General Mathematical Principles for Determining the Engineering Concept of Apartment Buildings Based on Expert Analytical Methods and Decision Support Systems
- Autores: Merkulov A.A.1, Stepanyan I.V.2
-
Afiliações:
- RUDN University
- Institute of Machines Science named after A.A. Blagonravov of the Russian Academy of Sciences
- Edição: Volume 26, Nº 2 (2025)
- Páginas: 144-154
- Seção: Articles
- URL: https://journals.rcsi.science/2312-8143/article/view/327612
- DOI: https://doi.org/10.22363/2312-8143-2025-26-2-144-154
- EDN: https://elibrary.ru/LRKTFT
- ID: 327612
Citar
Texto integral
Resumo
A well-designed engineering blueprint for a residential apartment building can effectively mitigate potential hazards during the preparatory phase of construction. This approach enables the consideration of factors that, due to the constraints inherent in specialized expertise, frequently go unaddressed in practice. The theory of expert systems and mathematical apparatus based on fuzzy logic are put forward as the methodological basis and fundamental research methods. The objective of the present study is to formulate mathematical principles that facilitate the determination of the engineering concept of apartment buildings at the preparatory stage of construction, based on the theory of fuzzy sets and decision support methods. The research objective is to develop general mathematical principles for solving applied problems using specialized expert systems. The research yielded the development of the mathematical foundations of a multifunctional expert system for the conceptualization of apartment buildings during the preparatory phase of construction; a fuzzy knowledge base was created. The projection of a multidimensional response surface function has been restored, reflecting the dependence of linguistic variables. Mathematical principles for determining the engineering concept of multi-family residential buildings at the preparatory stage of construction have been developed.
Palavras-chave
Sobre autores
Alexander Merkulov
RUDN University
Autor responsável pela correspondência
Email: amerkulov@levelgroup.ru
ORCID ID: 0009-0006-0211-808X
Postgraduate student of the Department of Mechanics and Control Processes, Academy of Engineering
6 Miklukho-Maklaya St, Moscow, 117198, Russian FederationIvan Stepanyan
Institute of Machines Science named after A.A. Blagonravov of the Russian Academy of Sciences
Email: neurocomp.pro@gmail.com
ORCID ID: 0000-0003-3176-5279
Código SPIN: 5644-6735
Doctor of Biological Sciences, Candidate of Technical Sciences, Leading Researcher
4 M. Kharitonyevskiy Pereulok, 101990, Moscow, Russian FederationBibliografia
- Caiado RGG, Scavarda LF, Gavião LO, Ivson P, Nascimento DL De M, Garza-Reyes JA. A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. International Journal of Production Economics. 2021;231:107883. http://doi.org/10.1016/j.ijpe.2020.107883 EDN: XZZKZY
- Harirchian E, Lahmer T. Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. Structures. 2020;28:1384-1399. http://doi.org/10.1016/j.istruc.2020.09.048 EDN: GJIYKQ
- Lanbaran NM, Celik E, Yiğider M. Evaluation of investment opportunities with interval-valued fuzzy topsis method. Applied Mathematics and Nonlinear Sciences. 2020;5(1):461-474. http://doi.org/10.2478/amns.2020.1.00044 EDN: QDEPPY
- Kendal SL, Creen M. An introduction to knowledge engineering. Springer London, 2007. https://doi.org/10.1007/978-1-84628-667-4
- Wang Y, Zhao Z, Guo J, Zou L, Ma L. A survey on control for Takagi-Sugeno fuzzy systems subject to engineering-oriented complexities. Systems Science & Control Engineering. 2021;9(1):334-349. http://doi.org/10.1080/ 21642583.2021.1907259 EDN: BYNODY
- Lucchese LV, de Oliveira GG, Pedrollo OC. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Natural Hazards. 2021;106(3):2381-2405. http://doi.org/10.1007/s11069-021-04547-6 EDN: OITGPK
- Azar AT. (ed.) Fuzzy systems. BoD - Books on Demand, 2010. ISBN: 9537619923
- Jana DK, Pramanik S, Sahoo P, Mukherjee A. Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries. Soft Computing. 2019;23:557-567. http://doi.org/10.1007/s00500-017-2860-8 EDN: CRZKUJ
- Kumar S, Anbanandam R. An integrated Delphi-fuzzy logic approach for measuring supply chain resilience: an illustrative case from manufacturing industry. Measuring Business Excellence. 2019;23(3):350-375. http://doi.org/10.1108/MBE-01-2019-0001
- Al-Ani BRK, Erkan TE. A study of load demand forecasting models in electricity using artificial neural networks and fuzzy logic model. International Journal of Engineering. 2022;35(6):1111-1118. http://doi.org/10.5829/ije.2022.35.06c.02 EDN: WOZLQP
- Hendiani S, Bagherpour M. Developing an integrated index to assess social sustainability in construction industry using fuzzy logic. Journal of cleaner production. 2019;230:647-662. http://doi.org/10.1016/j.jclepro.2019.05.055
- Hedaoo N, Pawar A. Risk Assessment Model Based on Fuzzy Logic for Residential Buildings. Slovak Journal of Civil Engineering. 2021;29(4):37-48. http://doi.org/10.2478/sjce-2021-0026 EDN: DQLKOL
- Panchalingam R, Chan KC. A state-of-the-art review on artificial intelligence for Smart Buildings. Intelligent Buildings International. 2019;13(4):203-226. http://doi.org/10.1080/17508975.2019.1613219 EDN: GVDBAU
- Vilela M, Oluyemi G, Petrovski A. A fuzzy inference system applied to value of information assessment for oil and gas industry. Decision Making: Applications in Management and Engineering. 2019;2(2):1-18. http://doi.org/10.31181/dmame1902001v EDN: FBRMFE
- Fayek AR. Fuzzy logic and fuzzy hybrid techniques for construction engineering and management. Journal of Construction Engineering and Management. 2020;146(7):04020064. http://doi.org/10.1061/(asce)co.1943-7862.0001854 EDN: AAFXLG
- Jain A, Sharma A. Membership function formulation methods for fuzzy logic systems: A comprehensive review. Journal of Critical Reviews. 2020;7(19):8717-8733.
- Pezeshki Z, Mazinani SM. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artificial Intelligence Review. 2019;52(1):495-525. http://doi.org/10.1007/s10462-018-9630-6 EDN: BLPALL
- Wang K, Ying Z, Goswami SS, Yin Y, Zhao Y. Investigating the Role of Artificial Intelligence Techno-logies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environ-ment. Sustainability. 2023;15(15):11848. http://doi.org/10.3390/su151511848 EDN: FSLOWM
- Ren X, Li C, Ma X, Chen F, Wang H, Sharma A, Gaba GS, Masud M. Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability. 2021;13(6):3405. http://doi.org/10.3390/su13063405 EDN: JDNYAS
Arquivos suplementares
