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Leveraging hybrid machine learning for sustainable development through recycled waste in landscape design

  • Yuanyuan Yao (Art and Design College of Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Suhui Li (School of Design, Shanghai Jiaotong University) ;
  • Pengfei Shi (Shangqiu Institute of Technology, Education and Art School) ;
  • Yuansheng Huang (Art and Design College of Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Xiujie Jiang (Art and Design College of Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Yadong Zheng (Art and Design College of Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Zhaochen Wang (School of Economics and Management, Shangqiu Normal University)
  • Received : 2023.12.03
  • Accepted : 2024.03.21
  • Published : 2024.08.25
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Abstract

Traditional methods for landscape design using recycled waste materials, which rely primarily on manual selection and placement for integration and optimization, frequently encounter sustainability issues, which reduce the overall efficiency. To address these constraints, this study proposes a hybrid machine-learning technique that leverages AI-enhanced algorithms to improve the usage of recycled materials in landscape design. Based on total adaptive learning models, this strategy incorporates adaptive correction of material variations, followed by improvement utilizing AI-informed selection algorithms. AI technologies, particularly AI sensing techniques, are used to improve adaptive material integration, allowing for more incredible sustainable growth of landscape projects. The AI-enhanced technique allows for more precise utilization and analysis of recycled materials at the design level, considerably improving the sustainability of landscape projects. The experimental results show a significant increase in the effectiveness of landscape projects processed with these AI-enhanced techniques. The findings support the suggested method's efficacy, demonstrating its robust enhancing capabilities, efficiency, and practical applicability in sustainable landscape design. This advancement contributes significantly to improving the quality of landscape projects, particularly in sustainable development applications where precision and resource optimization are critical.

Keywords

References

  1. Ahmed, H.T. and Aly, A.M. (2023), "Recycled Waste Materials in Landscape Design for Sustainable Development (Al-Ahsa as a Model)", Sustainability, 15(15), p.11705. https://doi.org/10.3390/su151511705
  2. Amura, A., Aldini, A., Pagnotta, S., Salerno, E., Tonazzini, A. and Triolo, P. (2021), "Analysis of diagnostic images of artworks and feature extraction: design of a methodology", J. Imaging, 7(3), p.53. https://doi.org/10.3390/jimaging7030053
  3. Ayyasamy, R.K., Shaikh, F.B., Lah, N.S.B.C., Kalhoro, S., Chinnasamy, P. and Krisnan, S. (2023), "Industry 4.0 Digital Technologies and Information Systems: Implications for Manufacturing Firms Innovation Performance", In: 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-6. https://doi.org/10.1109/ICCCI56745.2023.10128638
  4. Chatterjee, A. and Khovalyg, D. (2023), "Dynamic indoor thermal environment using reinforcement learning-based controls: Opportunities and challenges", Build. Environ., 244, 110766. https://doi.org/10.1016/j.buildenv.2023.110766
  5. Chen, X. (2023), "Environmental landscape design and planning system based on computer vision and deep learning", J. Intell. Syst., 32(1), 20220092. https://doi.org/10.1515/jisys-2022-0092
  6. Dale, V.H., Kline, K.L., Buford, M.A., Volk, T.A., Smith, C.T. and Stupak, I. (2016), "Incorporating bioenergy into sustainable landscape designs", Renew. Sustain. Energy Rev., 56, 1158-1171. https://doi.org/10.1016/j.rser.2015.12.038
  7. Ervin, S.M. (2018), "Turing landscapes: computational and algorithmic design approaches and futures in landscape architecture", In: Codify, pp. 89-114.
  8. Fábrega-Álvarez, P. and Parcero-Oubiña, C. (2019), "Now you see me. An assessment of the visual recognition and control of individuals in archaeological landscapes", J. Archaeol. Sci., 104, 56-74. https://doi.org/10.1016/j.jas.2019.02.002
  9. Fernberg, P. and Chamberlain, B. (2023), "Artificial intelligence in landscape architecture: a literature review", Landscape J., 42(1), 13-35. https://doi.org/10.3368/lj.42.1.13
  10. Guo, S., Tang, J., Liu, H. and Gu, X. (2021), "Study on landscape architecture model design based on big data intelligence", Big Data Res., 25, p.100219. https://doi.org/10.1016/j.bdr.2021.100219
  11. Hasan, M.M. (2023), An Intelligent Decision-making Scheme in a Dynamic Multi-objective Environment using Deep Reinforcement Learning Anglia Ruskin Research Online (ARRO).
  12. Heinzl, C. and Stappen, S. (2017), "Star: Visual computing in materials science", In: Computer Graphics Forum, 36(3), 647- 666. https://doi.org/10.1111/cgf.13214
  13. Holstov, A., Farmer, G. and Bridgens, B. (2017), "Sustainable materialisation of responsive architecture", Sustainability, 9(3), 435. https://doi.org/10.3390/su9030435
  14. Hoong, J.D.L.H., Lux, J., Mahieux, P.Y., Turcry, P. and AitMokhtar, A. (2020), "Determination of the composition of recycled aggregates using a deep learning-based image analysis", Automat. Constr., 116, 103204. https://doi.org/10.1016/j.autcon.2020.103204
  15. Hu, F., Zhang, S., Gao, J., Tang, Z., Chen, X., Qiu, L., Hu, H., Jiang, L., Wei, S., Guo, B. and Zhou, H. (2024), "Digitalization empowerment for green economic growth: the impact of green complexity", Environ. Eng. Manage. J., 23(3), 519-536. https://doi.org/10.30638/eemj.2024.040
  16. Hussein, M.Y.A., AL-Karablieh, M., Al-Kfouf, S. and Taani, M. (2024), 'Machine learning-driven sustainable urban design: transforming Singapore's landscape with vertical greenery', Asian J. Civil Eng.,25, 3851-3863. https://doi.org/10.1007/s42107-024-01016-w
  17. Jahani, A. and Rayegani, B. (2020), "Forest landscape visual quality evaluation using artificial intelligence techniques as a decision support system", Stochast. Environ. Res. Risk Assess., 34(10), 1473-1486. https://doi.org/10.1007/s00477-020-01832-x
  18. Jahani, A., Saffariha, M. and Barzegar, P. (2023), "Landscape aesthetic quality assessment of forest lands: an application of machine learning approach", Soft Comput., 27(10), 6671-6686. https://doi.org/10.1007/s00500-022-07642-3
  19. Kerschke, P. and Trautmann, H. (2019), "Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning", Evolution. Computat., 27(1), 99-127. https://doi.org/10.1162/evco_a_00236
  20. Kranjčić, N., Medak, D., Župan, R. and Rezo, M. (2019), "Machine learning methods for classification of the green infrastructure in city areas", ISPRS Int. J. Geo-Inform., 8(10), p.463. https://doi.org/10.3390/ijgi8100463
  21. Liu, X., Liu, X. and Zhang, Z. (2024), "Application of red mud in carbon capture, utilization and storage (CCUS) technology", Renew. Sustain. Energy Rev., 202, 114683. https://doi.org/10.1016/j.rser.2024.114683
  22. Luca, F. (2018), "Emergent, Adaptive and Responsive Urban Landscape Design Strategies", Acta Architecturae Naturalis, 4, 20-32.
  23. Ma, K., Yu, Y., Yang, B. and Yang, J. (2019), "Demand-side energy management considering price oscillations for residential building heating and ventilation systems", IEEE Transact. Indust. Inform., 15(8), 4742-4752. https://doi.org/10.1109/TII.2019.2901306
  24. Ma, Q., Zhang, Y., Hu, F. and Zhou, H. (2024), "Can the energy conservation and emission reduction demonstration city policy enhance urban domestic waste control? Evidence from 283 cities in China", Cities, 154, p.105323. https://doi.org/10.1016/j.cities.2024.105323
  25. Malan, K.M. (2021), "A survey of advances in landscape analysis for optimization", Algorithms, 14(2), p.40. https://doi.org/10.3390/a14020040
  26. Rondinella, F., Oreto, C., Abbondati, F. and Baldo, N. (2023), "Laboratory investigation and machine learning modeling of road pavement asphalt mixtures prepared with construction and demolition waste and rap", Sustainability, 15(23), p.16337. https://doi.org/10.3390/su152316337
  27. Shan, P. and Sun, W. (2021), "Research on landscape design system based on 3D virtual reality and image processing technology", Ecolog. Inform., 63, p.101287. https://doi.org/10.1016/j.ecoinf.2021.101287
  28. Shi, B. (2024), "3D dynamic landscape simulation of artificial intelligence in environmental landscape design", Heliyon, 10(15). https://doi.org/10.1016/j.heliyon.2024.e35268
  29. Skilling, G. (2020), "Modular Urbanism: Combining modular and multi-scalar design strategies in creating sustainable landscape architecture design and construction processes", Doctoral Dissertation; Tesis de maestría, Universidad de Calgary, Canada. http://dx.doi.org/10.11575/PRISM/38270
  30. Stupariu, M.-S., Cushman, S.A., Pleşoianu, A.-I., Pătru-Stupariu, I. and Fuerst, C. (2022), "Machine learning in landscape ecological analysis: a review of recent approaches", Landsc. Ecol., 37(5), 1227-1250. https://doi.org/10.1007/s10980-021-01366-9
  31. Tran, V.Q., Dang, V.Q. and Ho, L.S. (2022), "Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach", Constr. Build. Mater., 323, p.126578. https://doi.org/10.1016/j.conbuildmat.2022.126578
  32. Wattanapanich, C., Imjai, T., Sridhar, R., Garcia, R. and Thomas, B.S. (2024), "Optimizing Recycled Aggregate Concrete for Severe Conditions Through Machine Learning Techniques: A Review", Engineered Sci., 31, p.1191. https://doi.org/10.30919/es1191
  33. Wu, S., Cao, J. and Shao, Q. (2024a), "How do you select a remanufacturing mode: end-of-life or used product?", Environ. Develop. Sustain., p. 1-21. https://doi.org/10.1007/s10668-024-04515-7
  34. Wu, P., Liu, X., Zhang, Z., Wei, C., Wang, J. and Gu, J. (2024b), "The harmless and value-added utilization of red mud: Recovering iron from red mud by pyrometallurgy and preparing cementitious materials with its tailings", J. Indust. Eng. Chem., 132, 50-65. https://doi.org/10.1016/j.jiec.2023.11.038
  35. Yutian, L., Sun, X., Songxue, L. and Jiayu, W. (2022), "An approach to urban landscape character assessment: Linking urban big data and machine learning", Sustain. Cities Society, 83, p.103983. https://doi.org/10.1016/j.scs.2022.103983
  36. Zhang, C. (2022), "Feature extraction of color symbol elements in interior design based on extension data mining", Mathe. Probl. Eng., 2022(1), p.5151336. https://doi.org/10.1155/2022/5151336
  37. Zhang, R.,Yin, L., Jia, J., Yin, Y. and Li, C. (2019), 'Application of ATS-GWIFBM Operator Based on Improved Time Entropy in Green Building Projects', Adv. CivilEng.,2019,p.3519195. https://doi.org/10.1155/2019/3519195