Preview

Vestnik MGSU

Advanced search

Justification of the combination of standard values of material characteristics of layers in the building envelope on the basis of quadratic optimization

https://doi.org/10.22227/1997-0935.2025.2.193-214

Abstract

Introduction. The relevance of the research is determined by the features of the design and organizational-and-technological solutions formed in the process of developing modern construction projects, consisting in the use of a limited composition of technological resources (building materials, machines and equipment), which determines the discreteness of the values of the characteristics of the above-mentioned solutions. The purpose of the study is to develop tools to justify the combination of standard values of the characteristics of materials used for the device of layers of the enclosing structure, using quadratic optimization tools.

Materials and methods. Mathematical models for optimization of the thickness for the materials used as layers of the enclosing structure in a residential building are developed, based on discrete and binary unknown variables, as well as on the criteria of the weighted average (by the thickness of layers) temperature, total thickness and thermal resistance of the structure. The mathematical models have a quadratic structure of the objective function and a linear structure of indirect constraints, but the presence of constraints related to the discreteness (binarity) of unknown variables significantly complicates the process of the models’ implementation due to the lack of suitable standard (available in modern software environments for mathematical modelling) computational algorithms. In this regard, it was decided to develop a user computational algorithm that includes the advantages of the branch and bound method used to determine the optimal values of unknown variables for which discreteness or binary requirements are specified, as well as the interior point method used to determine the optimal solution of the quadratic optimization model without taking into account the above requirements.

Results. To practically verify the developed mathematical models, the proposed computational algorithm was applied to justify the selection of standard material characteristics for the given enclosure structure. The resulting data enabled the establishment of dependencies between the thermal performance indicators of the structure and the required thickness of its layers.

Conclusions. Based on the analysis of the results obtained from using the developed mathematical models and computational algorithm, the significant practical value of the proposed tools was confirmed.

About the Authors

Ya. A. Olekhnovich
Peter the Great St.Petersburg Polytechnic University (SPbPU)
Russian Federation

Yanis A. Olekhnovich — senior lecturer, Graduate School of Industrial, Civil and Road Construction, Institute of Civil Engineering

29 Polytechnic st., St. Petersburg, 194356

RSCI AuthorID: 820961, Scopus: 57212393243, ResearcherID: AAE-4749-2020



A. E. Radaev
Peter the Great St.Petersburg Polytechnic University (SPbPU)
Russian Federation

Anton E. Radaev — Candidate of Technical Sciences, Candidate of Economic Sciences, Associate Professor, Associate Professor of the Graduate School of Industrial of Civil and Road Construction, Institute of Civil Engineering

29 Polytechnic st., St. Petersburg, 194356

RSCI AuthorID: 650856, Scopus: 57196054199, ResearcherID: R-6085-2016



References

1. Yu W., Li B., Jia H., Zhang M., Wang D. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings. 2015; 88:135-143. DOI: 10.1016/j.enbuild.2014.11.063

2. Benaddi F.Z., Boukhattem P., Tabares-Velasco P.C. Multi-objective optimization of building envelope components based on economic, environmental, and thermal comfort criteria. Energy and Buildings. 2024; 305:113909. DOI: 10.1016/j.enbuild.2024.113909

3. Liu Y., Li T., Xu W., Wang Q., Huang H., He B.J. Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms. Energy and Buildings. 2023; 300:113665. DOI: 10.1016/j.enbuild.2023.113665

4. Delgarm N., Sajadi B., Delgarm S. Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC). Energy and Buildings. 2016; 131:42-53. DOI: 10.1016/j.enbuild.2016.09.003

5. He L., Zhang L. A bi-objective optimization of energy consumption and investment cost for public building envelope design based on the ε-constraint method. Energy and Buildings. 2022; 266:112133. DOI: 10.1016/j.enbuild.2022.112133

6. Karmellos M., Kiprakis A., Mavrotas G. A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies. Applied Energy. 2015; 139:131-150. DOI: 10.1016/j.apenergy.2014.11.023

7. Yang H., Xu Z., Shi Y., Tang W., Liu Ch., Yunusa-Kaltungo A. et al. Multi-objective optimization designs of phase change material-enhanced building using the integration of the Stacking model and NSGA-III algorithm. Journal of Energy Storage. 2023; 68:107807. DOI: 10.1016/j.est.2023.107807

8. Asadi E., Gameiro da Silva M., Antunes C.H., Dias L., Glicksman L. Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings. 2014; 81:444-456. DOI: 10.1016/j.enbuild.2014.06.009

9. Hosamo H.H., Tingstveit M.S., Nielsen H.K., Svennevig P.R., Svidt K. Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II. Energy and Buildings. 2022; 277:112479. DOI: 10.1016/j.enbuild.2022.112479

10. Rosso F., Ciancio V., Dell’Olmo J., Salata F. Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy and Buildings. 2020; 216:109945. DOI: 10.1016/j.enbuild.2020.109945

11. Wu C., Pan H., Luo Zh., Liu Ch., Huang H. Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II. Building and Environment. 2024; 254:111386. DOI: 10.1016/j.buildenv.2024.111386

12. Ouanes S., Sriti L. Regression-based sensitivity analysis and multi-objective optimisation of energy performance and thermal comfort: Building envelope design in hot arid urban context. Building and Environment. 2023; 248:111099. DOI: 10.1016/j.buildenv.2023.111099

13. Wong B., Wu Zh., Gan V., Chan C., Cheng J. Parametric building information modelling and optimality criteria methods for automated multi-objective optimisation of structural and energy efficiency. Journal of Building Engineering. 2023; 75:107068. DOI: 10.1016/j.jobe.2023.107068

14. Radaev A.E., Gamayunova O.S., Bardina G.A. Optimization of energy efficiency design characteristics for construction projects. AlfaBuild. 2021; 5(20):2003. DOI: 10.57728/ALF.20.3. EDN RZOJPY.

15. Yang J., Wu H., Xu X., Huang G., Cen J., Liang Y. Regional climate effects on the optimal thermal resistance and capacitance of residential building walls. Energy and Buildings. 2021; 244:111030. DOI: 10.1016/j.enbuild.2021.111030

16. Sánchez-Zabala V.F., Gomez-Acebo T. Building energy performance metamodels for district energy management optimisation platforms. Energy Conversion and Management: X. 2024; 21:100512. DOI: 10.1016/j.ecmx.2023.100512

17. Shi Y., Chen P. Energy retrofitting of hospital buildings considering climate change: An approach integrating automated machine learning with NSGA-III for multi-objective optimization. Energy and Buildings. 2024; 319:114571. DOI: 10.1016/j.enbuild.2024.114571

18. Huang J., Lv H., Gao T., Feng W., Chen Y., Zhou T. Thermal properties optimization of envelope in energy-saving renovation of existing public buildings. Energy and Buildings. 2014; 75:504-510. DOI: 10.1016/j.enbuild.2014.02.040

19. Gamayunova O.S. Procedure for determination of the thermal characteristics for wall structures of residential buildings : dis. ... cand. tech. sciences. St. Petersburg, 2021; 166. EDN CJHWXK. (rus.).

20. Ivanova V.R., Zhidko E.A. A comparison of insulation options for reconstruction of residential house method of analysis of hierarchies. Information Technologies in Construction, Social and Economic Systems. 2019; (3-4):(17-18):183-188. EDN THYVAJ. (rus.).

21. Ivanova I.B., Romanov M.A. Selection of the design solution based on the system of indicators using the method of pair comparisons. Social and Economic Management: Theory and Practice. 2019; 1(36):80-82. EDN UQDZZK. (rus.).

22. Radaev A.E., Gamayunova O.S., Bardina G.A. Use of optimization modeling tools to justify the characteristics of energy efficient structural solution. Construction and Industrial Safety. 2022; 27(79):5-25. EDN EXVSFS. (rus.).

23. Radaev A.E., Gamayunova O.S. Determination of the characteristics for a multilayer wall’s structure with application of quadratic programming tools. Construction and Industrial Safety. 2021; 22(74):111-127. DOI: 10.37279/2413-1873-2021-22-111-127. EDN ORVFEG. (rus.).

24. Rezanov E.M., Petrov P.V. Increasing the efficiency of warming of building walls taking into account the regulation of the released heat energy. Journal of Transsib Railway Studies. 2019; 4(40):77-86. EDN DWXNDX. (rus.).

25. Petrov P.V., Kulagin V.A., Rezanov E.M., Sta-rikov A.P. Improvement of technology of thermal insulation of buildings. Journal of Siberian Federal University. Engineering & Technologies. 2023; 16(2):187-197. EDN IXBHRM. (rus.).

26. Deep K., Singh K.P., Kansal M.L., Mohan C. A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation. 2009; 212(2):505-518. DOI: 10.1016/j.amc.2009.02.044


Review

For citations:


Olekhnovich Ya.A., Radaev A.E. Justification of the combination of standard values of material characteristics of layers in the building envelope on the basis of quadratic optimization. Vestnik MGSU. 2025;20(2):193-214. (In Russ.) https://doi.org/10.22227/1997-0935.2025.2.193-214

Views: 129


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1997-0935 (Print)
ISSN 2304-6600 (Online)