Developing an integrated conceptual framework for the interaction between bionic architecture and digital twin technology
Majid Ahmadnejad Karimi
1
(
)
Asem Sharbaf
2
(
)
Keywords: Bionic Architecture, Digital Twin, Integrated Conceptual Framework, Real-time Feedback, Continuous Improvement,
Abstract :
Bionic architecture, inspired by nature’s adaptive and efficient systems, is increasingly being explored in conjunction with digital twin technology to simulate and optimize built environments before physical implementation. This emerging synergy promises enhanced architectural intelligence; however, current approaches often lack a cohesive conceptual integration between bionic principles and digital twin components. This study aims to develop an integrated conceptual framework that enables a structured interaction between bionic architecture and digital twin systems, incorporating both bioinspired and digital elements within a unified model. Adopting a descriptive–analytical methodology based on a systematic literature review and thematic synthesis, the research identifies and classifies key components from each domain—such as biomimetic algorithms, sensor feedback loops, physical–virtual entities, and data-driven control mechanisms. These elements are organized into a three-layered process diagram and synthesized into a dual digital twin model comprising a biological twin (Twin A) and an architectural twin (Twin B). The framework is grounded in two core mechanisms: real-time feedback and iterative learning, supported by the integration of reinforcement learning (RL) and transfer learning (TL) algorithms. RL enables Twin A to optimize behavioral strategies through continuous environmental interaction, while TL allows Twin B to adapt architectural responses across different scenarios without retraining. The proposed closed-loop system enhances adaptability, responsiveness, and efficiency across both physical and digital domains, thereby establishing a novel paradigm of bio-cyber-architectural cognition. This framework not only contributes a theoretical foundation for intelligent architecture but also opens pathways for empirical experimentation, advanced BIM–digital twin integration, and interdisciplinary architectural innovation.
References:
1) Agrawal, A., Fischer, M., & Singh, V. (2022). Digital twin: From concept to practice. Journal of Management in Engineering, 38(3), 06022001. doi:10.1061/(ASCE)ME.1943-5479.0001034
2) Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Advanced Engineering Informatics, 47, 101225. doi:10.1016/j.aei.2020.101225.
3) Al-Ali, A. R., Gupta, R., Zaman Batool, T., Landolsi, T., Aloul, F., & Al Nabulsi, A. (2020). Digital twin conceptual model within the context of internet of things. Future Internet, 12(10), 163. doi:10.3390/fi12100163.
4) Al-Obaidi, K. M., Ismail, M. A., Hussein, H. and Rahman, A. M. A. (2017). Biomimetic building skins: An adaptive approach. Renewable and Sustainable Energy Reviews, 79, 1472-1491. doi:10.1016/j.rser.2017.05.028.
5) Asghar, Q., & Naqvi, S. M. Z. A. (2019). BIOMIMICRY PERMEATED ARCHITECTURE PEDAGOGY A METHOD OF INVESTIGATING BIO-MIMICRY AND DIGITAL TECHNIQUES IN TEH ARCHITECTUREAL DESGIN STUDIOS. Department of Architecture & Planning, NED University of Engineering & Technology, City Campus Maulana Din Muhammad Wafai Road, Karachi., 27 (2), 34–47. https://doi.org/10.53700/jrap2722019_4.
6) Autodesk. (2021). Digital Twins in Construction, Engineering, & Architecture | Autodesk. 2021. https://www.autodesk.com/solutions/digital-twin/architecture-engineering-construction.
7) Bar-Cohen, Y. (2005). Biomimetics: Biologically inspired technologies. CRC Press.
8) Bernhardt, J. R., O'Connor, M. I., Sunday, J. M., & Gonzalez, A. (2020). Life in fluctuating environments. Philosophical Transactions of the Royal Society B, 375(1814), 20190454. doi:10.1098/rstb.2019.0454.
9) Botín-Sanabria, D. M., Mihaita, A. S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., and Lozoya-Santos, J. D. J. (2022). 4.4 [JP] Digital twin technology challenges and applications: A comprehensive review. Remote Sensing, 14(6), 1335. doi:10.3390/rs14061335.
10) Bottani, E., Vignali, G., & Tancredi, G. P. C. (2020, June). A digital twin model of a pasteurization system for food beverages: Tools and architecture. In 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-8). IEEE. https://doi.org/10.1109/ice/itmc49519.2020.9198625.
11) Botton-Amiot, G., Martinez, P., & Sprecher, S. G. (2023). Associative learning in the cnidarian Nematostella vectensis. Proceedings of the National Academy of Sciences, 120(13), e2220685120. doi:10.1073/pnas.2220685120.
12) Campos, A., Lozoya-Santos, J., Vargas-Martínez, A., Ramirez-Mendoza, R. A., & Morales-Menendez, R. (2019). Digital twin applications: a review. pp. 606–611.
13) Cao, X., Ren, X., Zhao, T., Li, Y., Xiao, D. and Fang, D. (2021). Numerical and theoretical analysis of the dynamic mechanical behavior of a modified rhombic dodecahedron lattice structure. International Journal of Mechanics and Materials in Design, 17, 271-283. https://doi.org/10.1007/s10999-020-09517-7.
14) Chayaamor-Heil, N. (2018). The Impact of Nature-inspired Algorithms on the Biomimetic Approach in Architectural and Urban Design In Conference on Biomimetic and Biohybrid Systems (pp. 97-109). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-95972-6_11.
15) Chayaamor-Heil, N. (2023). From bioinspiration to biomimicry in architecture: Opportunities and challenges. Encyclopedia, 3(1), 202-223. https://doi.org/10.3390/encyclopedia3010014.
16) Chen, L., & Zhang, Y. (2023). Reinforcement learning for adaptive biomimetic façade design Automation in Construction, 152, 104757. doi:10.1016/j.autcon.2023.104757
17) Chiachío, M., Megía, M., Chiachío, J., Fernandez, J., & Jalón, M. L. (2022). Structural digital twin framework: Formulation and technology integration. Automation in Construction, 140, 104333. doi:10.1016/j.autcon.2022.104333.
18) Contreras, G. S., Lezcano, R. A. G., Fernández, E. J. L., Concepción, M., & Gutiérrez, P. (2023). Architecture Learns from Nature. The Influence of Biomimicry and Biophilic Design in Building. Modern Applied Science, 17(1), 1-58. https://doi.org/10.5539/mas.v17n1p58.
19) Cvek, U., Trutschl, M., Kilgore, P. C., Stone II, R. and Clifford, J. L. (2011). Multidimensional visualization techniques for the microarray data. In 2011 15th International Conference on Information Visualization (pp. 241-246). IEEE. https://doi.org/10.1109/iv.2011.37.
20) Cvek, U., Trutschl, M., Stone II, R., Syed, Z., Clifford, J. L. and Sabichi, A. L. (2009). Multidimensional visualization tools for analysis of expression data. International Journal of Computer and Information Engineering, 3(6), 1531-1539. doi:10.5281/zenodo.1074635.
21) De Oliveira, A. R. M., de Arruda, A. J. V., Langella, C., & Perricone, V. (2023). Biomimicry as a Tool for Developing Bioinspired Products: Methods, Process and Application. Ergonomics In Design, 77, 24-39. https://doi.org/10.54941/ahfe1003360.
22) Duan, H., & Tian, F. (2020). The development of standardized models of digital twin. IFAC-PapersOnLine, 53(5), 726-731. https://doi.org/10.1016/j.ifacol.2021.04.164.
23) Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2018). BIM handbook: A guide to Building Information Modeling. Wiley.
24) El-Zeiny, R. M. A. (2012). Biomimicry as a problem solving methodology in interior architecture. Procedia-Social and Behavioral Sciences, 50, 502-512. https://doi.org/10.1016/j.sbspro.2012.08.054.
25) Fawole, O. A. and Opara, U. L. (2013). Developmental changes in maturity indices of pomegranate fruit: a descriptive review Scientia Horticulturae, 159, 152-161. doi: 10.1016/j.scienta.2013.05.016
26) Gabor, T., Belzner, L., Kiermeier, M., Beck, M. T. and Neitz, A. (2016). A simulation-based architecture for smart cyber-physical systems In 2016 IEEE international conference on autonomic computing (ICAC) (pp. 374-379). IEEE. https://doi.org/10.1109/icac.2016.29.
27) Goodwin, T., Xu, J., Celik, N., & Chen, C. H. (2024). Real-time digital twin-based optimization with predictive simulation learning. Journal of Simulation, 18(1), 47-64. doi:10.1080/17477778.2022.2046520.
28) Gopinath, V., Srija, A., & Sravanthi, C. N. (2019, May). Re-design of smart homes with digital twins. In Journal of Physics: Conference Series (Vol. 1228, No. 1, p. 012031). IOP Publishing. doi:10.1088/1742-6596/1228/1/012031.
29) Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication. White paper, 1 (2014), 1-7.
30) Grieves, M. and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, 85-113. https://doi.org/10.1007/978-3-319-38756-7_4.
31) Hales, T. C. (2005). A proof of the Kepler conjecture. Annals of mathematics, 1065-1185.
32) Htet, H. K. K., Usman, I., & Anshori, M. Y. (2023). The Digital Twin Technology: A Scoping Review of Characterization and Implementation Through Business IT Perspectives. Business and Finance Journal, 8(1), 16-29. doi:10.33086/bfj.v8i1.3662.
33) Huang, H., Ji, T., & Xu, X. (2024). An adaptable Digital Twin model for manufacturing. Manufacturing Letters, 41, 1163-1169. https://doi.org/10.1016/j.mfglet.2024.09.142.
34) Huang, H. J., Wu, Z. and Zhi, L. H. Z. (2011). Architectural Design of Bionic Structure and Biomimetic Materials. Advanced Materials Research, 314, 1991-1994. https://doi.org/10.4028/www.scientific.net/amr.314-316.1991.
35) Huang, J. Y. and Siao, S. T. (2016). Development of an integrated bionic design system. Journal of Engineering, Design and Technology, 14(2), 310-327. doi: 10.1108/jedt-08-2014-0057.
36) Iliuţă, M. E., Moisescu, M. A., Pop, E., Ionita, A. D., Caramihai, S. I., & Mitulescu, T. C. (2024). Digital Twin—A Review of the Evolution from Concept to Technology and Its Analytical Perspectives on Applications in Various Fields. Appl Sci, 14(13), 5454. https://doi.org/10.3390/app14135454.
37) Jamei, E., & Vrcelj, Z. (2021). Biomimicry and the built environment, learning from nature’s solutions. Appl Sci, 11(16), 7514. https://doi.org/10.33086/bfj.v8i1.3662.
38) Jeffery, K. J., Wilson, J. J., Casali, G. and Hayman, R. M. (2015). Neural encoding of large-scale three-dimensional space—properties and constraints. Front. Psychol., 6, 927.
39) Jeong, D. Y., Baek, M. S., Lim, T. B., Kim, Y. W., Kim, S. H., Lee, Y. T., & Lee, I. B. (2022). Digital twin: Technology evolution stages and implementation layers with technology elements. Ieee Access, 10, 52609-52620. doi: 10.1109/access.2022.3174220.
40) Jovic, B., Cucakovic, A., Markovic, M., & Cvijic, K. (2021). Biomimetic approach to parametric flower modeling In ICGG 2020-Proceedings of the 19th International Conference on Geometry and Graphics (pp. 244-251). Springer International Publishing. . https://doi.org/10.1007/978-3-030-63403-2_22.
41) Khennak, I., & Drias, H. (2018, May). Data mining techniques and nature-inspired algorithms for query expansion. In Proceedings of the international conference on learning and optimization algorithms: theory and applications (pp. 1-6). https://doi.org/10.1145/3230905.3234631.
42) Körner, A., Mader, A., Saffarian, S., & Knippers, J. (2016, October). Bio-inspired kinetic curved-line folding for architectural applications. In Proceedings of the Acadia Posthuman Frontiers Conference, Ann Arbor, MI, USA (pp. 27-29). https://doi.org/10.52842/conf.acadia.2016.270.
43) Kovatchev, B. P., Renard, E. and Cobelli, C. (2010). Artificial pancreas: Past, present, and future Diabetes Technology & Therapeutics, 12(S1), S9–S14.
44) Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018). Digital Twin in manufacturing: A categorical classification and evaluation. IFAC‐PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
45) Lebedev, M. A., & Nicolelis, M. A. L. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29, 536–546.
46) Lee, J., Azamfar, M., & Bagheri, B. (2021). A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems Manufact. Lett., 27, 87-91. https://doi.org/10.1016/j.mfglet.2021.01.005
47) Lee, J., Bagheri, B., & Kao, H. A. (2021). A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems Manufacturing Letters, 19, 18–23.
48) Lehner, D., Sint, S., Eisenberg, M., & Wimmer, M. (2023). A pattern catalog for augmenting Digital Twin models with behavior. at-Automatisierungstechnik, 71(6), 423-443. https://doi.org/10.1515/auto-2022-0144.
49) Li, L., Gu, F., Li, H., Guo, J., & Gu, X. (2021). Digital twin bionics: A biological evolution-based digital twin approach for rapid product development. IEEE Access, 9, 121507-121521. doi: 10.1109/access.2021.3108218.
50) Ilieva, L., Ursano, I., Traista, L., Hoffmann, B., & Dahy, H. (2022). Biomimicry as a sustainable design methodology—Introducing the ‘Biomimicry for Sustainability’ framework. Biomimetics, 7(2), 37. doi:10.3390/biomimetics7020037.
51) Lin, Y., Li, L., Nugent, Ch., Gao, D., Chen, L., Ning, H., Wang, H., Ali, A., Wang, Y. and Ian. C. (2022). Human Digital Twin: A Survey. research square platform lac.
https://doi.org/10.48550/arXiv.2212.05937.
52) Liu, S., Zheng, P., & Shang, S. (2023). A novel bionic decision-making mechanism for digital twin-based manufacturing system. Manufacturing Letters, 35, 127-131. https://doi.org/10.1016/j.mfglet.2023.08.119
53) Liu, Zh., Liao, M., Norbert Meyendorf, Blasch, E., Yang, Ch., and Tsukada, K. (2023). Digital Twin for Predictive Maintenance In , 6. society of photo optical instrumentation engineers. https://doi.org/10.1117/12.2660270.
54) Liu, S., Lu, Y., Zheng, P., Shen, H., & Bao, J. (2022). Adaptive reconstruction of digital twins for machining systems: A transfer learning approach. Robotics and Computer-Integrated Manufacturing, 78, 102390. doi:10.1016/j.rcim.2022.102390.
55) Liu, S., Bao, J., Lu, Y., Li, J., Lu, S. and Sun, X. (2021). Digital twin modeling method based on biomimicry for machining aerospace components. Journal of manufacturing systems, 58, 180-195. https://doi.org/10.1016/j.jmsy.2020.04.014.
56) Luo, R., Sheng, B., Lu, Y., Huang, Y., Fu, G., & Yin, X. (2023). Digital twin model quality optimization and control methods based on workflow management. Appl Sci, 13(5), 2884. doi:10.3390/app13052884.
57) Mandelbrot, B. B. (1982). The fractal geometry of nature. W. H. Freeman.
58) Mayatskaya, I. A., Yazyeva, S. B., Lapina, A. P., & Davydova, V. V. (2020, August). Architectural bionics and the search for optimal solutions in the design of unique structures. In IOP Conference Series: Materials Science and Engineering (Vol. 913, No. 2, p. 022070). IOP Publishing. doi:10.1088/1757-899x/913/2/022070.
59) Mei, X., Liu, C., & Li, Z. (2024). Research progress on functional, structural and material design of plant-inspired green bionic buildings. Energy and Buildings, 114357. https://doi.org/10.1016/j.enbuild.2024.114357.
60) Menges, A. (2012). Biomimetic design processes in architecture: morphogenetic and evolutionary computational design. Bioinspiration & biomimetics, 7(1), 015003. https://doi.org/10.1088/1748-3182/7/1/015003.
61) Mitkov, R., Logg, A., Pantusheva, M., Naserentin, V. and Petrova-Antonova, D. (2024). The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. doi:10.5194/isprs-annals-x-4-w4-2024-131-2024
62) Mizobuti, V., & Junior, L. C. V. (2020). Bioinspired architectural design based on structural topology optimization. Frontiers of Architectural Research, 9(2), 264-276. https://doi.org/10.1016/j.foar.2019.12.002.
63) Naudet, Y., Baudet, A., & Risse, M. (2021). Human Digital Twin in Industry 4.0: Concept and Preliminary Model In IN4PL (pp. 137-144). 10.5220/0010709000003062.
64) Nie, J., Xu, W. S., Cheng, D. Z., & Yu, Y. L. (2019). Digital twin-based smart building management and control framework. DEStech Trans. Comput. Sci. Eng. https://doi.org/10.12783/dtcse/icaic2019/29395.
65) Pang, T. Y., Pelaez Restrepo, J. D., Cheng, C. T., Yasin, A., Lim, H., & Miletic, M. (2021). Developing a digital twin and digital thread framework for an ‘Industry 4.0’Shipyard. Applied Sciences, 11(3), 1097. https://doi.org/10.3390/app11031097.
66) Pasquale F, Sarma, S. V., & Lybarger, J. (2014). Closed-loop neuroprosthetics: Bidirectional prostheses for restoring sensorimotor function. Frontiers in Neuroscience, 8, 289.
67) Pauwels, P., & Zhang, S. (2019). Parameterization and optimization in parametric design: A review Automation in Construction, 98, 225–236.
68) Pires, F., Cachada, A., Barbosa, J., Moreira, A. P., & Leitão, P. (2019, July). Digital twin in industry 4.0: Technologies, applications and challenges In 2019 IEEE 17th international conference on industrial informatics (INDIN) (Vol. 1, pp. 721-726). IEEE. https://doi.org/10.1109/indin41052.2019.8972134.
69) Pottmann, H. Asperl, A. Hofer, M. Kilian, A. (2007). Architectural Geometry. USA: Bentley Institute Press.
70) Raman, R., & Bashir, R. (2017). Biomimicry, biofabrication, and biohybrid systems: The emergence and evolution of biological design. Advanced healthcare materials, 6(20), 1700496. doi:10.1002/adhm.201700496.
71) Ramzy, N. (2015). Sustainable spaces with psychological values: Historical architecture as reference book for biomimetic models with biophilic qualities. International Journal of Architectural Research: ArchNet-IJAR, 9(2), 248-267. doi:10.26687/archnet-ijar.v9i2.464.
72) Rassölkin, A., Orosz, T., Demidova, G. L., Kuts, V., Rjabtšikov, V., Vaimann, T. and Kallaste, A. (2021). Implementation of digital twins for electrical energy conversion systems in selected case studies. Estonian Academy Publishers, 70, 19–39.
73) Santhaiah, C. and Reddy, R. M. (2014). Role of computers in bioinformatics by using different biological datasets. J. Comput. Eng., 16(2), 80-83. doi:10.9790/0661-16278083.
74) Santos, C. H. D., De Queiroz, J. A., Leal, F., & Montevechi, J. A. B. (2022). Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimize decision making on non-automated process. Journal of Simulation, 16(3), 284-297. https://doi.org/10.1080/17477778.2020.1811172
75) Smith, A., & Jones, B. (2019). Biomimetic design approaches in architecture: Exploring natural models. Journal of Architectural Science, 10(2), 45–60.
76) Song, J., & Sun, S. (2021). Research on architectural form optimization method based on environmental performance-driven design. In Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) (pp. 217-228). Springer Singapore. doi:10.1007/978-981-33-4400-6_21
77) Soori, M., Arezoo, B., & Dastres, R. (2023). Digital twin for smart manufacturing, A review. Sustainable Manufacturing and Service Economics, 100017. doi:10.1016/j.smse.2023.100017.
78) Stadtmann, F., Wassertheurer, H. A. G., & Rasheed, A. (2023, June). Demonstration of a standalone, descriptive, and predictive digital twin of a floating offshore wind turbine. In International Conference on Offshore Mechanics and Arctic Engineering (Vol. 86908, p. V008T09A039). American Society of Mechanical Engineers. https://doi.org/10.1115/omae2023-103112.
79) Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State‐of‐the‐Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. doi: 10.1109/TII.2018.2873186
80) Tekinerdogan, B., & Verdouw, C. (2020). Systems architecture design pattern catalog for developing digital twins. Sensors, 20(18), 5103. https://doi.org/10.3390/s20185103.
81) Umorina, Z. (2019). Application of bionic architecture methods as a future-oriented approach to modern architecture development in Russia In IOP Conference Series: Materials Science and Engineering (Vol. 687, No. 5, p. 055065). IOP Publishing. https://doi.org/10.1088/1757-899x/687/5/055065.
82) Ushizima, D. M., Bale, H. A., Bethel, E. W., Ercius, P., Helms, B. A., Krishnan, H., ... & Yang, C. (2016). IDEAL: I mages Across D domains, E experiments, A algorithms and L earning. Journal of Mathematics, 68, 2963-2972. doi:10.1007/s11837-016-2098-4.
83) Varshabi, N., Arslan Selçuk, S., & Mutlu Avinç, G. (2022). Biomimicry for energy-efficient building design: A bibliometric analysis. Biomimetics, 7(1), 21. doi:10.3390/biomimetics7010021.
84) Viola, J., & Chen, Y. (2020, October). Digital twin-enabled smart control engineering as an industrial AI: A new framework and case study In 2020 2nd International Conference on Industrial Artificial Intelligence (IAI) (pp. 1-6). IEEE. https://doi.org/10.1109/iai50351.2020.9262203.
85) Vorobyeva, O. I. (2018, November). Bionic architecture: back to the origins and a step forward. In IOP Conference Series: Materials Science and Engineering (Vol. 451, No. 1, p. 012145). IOP Publishing. https://doi.org/10.1088/1742-6596/451/1/012145.
86) Wang, Z., Han, K., & Tiwari, P. (2021, July). Digital twin simulation of connected and automated vehicles using the Unity game engine. In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) (pp. 1-4). IEEE. https://doi.org/10.1109/dtpi52967.2021.9540074.
87) Wilken, S., Guerra, R. E., Levine, D., & Chaikin, P. M. (2021). Random close packing as a dynamical phase transition. Phys. Rev. Lett., 127(3), 038002. doi: 10.1103/physrevlett.127.038002.
88) Wilson, B. S. and Dorman, M. F. (2008). Cochlear implants: A remarkable past and a brilliant future. Hearing Research, 242(1–2), 3–21.
89) Xuan, D. T., Huynh, T. V., Hung, N. T., & Thang, V. T. (2023). Applying Digital Twin and Multi-Adaptive Genetic Algorithms in Human–Robot Cooperative Assembly Optimization. Appl Sci, 13(7), 4229. https://doi.org/10.3390/app13074229.
90) Yuan, Y., Yu, X., Yang, X., Xiao, Y., Xiang, B., Wang, Y. (2017). Bionic building energy efficiency and bionic green architecture: A review. Renewable and sustainable energy reviews, 74, 771-787. https://doi.org/10.1016/j.rser.2017.03.004.
91) Zhang, Y., & Li, Z. (2023). Multi-criteria decision analysis for performance evaluation in digital twin systems. International Journal of Production Research, 61(5), 1475–1492. doi:10.1080/00207543.2023.2178901
92) Zhang, X., Kumar, V., & Chauhan, A. (2020). Data integration strategies in architectures: From the IoT to big data. Journal of Computing, 12(3), 112–130.
93) Zheng, F., & Wang, J. (2024). Multi-objective genetic algorithm for energy optimization in biomimetic structures. Renewable Energy, 200, 1234–1245. doi:10.1016/j.renene.2023.10.056
94) Zhou, L., An, C., Shi, J., Lv, Z., & Liang, H. (2021, August). Design and construction integration technology based on the digital twin. In 2021 Power System and Green Energy Conference (PSGEC) (pp. 7-11). IEEE. https://doi.org/10
1) Agrawal, A., Fischer, M., & Singh, V. (2022). Digital twin: From concept to practice. Journal of Management in Engineering, 38(3), 06022001. doi:10.1061/(ASCE)ME.1943-5479.0001034
2) Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Advanced Engineering Informatics, 47, 101225. doi:10.1016/j.aei.2020.101225.
3) Al-Ali, A. R., Gupta, R., Zaman Batool, T., Landolsi, T., Aloul, F., & Al Nabulsi, A. (2020). Digital twin conceptual model within the context of internet of things. Future Internet, 12(10), 163. doi:10.3390/fi12100163.
4) Al-Obaidi, K. M., Ismail, M. A., Hussein, H. and Rahman, A. M. A. (2017). Biomimetic building skins: An adaptive approach. Renewable and Sustainable Energy Reviews, 79, 1472-1491. doi:10.1016/j.rser.2017.05.028.
5) Asghar, Q., & Naqvi, S. M. Z. A. (2019). BIOMIMICRY PERMEATED ARCHITECTURE PEDAGOGY A METHOD OF INVESTIGATING BIO-MIMICRY AND DIGITAL TECHNIQUES IN TEH ARCHITECTUREAL DESGIN STUDIOS. Department of Architecture & Planning, NED University of Engineering & Technology, City Campus Maulana Din Muhammad Wafai Road, Karachi., 27 (2), 34–47. https://doi.org/10.53700/jrap2722019_4.
6) Autodesk. (2021). Digital Twins in Construction, Engineering, & Architecture | Autodesk. 2021. https://www.autodesk.com/solutions/digital-twin/architecture-engineering-construction.
7) Bar-Cohen, Y. (2005). Biomimetics: Biologically inspired technologies. CRC Press.
8) Bernhardt, J. R., O'Connor, M. I., Sunday, J. M., & Gonzalez, A. (2020). Life in fluctuating environments. Philosophical Transactions of the Royal Society B, 375(1814), 20190454. doi:10.1098/rstb.2019.0454.
9) Botín-Sanabria, D. M., Mihaita, A. S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., and Lozoya-Santos, J. D. J. (2022). 4.4 [JP] Digital twin technology challenges and applications: A comprehensive review. Remote Sensing, 14(6), 1335. doi:10.3390/rs14061335.
10) Bottani, E., Vignali, G., & Tancredi, G. P. C. (2020, June). A digital twin model of a pasteurization system for food beverages: Tools and architecture. In 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-8). IEEE. https://doi.org/10.1109/ice/itmc49519.2020.9198625.
11) Botton-Amiot, G., Martinez, P., & Sprecher, S. G. (2023). Associative learning in the cnidarian Nematostella vectensis. Proceedings of the National Academy of Sciences, 120(13), e2220685120. doi:10.1073/pnas.2220685120.
12) Campos, A., Lozoya-Santos, J., Vargas-Martínez, A., Ramirez-Mendoza, R. A., & Morales-Menendez, R. (2019). Digital twin applications: a review. pp. 606–611.
13) Cao, X., Ren, X., Zhao, T., Li, Y., Xiao, D. and Fang, D. (2021). Numerical and theoretical analysis of the dynamic mechanical behavior of a modified rhombic dodecahedron lattice structure. International Journal of Mechanics and Materials in Design, 17, 271-283. https://doi.org/10.1007/s10999-020-09517-7.
14) Chayaamor-Heil, N. (2018). The Impact of Nature-inspired Algorithms on the Biomimetic Approach in Architectural and Urban Design In Conference on Biomimetic and Biohybrid Systems (pp. 97-109). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-95972-6_11.
15) Chayaamor-Heil, N. (2023). From bioinspiration to biomimicry in architecture: Opportunities and challenges. Encyclopedia, 3(1), 202-223. https://doi.org/10.3390/encyclopedia3010014.
16) Chen, L., & Zhang, Y. (2023). Reinforcement learning for adaptive biomimetic façade design Automation in Construction, 152, 104757. doi:10.1016/j.autcon.2023.104757
17) Chiachío, M., Megía, M., Chiachío, J., Fernandez, J., & Jalón, M. L. (2022). Structural digital twin framework: Formulation and technology integration. Automation in Construction, 140, 104333. doi:10.1016/j.autcon.2022.104333.
18) Contreras, G. S., Lezcano, R. A. G., Fernández, E. J. L., Concepción, M., & Gutiérrez, P. (2023). Architecture Learns from Nature. The Influence of Biomimicry and Biophilic Design in Building. Modern Applied Science, 17(1), 1-58. https://doi.org/10.5539/mas.v17n1p58.
19) Cvek, U., Trutschl, M., Kilgore, P. C., Stone II, R. and Clifford, J. L. (2011). Multidimensional visualization techniques for the microarray data. In 2011 15th International Conference on Information Visualization (pp. 241-246). IEEE. https://doi.org/10.1109/iv.2011.37.
20) Cvek, U., Trutschl, M., Stone II, R., Syed, Z., Clifford, J. L. and Sabichi, A. L. (2009). Multidimensional visualization tools for analysis of expression data. International Journal of Computer and Information Engineering, 3(6), 1531-1539. doi:10.5281/zenodo.1074635.
21) De Oliveira, A. R. M., de Arruda, A. J. V., Langella, C., & Perricone, V. (2023). Biomimicry as a Tool for Developing Bioinspired Products: Methods, Process and Application. Ergonomics In Design, 77, 24-39. https://doi.org/10.54941/ahfe1003360.
22) Duan, H., & Tian, F. (2020). The development of standardized models of digital twin. IFAC-PapersOnLine, 53(5), 726-731. https://doi.org/10.1016/j.ifacol.2021.04.164.
23) Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2018). BIM handbook: A guide to Building Information Modeling. Wiley.
24) El-Zeiny, R. M. A. (2012). Biomimicry as a problem solving methodology in interior architecture. Procedia-Social and Behavioral Sciences, 50, 502-512. https://doi.org/10.1016/j.sbspro.2012.08.054.
25) Fawole, O. A. and Opara, U. L. (2013). Developmental changes in maturity indices of pomegranate fruit: a descriptive review Scientia Horticulturae, 159, 152-161. doi: 10.1016/j.scienta.2013.05.016
26) Gabor, T., Belzner, L., Kiermeier, M., Beck, M. T. and Neitz, A. (2016). A simulation-based architecture for smart cyber-physical systems In 2016 IEEE international conference on autonomic computing (ICAC) (pp. 374-379). IEEE. https://doi.org/10.1109/icac.2016.29.
27) Goodwin, T., Xu, J., Celik, N., & Chen, C. H. (2024). Real-time digital twin-based optimization with predictive simulation learning. Journal of Simulation, 18(1), 47-64. doi:10.1080/17477778.2022.2046520.
28) Gopinath, V., Srija, A., & Sravanthi, C. N. (2019, May). Re-design of smart homes with digital twins. In Journal of Physics: Conference Series (Vol. 1228, No. 1, p. 012031). IOP Publishing. doi:10.1088/1742-6596/1228/1/012031.
29) Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication. White paper, 1 (2014), 1-7.
30) Grieves, M. and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, 85-113. https://doi.org/10.1007/978-3-319-38756-7_4.
31) Hales, T. C. (2005). A proof of the Kepler conjecture. Annals of mathematics, 1065-1185.
32) Htet, H. K. K., Usman, I., & Anshori, M. Y. (2023). The Digital Twin Technology: A Scoping Review of Characterization and Implementation Through Business IT Perspectives. Business and Finance Journal, 8(1), 16-29. doi:10.33086/bfj.v8i1.3662.
33) Huang, H., Ji, T., & Xu, X. (2024). An adaptable Digital Twin model for manufacturing. Manufacturing Letters, 41, 1163-1169. https://doi.org/10.1016/j.mfglet.2024.09.142.
34) Huang, H. J., Wu, Z. and Zhi, L. H. Z. (2011). Architectural Design of Bionic Structure and Biomimetic Materials. Advanced Materials Research, 314, 1991-1994. https://doi.org/10.4028/www.scientific.net/amr.314-316.1991.
35) Huang, J. Y. and Siao, S. T. (2016). Development of an integrated bionic design system. Journal of Engineering, Design and Technology, 14(2), 310-327. doi: 10.1108/jedt-08-2014-0057.
36) Iliuţă, M. E., Moisescu, M. A., Pop, E., Ionita, A. D., Caramihai, S. I., & Mitulescu, T. C. (2024). Digital Twin—A Review of the Evolution from Concept to Technology and Its Analytical Perspectives on Applications in Various Fields. Appl Sci, 14(13), 5454. https://doi.org/10.3390/app14135454.
37) Jamei, E., & Vrcelj, Z. (2021). Biomimicry and the built environment, learning from nature’s solutions. Appl Sci, 11(16), 7514. https://doi.org/10.33086/bfj.v8i1.3662.
38) Jeffery, K. J., Wilson, J. J., Casali, G. and Hayman, R. M. (2015). Neural encoding of large-scale three-dimensional space—properties and constraints. Front. Psychol., 6, 927.
39) Jeong, D. Y., Baek, M. S., Lim, T. B., Kim, Y. W., Kim, S. H., Lee, Y. T., & Lee, I. B. (2022). Digital twin: Technology evolution stages and implementation layers with technology elements. Ieee Access, 10, 52609-52620. doi: 10.1109/access.2022.3174220.
40) Jovic, B., Cucakovic, A., Markovic, M., & Cvijic, K. (2021). Biomimetic approach to parametric flower modeling In ICGG 2020-Proceedings of the 19th International Conference on Geometry and Graphics (pp. 244-251). Springer International Publishing. . https://doi.org/10.1007/978-3-030-63403-2_22.
41) Khennak, I., & Drias, H. (2018, May). Data mining techniques and nature-inspired algorithms for query expansion. In Proceedings of the international conference on learning and optimization algorithms: theory and applications (pp. 1-6). https://doi.org/10.1145/3230905.3234631.
42) Körner, A., Mader, A., Saffarian, S., & Knippers, J. (2016, October). Bio-inspired kinetic curved-line folding for architectural applications. In Proceedings of the Acadia Posthuman Frontiers Conference, Ann Arbor, MI, USA (pp. 27-29). https://doi.org/10.52842/conf.acadia.2016.270.
43) Kovatchev, B. P., Renard, E. and Cobelli, C. (2010). Artificial pancreas: Past, present, and future Diabetes Technology & Therapeutics, 12(S1), S9–S14.
44) Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018). Digital Twin in manufacturing: A categorical classification and evaluation. IFAC‐PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
45) Lebedev, M. A., & Nicolelis, M. A. L. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29, 536–546.
46) Lee, J., Azamfar, M., & Bagheri, B. (2021). A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems Manufact. Lett., 27, 87-91. https://doi.org/10.1016/j.mfglet.2021.01.005
47) Lee, J., Bagheri, B., & Kao, H. A. (2021). A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems Manufacturing Letters, 19, 18–23.
48) Lehner, D., Sint, S., Eisenberg, M., & Wimmer, M. (2023). A pattern catalog for augmenting Digital Twin models with behavior. at-Automatisierungstechnik, 71(6), 423-443. https://doi.org/10.1515/auto-2022-0144.
49) Li, L., Gu, F., Li, H., Guo, J., & Gu, X. (2021). Digital twin bionics: A biological evolution-based digital twin approach for rapid product development. IEEE Access, 9, 121507-121521. doi: 10.1109/access.2021.3108218.
50) Ilieva, L., Ursano, I., Traista, L., Hoffmann, B., & Dahy, H. (2022). Biomimicry as a sustainable design methodology—Introducing the ‘Biomimicry for Sustainability’ framework. Biomimetics, 7(2), 37. doi:10.3390/biomimetics7020037.
51) Lin, Y., Li, L., Nugent, Ch., Gao, D., Chen, L., Ning, H., Wang, H., Ali, A., Wang, Y. and Ian. C. (2022). Human Digital Twin: A Survey. research square platform lac.
https://doi.org/10.48550/arXiv.2212.05937.
52) Liu, S., Zheng, P., & Shang, S. (2023). A novel bionic decision-making mechanism for digital twin-based manufacturing system. Manufacturing Letters, 35, 127-131. https://doi.org/10.1016/j.mfglet.2023.08.119
53) Liu, Zh., Liao, M., Norbert Meyendorf, Blasch, E., Yang, Ch., and Tsukada, K. (2023). Digital Twin for Predictive Maintenance In , 6. society of photo optical instrumentation engineers. https://doi.org/10.1117/12.2660270.
54) Liu, S., Lu, Y., Zheng, P., Shen, H., & Bao, J. (2022). Adaptive reconstruction of digital twins for machining systems: A transfer learning approach. Robotics and Computer-Integrated Manufacturing, 78, 102390. doi:10.1016/j.rcim.2022.102390.
55) Liu, S., Bao, J., Lu, Y., Li, J., Lu, S. and Sun, X. (2021). Digital twin modeling method based on biomimicry for machining aerospace components. Journal of manufacturing systems, 58, 180-195. https://doi.org/10.1016/j.jmsy.2020.04.014.
56) Luo, R., Sheng, B., Lu, Y., Huang, Y., Fu, G., & Yin, X. (2023). Digital twin model quality optimization and control methods based on workflow management. Appl Sci, 13(5), 2884. doi:10.3390/app13052884.
57) Mandelbrot, B. B. (1982). The fractal geometry of nature. W. H. Freeman.
58) Mayatskaya, I. A., Yazyeva, S. B., Lapina, A. P., & Davydova, V. V. (2020, August). Architectural bionics and the search for optimal solutions in the design of unique structures. In IOP Conference Series: Materials Science and Engineering (Vol. 913, No. 2, p. 022070). IOP Publishing. doi:10.1088/1757-899x/913/2/022070.
59) Mei, X., Liu, C., & Li, Z. (2024). Research progress on functional, structural and material design of plant-inspired green bionic buildings. Energy and Buildings, 114357. https://doi.org/10.1016/j.enbuild.2024.114357.
60) Menges, A. (2012). Biomimetic design processes in architecture: morphogenetic and evolutionary computational design. Bioinspiration & biomimetics, 7(1), 015003. https://doi.org/10.1088/1748-3182/7/1/015003.
61) Mitkov, R., Logg, A., Pantusheva, M., Naserentin, V. and Petrova-Antonova, D. (2024). The Role of Computational Fluid Dynamics within City Digital Twins: Opportunities and Challenges. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. doi:10.5194/isprs-annals-x-4-w4-2024-131-2024
62) Mizobuti, V., & Junior, L. C. V. (2020). Bioinspired architectural design based on structural topology optimization. Frontiers of Architectural Research, 9(2), 264-276. https://doi.org/10.1016/j.foar.2019.12.002.
63) Naudet, Y., Baudet, A., & Risse, M. (2021). Human Digital Twin in Industry 4.0: Concept and Preliminary Model In IN4PL (pp. 137-144). 10.5220/0010709000003062.
64) Nie, J., Xu, W. S., Cheng, D. Z., & Yu, Y. L. (2019). Digital twin-based smart building management and control framework. DEStech Trans. Comput. Sci. Eng. https://doi.org/10.12783/dtcse/icaic2019/29395.
65) Pang, T. Y., Pelaez Restrepo, J. D., Cheng, C. T., Yasin, A., Lim, H., & Miletic, M. (2021). Developing a digital twin and digital thread framework for an ‘Industry 4.0’Shipyard. Applied Sciences, 11(3), 1097. https://doi.org/10.3390/app11031097.
66) Pasquale F, Sarma, S. V., & Lybarger, J. (2014). Closed-loop neuroprosthetics: Bidirectional prostheses for restoring sensorimotor function. Frontiers in Neuroscience, 8, 289.
67) Pauwels, P., & Zhang, S. (2019). Parameterization and optimization in parametric design: A review Automation in Construction, 98, 225–236.
68) Pires, F., Cachada, A., Barbosa, J., Moreira, A. P., & Leitão, P. (2019, July). Digital twin in industry 4.0: Technologies, applications and challenges In 2019 IEEE 17th international conference on industrial informatics (INDIN) (Vol. 1, pp. 721-726). IEEE. https://doi.org/10.1109/indin41052.2019.8972134.
69) Pottmann, H. Asperl, A. Hofer, M. Kilian, A. (2007). Architectural Geometry. USA: Bentley Institute Press.
70) Raman, R., & Bashir, R. (2017). Biomimicry, biofabrication, and biohybrid systems: The emergence and evolution of biological design. Advanced healthcare materials, 6(20), 1700496. doi:10.1002/adhm.201700496.
71) Ramzy, N. (2015). Sustainable spaces with psychological values: Historical architecture as reference book for biomimetic models with biophilic qualities. International Journal of Architectural Research: ArchNet-IJAR, 9(2), 248-267. doi:10.26687/archnet-ijar.v9i2.464.
72) Rassölkin, A., Orosz, T., Demidova, G. L., Kuts, V., Rjabtšikov, V., Vaimann, T. and Kallaste, A. (2021). Implementation of digital twins for electrical energy conversion systems in selected case studies. Estonian Academy Publishers, 70, 19–39.
73) Santhaiah, C. and Reddy, R. M. (2014). Role of computers in bioinformatics by using different biological datasets. J. Comput. Eng., 16(2), 80-83. doi:10.9790/0661-16278083.
74) Santos, C. H. D., De Queiroz, J. A., Leal, F., & Montevechi, J. A. B. (2022). Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimize decision making on non-automated process. Journal of Simulation, 16(3), 284-297. https://doi.org/10.1080/17477778.2020.1811172
75) Smith, A., & Jones, B. (2019). Biomimetic design approaches in architecture: Exploring natural models. Journal of Architectural Science, 10(2), 45–60.
76) Song, J., & Sun, S. (2021). Research on architectural form optimization method based on environmental performance-driven design. In Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) (pp. 217-228). Springer Singapore. doi:10.1007/978-981-33-4400-6_21
77) Soori, M., Arezoo, B., & Dastres, R. (2023). Digital twin for smart manufacturing, A review. Sustainable Manufacturing and Service Economics, 100017. doi:10.1016/j.smse.2023.100017.
78) Stadtmann, F., Wassertheurer, H. A. G., & Rasheed, A. (2023, June). Demonstration of a standalone, descriptive, and predictive digital twin of a floating offshore wind turbine. In International Conference on Offshore Mechanics and Arctic Engineering (Vol. 86908, p. V008T09A039). American Society of Mechanical Engineers. https://doi.org/10.1115/omae2023-103112.
79) Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State‐of‐the‐Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. doi: 10.1109/TII.2018.2873186
80) Tekinerdogan, B., & Verdouw, C. (2020). Systems architecture design pattern catalog for developing digital twins. Sensors, 20(18), 5103. https://doi.org/10.3390/s20185103.
81) Umorina, Z. (2019). Application of bionic architecture methods as a future-oriented approach to modern architecture development in Russia In IOP Conference Series: Materials Science and Engineering (Vol. 687, No. 5, p. 055065). IOP Publishing. https://doi.org/10.1088/1757-899x/687/5/055065.
82) Ushizima, D. M., Bale, H. A., Bethel, E. W., Ercius, P., Helms, B. A., Krishnan, H., ... & Yang, C. (2016). IDEAL: I mages Across D domains, E experiments, A algorithms and L earning. Journal of Mathematics, 68, 2963-2972. doi:10.1007/s11837-016-2098-4.
83) Varshabi, N., Arslan Selçuk, S., & Mutlu Avinç, G. (2022). Biomimicry for energy-efficient building design: A bibliometric analysis. Biomimetics, 7(1), 21. doi:10.3390/biomimetics7010021.
84) Viola, J., & Chen, Y. (2020, October). Digital twin-enabled smart control engineering as an industrial AI: A new framework and case study In 2020 2nd International Conference on Industrial Artificial Intelligence (IAI) (pp. 1-6). IEEE. https://doi.org/10.1109/iai50351.2020.9262203.
85) Vorobyeva, O. I. (2018, November). Bionic architecture: back to the origins and a step forward. In IOP Conference Series: Materials Science and Engineering (Vol. 451, No. 1, p. 012145). IOP Publishing. https://doi.org/10.1088/1742-6596/451/1/012145.
86) Wang, Z., Han, K., & Tiwari, P. (2021, July). Digital twin simulation of connected and automated vehicles using the Unity game engine. In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) (pp. 1-4). IEEE. https://doi.org/10.1109/dtpi52967.2021.9540074.
87) Wilken, S., Guerra, R. E., Levine, D., & Chaikin, P. M. (2021). Random close packing as a dynamical phase transition. Phys. Rev. Lett., 127(3), 038002. doi: 10.1103/physrevlett.127.038002.
88) Wilson, B. S. and Dorman, M. F. (2008). Cochlear implants: A remarkable past and a brilliant future. Hearing Research, 242(1–2), 3–21.
89) Xuan, D. T., Huynh, T. V., Hung, N. T., & Thang, V. T. (2023). Applying Digital Twin and Multi-Adaptive Genetic Algorithms in Human–Robot Cooperative Assembly Optimization. Appl Sci, 13(7), 4229. https://doi.org/10.3390/app13074229.
90) Yuan, Y., Yu, X., Yang, X., Xiao, Y., Xiang, B., Wang, Y. (2017). Bionic building energy efficiency and bionic green architecture: A review. Renewable and sustainable energy reviews, 74, 771-787. https://doi.org/10.1016/j.rser.2017.03.004.
91) Zhang, Y., & Li, Z. (2023). Multi-criteria decision analysis for performance evaluation in digital twin systems. International Journal of Production Research, 61(5), 1475–1492. doi:10.1080/00207543.2023.2178901
92) Zhang, X., Kumar, V., & Chauhan, A. (2020). Data integration strategies in architectures: From the IoT to big data. Journal of Computing, 12(3), 112–130.
93) Zheng, F., & Wang, J. (2024). Multi-objective genetic algorithm for energy optimization in biomimetic structures. Renewable Energy, 200, 1234–1245. doi:10.1016/j.renene.2023.10.056
94) Zhou, L., An, C., Shi, J., Lv, Z., & Liang, H. (2021, August). Design and construction integration technology based on the digital twin. In 2021 Power System and Green Energy Conference (PSGEC) (pp. 7-11). IEEE. https://doi.org/10
