Skip to main content
SpringerLink
Log in

A BERT-based model for coupled biological strategies in biomimetic design

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The biomimetic design provides an adequate solution to attain an excellent design. However, the prototype space for biomimetic design is relatively large, and it becomes more and more challenging to find the required biological prototypes efficiently and accurately. To improve the design efficiency and enrich the biomimetic information, this paper proposes a coupled biological strategies-enabled bidirectional encoder representation from transformers (BERT) model to assist biomimetic design, namely BioDesign. We extract the biological strategies and extract dimensional information from AskNature as a part of the database. The linguistic expression model-BERT helps to search for biological strategy. Based on the coupled biological strategies analysis, the quantitative results of biomimetic strategies are given by BioDesign. Finally, we take the erosion wear-resistant design of the control valve core as an example to demonstrate the utilization based on the proposed BioDesign. The erosion wear experiment demonstrated the feasibility and effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability Statement

The datasets generated during and/or analyzed during the current study are not publicly available because the data form a part of an ongoing study.

Abbreviations

Innov :

Coupled biomimetic strategies by BioDesign model

\(K_{i}\) :

Keyword complexity

\({Len}[M({K}_{i}, {{keyword}_{i})]}\) :

Number of elements in the model response

M :

BioDesign model output corresponding to each keyword

Theme :

Key vocabulary of the designed subject

\(x_{rs, i}\) :

Response result of the BioDesign model

\(\theta _{i}\) :

Weight activity

\(\omega _{cp, i}\) :

Coupled elasticity weight of keyword

\(\omega _{i}\) :

Initial elasticity weight of keyword

\(C\omega _{i}\) :

Most important input among the keyword

References

  1. Cruz E, Hubert T, Chancoco G, Naim O, Chayaamor-Heil N, Cornette R, Menezo C, Badarnah L, Raskin K, Aujard F (2021) Design processes and multi-regulation of biomimetic building skins: a comparative analysis. Energy Build 246:111034

    Google Scholar 

  2. Palin D, Russell S, Kohle FFE, O’Dowd E, Flynn SYT (2020) Bioform—learning at the intersection of science and design. Dearq-Rev De Arquit-J Archit 26:52–59

    Google Scholar 

  3. Sullivan TN, Hung T-T, Velasco-Hogan A, Meyers MA (2019) Bioinspired avian feather designs. Mater Sci Eng C-Mater Biol Appl 105:110066

    Google Scholar 

  4. Zhang X-c, An C-c, Shen Z-f, Wu H-x, Yang W-g, Bai J-p (2020) Dynamic crushing responses of bio-inspired re-entrant auxetic honeycombs under in-plane impact loading. Mater Today Commun 23:100918

    Google Scholar 

  5. Bonfanti S, Guerra R, Zaiser M, Zapperi S (2021) Digital strategies for structured and architected materials design. Apl Mater 9(2):020904

    Google Scholar 

  6. Speck O, Speck D, Horn R, Gantner J, Sedlbauer KP (2017) Biomimetic bio-inspired biomorph sustainable? an attempt to classify and clarify biology-derived technical developments. Bioinspir Biomimet 12(1):011004

    Google Scholar 

  7. Kruiper R, Vincent JFV, Abraham E, Soar RC, Konstas I, Chen-Burger J, Desmulliez MPY (2018) Towards a design process for computer-aided biomimetics. Biomimetics 3(3):14

    Google Scholar 

  8. Graeff E, Maranzana N, Aoussat A (2019) Biomimetics, where are the biologists? J Eng Des 30(8–9):289–310

    Google Scholar 

  9. Chakrabarti A, Siddharth L, Dinakar M, Panda M, Palegar N, Keshwani S (2017) In: Chakrabarti A, Chakrabarti D (eds.) Idea Inspire 3.0-A tool for analogical design. Smart innovation systems and technologies, vol 66, pp 475–485

  10. Lenau T, Metze A, Hesselberg T (2018) Paradigms for biologically inspired design. In: Proceedings of SPIE, vol 10593

  11. Vandevenne D, Verhaegen P-A, Dewulf S, Duflou J (2015) Seabird: scalable search for systematic biologically inspired design. Artif Intell Eng Des Anal Manuf 30:78–95

    Google Scholar 

  12. Hashemi Farzaneh H, Helms MK, Lindemann U (2015) Visual representations as a bridge for engineers and biologists in bio-inspired design collaborations. In: International conference on engineering design, ICED15

  13. Willocx M, Ayali A, Duflou JR (2020) Where and how to find bio-inspiration? A comparison of search approaches for bio-inspired design. CIRP J Manuf Sci Technol 31:61–67

    Google Scholar 

  14. Wang X, Yang X, Du J, Wang X, Li J, Tang X (2021) A deep learning approach for identifying biomedical breakthrough discoveries using context analysis. Scientometrics 126(7):5531–5549

    Google Scholar 

  15. Seluk SA, Avin GM (2021) Natural language approach for bio-informed architectural education: a biomimetic shell design. Int J Technol Des Educ 3:1–21

    Google Scholar 

  16. Chen C, Li Y, Tao Y, Chen J, Liu Q, Li S (2021) A method to automatically push keywords for biological information searching in bio-inspired design. Proc Inst Mech Eng C J Mech Eng Sci 235(1):30–47

    Google Scholar 

  17. Chiu I, Shu LH (2007) Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Artif Intell Eng Des Anal Manuf 21(1):45–59

    Google Scholar 

  18. Cheong H, Hallihan GM, Shu LH (2014) Design problem solving with biological analogies: a verbal protocol study. Artif Intell Eng Des Anal Manuf 28(1):27–47

    Google Scholar 

  19. Ofer D, Brandes N, Linial M (2021) The language of proteins: Nlp, machine learning & protein sequences. Comput Struct Biotechnol J 19:1750–1758

    Google Scholar 

  20. Li X, Lin B (2021) The development and design of artificial intelligence in cultural and creative products. Math Probl Eng 2021:1–10

    Google Scholar 

  21. Sha W, Guo Y, Yuan Q, Tang S, Zhang X, Lu S, Guo X, Cao Y-C, Cheng S (2020) Artificial intelligence to power the future of materials science and engineering. Adv Intell Syst 2(4):1900143

    Google Scholar 

  22. Verganti R, Vendraminelli L, Iansiti M (2020) Innovation and design in the age of artificial intelligence. J Prod Innov Manag 37(3):212–227

    Google Scholar 

  23. Zhong FS, Xing J, Li XT, Liu XH, Fu ZY, Xiong ZP, Lu D, Wu XL, Zhao JH, Tan XQ, Li F, Luo XM, Li ZJ, Chen KX, Zheng MY, Jiang HL (2018) Artificial intelligence in drug design. Sci China-Life Sci 61(10):1191–1204

    Google Scholar 

  24. Gu GX, Chen CT, Richmond DJ, Buehler MJ (2018) Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horiz 5(5):939–945

    Google Scholar 

  25. Krichmar JL, Severa W, Khan MS, Olds JL (2019) Making bread: biomimetic strategies for artificial intelligence now and in the future. Front Neurosci 13:666

    Google Scholar 

  26. Buche A, Chandak MB (2020) Bert for opinion mining and sentiment farming. Biosci Biotechnol Res Commun 13(14):35–39

    Google Scholar 

  27. Nam S, Yoon S, Raghavan N, Park H (2021) Identifying service opportunities based on outcome-driven innovation framework and deep learning: a case study of hotel service. Sustainability 13(1):391

    Google Scholar 

  28. Chen T, Wu M, Li H (2019) A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning. Database: J Biol Databases Curat. 2019. https://doi.org/10.1093/database/baz116

  29. Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234–1240

    Google Scholar 

  30. Wang J, Zhang X, Chen L (2021) How well do pre-trained contextual language representations recommend labels for github issues? Knowl-Based Syst 232:107476

    Google Scholar 

  31. Graeff E, Maranzana N, Aoussat A (2019) Engineers’ and biologists’ roles during biomimetic design processes, towards a methodological symbiosis. In: Proceedings of the design society: International conference on engineering design, vol 1, pp 319–328

  32. Pentelovitch N, Nagel JK (2022) Understanding the use of bio-inspired design tools by industry professionals. Biomimetics 7(2):63

    Google Scholar 

  33. Weidner BV, Nagel J, Weber HJ (2018) Facilitation method for the translation of biological systems to technical design solutions. Int J Des Creat Innov 6:211–234

    Google Scholar 

  34. Nagel JKS (2014). In: Goel AK, McAdams DA, Stone RB (eds) A thesaurus for bioinspired engineering design. Springer, London, pp 63–94

  35. Nagel JKS, Stone RB (2012) A computational approach to biologically inspired design. Artif Intell Eng Des Anal Manuf 26(2):161–176

    Google Scholar 

  36. Shu LH (2010) A natural-language approach to biomimetic design. Artif Intell Eng Des Anal Manuf 24(4):507–519

    Google Scholar 

  37. Chiarello F, Belingheri P, Fantoni G (2021) Data science for engineering design: state of the art and future directions. Comput Ind 129(2):103447

    Google Scholar 

  38. Stroble JK, McAdams DA, Stone RB, et al (2009) Conceptualization of biomimetic sensors through functional representation of natural sensing solutions. In: DS 58-2: proceedings of ICED 09, the 17th international conference on engineering design, Vol 2, design theory and research methodology, Palo Alto, CA, USA, 24.–27.08., pp 53–64

  39. Jiang S, Hu J, Luo J (2021) Data-driven design-by-analogy: state of the art. International design engineering technical conferences and computers and information in engineering conference, vol 2: 41st computers and information in engineering conference (CIE)

  40. Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to wordnet: an on-line lexical database. Int J Lexicogr 3(4):235–244

    Google Scholar 

  41. Linsey JS, Markman AB, Wood KL (2012) Design by analogy: a study of the wordtree method for problem re-representation. J Mech Des 134(4):041009

    Google Scholar 

  42. Speer R, Chin J, Havasi C (2016) Conceptnet 5.5: an open multilingual graph of general knowledge. In: Proceedings of 31St AAAI conference on artificial intelligence

  43. Han J, Shi F, Chen L, Childs PRN (2018) A computational tool for creative idea generation based on analogical reasoning and ontology. Artif Intell Eng Des Anal Manuf 32(4):462–477

    Google Scholar 

  44. Sarica S, Luo J, Wood KL (2020) Technet: technology semantic network based on patent data. Expert Syst Appl 142:112995

    Google Scholar 

  45. Sarica S, Luo J (2021) Design knowledge representation with technology semantic network. Proc Des Soc 1:1043–1052

    Google Scholar 

  46. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805

  47. Brown TB, Mann B, Ryder N, Subbiah M, Amodei D (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877–1901

    Google Scholar 

  48. Li R, Mo T, Yang J, Li D, Jiang S, Wang D (2021) Bridge inspection named entity recognition via bert and lexicon augmented machine reading comprehension neural model. Adv Eng Inform 50:101416

    Google Scholar 

  49. Qiao B, Zou ZY, Huang Y, Fang K, Zhu XH, Chen YM (2022) A joint model for entity and relation extraction based on bert. Neural Comput Appl 34(5):3471–3481

    Google Scholar 

  50. Li MG, Li WR, Wang F, Jia XJ, Rui GW (2021) Applying bert to analyze investor sentiment in stock market. Neural Comput Appl 33(10):4663–4676

    Google Scholar 

  51. Jiang XU, Wang X, Wang Y, Guo F (2017) Complexity computation approach of design cognition using deterministic information theory. China Mech Eng 28(05):596–602

    Google Scholar 

  52. Bhasin D, McAdams D (2019) Current state of the art: problem-driven multi-functional bio-inspired designs. In: International design engineering technical conferences and computers and information in engineering conference, vol 7: 31st international conference on design theory and methodology

  53. Fu K, Moreno D, Yang M, Wood KL (2014) Bio-inspired design: an overview investigating open questions from the broader field of design-by-analogy. J Mech Des 136(11):111102

    Google Scholar 

  54. Sartori J, Pal U, Chakrabarti A (2010) A methodology for supporting transfer in biomimetic design. Artif Intell Eng Des Anal Manuf 24:483–506

    Google Scholar 

  55. Pham CTA, Magistretti S, Dell’Era C (2021) The role of design thinking in big data innovations. Innov-Org Manag 24:290–314

    Google Scholar 

  56. Thuethongchai N, Taiphapoon T, Chandrachai A, Triukose S (2020) Adopt big-data analytics to explore and exploit the new value for service innovation. Soc Sci-Basel 9(3):29

    Google Scholar 

  57. Speck O, Speck D, Horn R, Gantner J, Sedlbauer KP (2017) Biomimetic bio-inspired biomorph sustainable? an attempt to classify and clarify biology-derived technical developments. Bioinspir Biomim 12(1):011004

    Google Scholar 

  58. Saha T, Jayashree SR, Saha S, Bhattacharyya P (2020) Bert-caps: a transformer-based capsule network for tweet act classification. IEEE Trans Comput Soc Syst 7(5):1168–1179

    Google Scholar 

  59. Pota M, Ventura M, Catelli R, Esposito M (2021) An effective bert-based pipeline for twitter sentiment analysis: a case study in Italian. Sensors 21(1):133

    Google Scholar 

  60. Abudeif AM, Abdel Moneim AA, Farrag AF (2015) Multicriteria decision analysis based on analytic hierarchy process in gis environment for siting nuclear power plant in egypt. Ann Nucl Energy 75:682–692

    Google Scholar 

  61. Sun F, Xu H (2020) A review of biomimetic research for erosion wear resistance. Bio-Des Manuf 3(4):331–347

    Google Scholar 

  62. Kadkhodapour J, Anaraki AP, Taherkhani B (2015) Mechanism of foreign object damage and investigating effect of particle parameters on erosion rate of a rough surface using experimental and numerical methods. J Fail Anal Prev 15(2):272–281

    Google Scholar 

  63. Sherif HA, Almufadi FA (2018) Analysis of elastic and plastic impact models. Wear 412–413:127–135

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of China under Grant [51875113], Natural Science Joint Guidance Foundation of the Heilongjiang Province of China under Grant [LH2019E027]. PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities under Grant [XK2070021009].

Author information

Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150000, Heilongjiang, China

    Feng Sun, He Xu & Yihan Meng

  2. College of Computer Science, Dalian University of Technology, Dalian, 116000, Liaoning, China

    Zhimao Lu

Authors
  1. Feng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. He Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yihan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhimao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to He Xu.

Ethics declarations

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, F., Xu, H., Meng, Y. et al. A BERT-based model for coupled biological strategies in biomimetic design. Neural Comput & Applic 35, 2827–2843 (2023). https://doi.org/10.1007/s00521-022-07734-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07734-z

Keywords

Search

Navigation