996.
- Zhuang, D., Zhang, X., Lu, Y., Wang, C., Jin, X., Zhou, X., & Shi, X. (2021). A performance data integrated BIM framework for building life-cycle energy efficiency and environmental optimization design. Automation in Construction, 127, 103712.
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