Intelligent Design of Ecological Furniture in Risk Areas based on Artificial Simulation
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Abstract
The study is based on the characterization of different AI models applied in the public furniture design analyzing the conditions of risk, materiality, and integration of variables in two AI generative modeling algorithms. As risky since they contain flood-prone areas, low vegetation coverage, and underdevelopment of infrastructure; therefore, these characterizations are tested through artificial simulation. The experimental method is applied through laboratory tests of various material components and their structuring in 3D simulators to check their resistance and risk scenarios. The case study of one of the most risky and populated areas of the informal settlement area of the Northwest of Guayaquil, such as the Coop, is analyzed. Sergio Toral is the focal point for on-site testing. It is concluded that the generation of a planned scheme of ecological furniture with different materials responds more effectively to the territory and that through artificial simulation an advantage can be obtained in terms of execution time and results, thus demonstrating that artificial intelligence is an ideal tool. To generate furniture design proposals that are more diverse, innovative, and functional with the environment, but it generates a minimum level of error for specific designs in the experimental model_01 of 0.1% to 3% and a high level in the experimental model_02 with an increasing error from 20% to 70%. As a future line of research, it is proposed to generate a simulated system of all the new informal settlements in Guayaquil and establish focal points for the implementation of new ecological furniture.
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Revich BA. The significance of green spaces for protecting health of urban population. Health Risk Anal. 2023;2023(2):168-85. Available from: https://doi.org/10.21668/health.risk/2023.2.17.eng
Shan J, Huang Z, Chen S, Li Y, Ji W. Green space planning and landscape sustainable design in smart cities considering public green space demands of different formats. Complexity. 2021;2021:5086636. Available from: https://doi.org/10.1155/2021/5086636
Gromek P, Sobolewski G. Risk-based approach for informing sustainable infrastructure resilience enhancement and potential resilience implication in terms of emergency service perspective. Sustainability (Switzerland). 2020;12(11):4530. Available from: https://doi.org/10.3390/su12114530
Kumar A, Pandey AC, Chapter 15 - Geoinformation for urban geoenvironmental hazard-risk and vulnerability assessment. In: Earth Observation in Urban Monitoring: Techniques and Challenges. Elsevier; 2023;309-38. Available from: https://doi.org/10.1016/B978-0-323-99164-3.00010-0
Alghamdi S, Tang W, Kanjanabootra S, Alterman D. Optimising building energy and comfort predictions with intelligent computational model. Sustainability (Switzerland). 2024;16(8):3432. Available from: https://doi.org/10.3390/su16083432
Á Ayuso L, del Blanco García FL. Application of neural networks to the design of collective housing. Rita Rev Index Text Acad. 2021;16:214-31. Available from: http://dx.doi.org/10.24192/2386-7027(2021)(v16)(20)
Lin P, Wu M, Zhang L. Probabilistic safety risk assessment in large-diameter tunnel construction using an interactive and explainable tree-based pipeline optimization method. Appl Soft Comput. 2023;143:110376. Available from: https://doi.org/10.1016/j.asoc.2023.110376
Sisa I, Abad A, Espinosa I, Martinez-Cornejo I, Burbano-Santos P. A decade of Ecuador´s efforts to raise its health research output: a bibliometric analysis. Glob Health Action. 2021;14(1):1855694. Available from: https://doi.org/10.1080/16549716.2020.1855694
Placeres IB. Public investment and poverty: the Ecuadorian case up to 2020. Universidad y Sociedad. 2022 Feb;14(S1):656-65.
Cajide BV. The cyborg as contemporary architectural design strategy. Rita Rev Index Text Acad. 2023;19:38-51.
Grau JFR, Maldonado MP. Architectural drawing. Language of thought and construction. Past, present and future. VLC Arquitectura. 2023;10(2):225-55. Available from: https://www.research.ed.ac.uk/en/publications/architectural-drawing-language-of-thought-and-construction-past-p
Qin S, Zhang X, Lu S, He Q, Huang W, Lin B. Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence. J Build Eng. 2024;95:109996. https://doi.org/10.1016/j.jobe.2024.109996
Gao W, Lu S, Zhang X, He Q, Huang W, Lin B. Impact of 3D modeling behavior patterns on the creativity of sustainable building design through process mining. Autom Constr. 2023;150:104804. Available from: https://doi.org/10.1016/j.autcon.2023.104804
Khan AM, Tariq MA, U. Rehman SK, Saeed T, Alqahtani FK, Sherif M. BIM integration with XAI using LIME and MOO for automated green building energy performance analysis. Energies. 2024;17(13):3295. Available from: https://doi.org/10.3390/en17133295
Golafshani E, Chiniforush AA, Zandifaez P, Ngo T. An artificial intelligence framework for predicting operational energy consumption in office buildings. Energy Build. 2024;317:114409. Available from: https://doi.org/10.1016/j.enbuild.2024.114409.
Hosseini Khorasani SA, Borhani E, Yousefieh M, Janghorbani A. Towards tailored thermoelectric materials: An artificial intelligence-powered approach to material design. Physica B Condens Matter. 2024;685:415946. Available from: https://doi.org/10.1016/j.physb.2024.415946
Le Q, Xing G, Wei L. Study on precursor anomaly data recognition and prediction of L-IAZPSO-SVM algorithm. In: ACM International Conference Proceeding Series. 2019;2019. Available from: https://doi.org/10.1145/3358331.3358371.
Lee CS, Wang MH, Chen CY, Reformat M, Nojima Y, Kubota N. Knowledge graph-based genetic fuzzy agent for human intelligence and machine co-learning. In: IEEE International Conference on Fuzzy Systems; Incheon. Available from: http://dx.doi.org/10.1109/FUZZ52849.2023.10309699
Schmidpeter R, Funk C. Artificial intelligence: Companion to a new human ‘measure’? In: CSR, Sustainability, Ethics and Governance. Springer Nature. 2023; 9-15. Available from: https://ideas.repec.org/h/spr/csrchp/978-3-031-09245-9_2.html
Wu J, Wang M, Zhao Y, Zhang L, Chen H. Intelligent design method of mine tunnel portal driven by knowledge graph. Comput Geotech. 2024;173:106431. Available from: https://doi.org/10.1016/j.compgeo.2024.106431
Alawi OA, Kamar HM, Yaseen ZM. Optimizing building energy performance predictions: A comparative study of artificial intelligence models. J Build Eng. 2024;88:109247. Available from: https://doi.org/10.1016/j.jobe.2024.109247
Trifunovic S, Dimitrov D, Vukovic D, Kolaric M, Banjac R, Jovanovic T, et al. Electronic cigarette liquids impair metabolic cooperation and alter proteomic profiles in V79 cells. Respir Res. 2022;23(1):191. https://doi.org/10.1186/s12931-022-02102-w
Xu L, Wang L, Zhu M. Application of BIM technology in structural design of prefabricated building based on big data simulation modeling analysis. Scalable Comput. 2024;25(4):2862-75. https://doi.org/10.12694/scpe.v25i4.2854