Artificial Intelligence and Environmental Protection of Buildings

Zheng Chen


(China)
https://orcid.org/0000-0001-8134-8129

Yu He

yu.he1@yahoo.com
Guilin University of Electronic Technology, School of Art and Design, Guilin, China (China)
https://orcid.org/0000-0001-9732-9766

Abstract

Global environmental pollution has an extremely negative impact on the population of the planet and threatens the future of mankind. One of the main sources of waste and toxic emissions into the atmosphere is the construction sector. It is necessary to find ways to minimize the damage caused to nature. Currently, artificial intelligence technologies are among the most promising ways to improve the environment. Automatic control systems solve a number of problems related to reducing costs and resources, full use of renewable energy sources, improving the safety of energy systems, and many others. The purpose of this article is to determine the functionality of artificial intelligence technologies and ways of their application in green construction. To solve this problem, methods of analysis and synthesis of existing information models were applied. The article discloses automatic control systems in the design, construction, and operation of buildings. These include well-known methods, such as Building Information Model, Machine Learning, Deep Learning, and narrow-profile ones: Response Surface Methodology, Multi-Agent System, Digital Twins, etc. In addition, the study states that when planning and arranging green buildings must adhere to the following principles: high energy efficiency, rational use of natural resources, adaptation to the environment and climate, ensuring comfort and safety for residents. The article presents the standards of green construction existing in the world. This work can serve as a guide when choosing information models and is of practical value in the development of green buildings.


Keywords:

ecology, energy efficiency, green buildings, information model, automatic control system

AKOMEA-FRIMPONG I., KUKAH A. S., JIN X., OSEI-KYEI R., PARIAFSAI F., 2022, Green finance for green buildings: A systematic review and conceptual foundation, Journal of Cleaner Production 356: 131869.
DOI: https://doi.org/10.1016/j.jclepro.2022.131869   Google Scholar

BADUGE S.K., THILAKARATHNA S., PERERA J.S., ARASHPOUR M., SHARAFI P., TEODOSIO B., SHRINGI A., MENDIS P., 2022, Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications, Automation in Construction 141: 104440.
DOI: https://doi.org/10.1016/j.autcon.2022.104440   Google Scholar

BUILTIN, 2022, What is deep learning and how does it work? https://builtin.com/machine-learning/what-is-deep-learning.
  Google Scholar

CHINESESTANDARD.NET, 2014, Assessment standard for green building, https://www.chinesestandard.net/PDF.aspx/GBT50378-2014.
  Google Scholar

DING Z., LI Z., FAN C., 2018, Building energy savings: Analysis of research trends based on text min-ing, Automation in Construction 96: 398-410.
DOI: https://doi.org/10.1016/j.autcon.2018.10.008   Google Scholar

DOUNIS A.I., 2010, Artificial intelligence for energy conservation in buildings, Advances in Building Energy Research 4(1): 267-299.
DOI: https://doi.org/10.3763/aber.2009.0408   Google Scholar

DOWLING R., MCGUIRK P., MAALSEN S., SADOWSKI J., 2021, How smart cities are made: A priori, ad hoc and post hoc drivers of smart city implementation in Sydney, Australia, Urban Studies, 58(16): 3299-3315.
DOI: https://doi.org/10.1177/0042098020986292   Google Scholar

GOHARI S., BAER D., NIELSEN B. F., GILCHER E., SITUMORANG W.Z., 2020, Prevailing ap-proaches and practices of citizen participation in smart city projects: Lessons from Trondheim, Nor-way, Infrastructures 5(4): 36.
DOI: https://doi.org/10.3390/infrastructures5040036   Google Scholar

IEA, 2019, Global Status Report for Buildings and Construction, https://www.iea.org/reports/global-status-report-for-buildings-and-construction-2019.
  Google Scholar

JINKANG R., 2019, Environmental impact, significance and development direction of green buildings, Green Building Materials 3: 30-31.
  Google Scholar

JONBAN M.S., ROMERAL L., AKBARIMAJD A., ALI Z., GHAZIMIRSAEID S.S., MARZBAND M., PUTRUS G., 2021, Autonomous energy management system with self-healing capabilities for green buildings (microgrids), Journal of Building Engineering 34: 101604.
DOI: https://doi.org/10.1016/j.jobe.2020.101604   Google Scholar

KARCHES T., 2022, Fine-tuning the aeration control for energy-efficient operation in a small sewage treatment plant by applying biokinetic modeling. Energies 15(17): 6113, https://doi.org/10.3390/en15176113.
DOI: https://doi.org/10.3390/en15176113   Google Scholar

KAYA M.M., TAŞKIRAN Y., KANOĞLU A., DEMİRTAŞ A., ZOR E., BURÇAK I., NACAK M.C., AKGÜL F.T., 2021, Designing a smart home management system with artificial intelligence & machine learning, technical report, DOI: 10.13140/RG.2.2.33082.72641/1.
  Google Scholar

LI Q., LONG R., CHEN H., CHEN F., WANG J., 2020, Visualized analysis of global green buildings: Development, barriers and future directions, Journal of Cleaner Production 245: 118775.
DOI: https://doi.org/10.1016/j.jclepro.2019.118775   Google Scholar

LU H., SHENG X., DU F., 2022, Economic benefit evaluation system of green building energy saving building technology based on entropy weight method, Processes 10(2): 382.
DOI: https://doi.org/10.3390/pr10020382   Google Scholar

PERSHAKOV V., BIELIATYNSKYI A., POPOVYCH I., LYSNYTSKA K., KRASHENINNIKOV V., 2016, Progressive collapse of high-rise buildings from fire, MATEC Web of Conferences 73: 01001, https://doi.org/10.1051/matecconf/20167301001.
DOI: https://doi.org/10.1051/matecconf/20167301001   Google Scholar

SERRANO W., 2022, iBuilding: Artificial intelligence in intelligent buildings, Computing and Applications 34(2): 875-897.
DOI: https://doi.org/10.1007/s00521-021-05967-y   Google Scholar

SHAHSAVAR M.M., AKRAMI M., GHEIBI M., KAVIANPOUR B., FATHOLLAHI-FARD A.M., BEHZADIAN K., 2021, Constructing a smart framework for supplying the biogas energy in green build-ings using an integration of response surface methodology, artificial intelligence and petri net modelling, Energy Conversion and Management 248: 114794.
DOI: https://doi.org/10.1016/j.enconman.2021.114794   Google Scholar

SHEN Y., FAURE M., 2021, Green building in China, International Environmental Agreements: Poli-tics, Law and Economics 21(2): 183-199.
DOI: https://doi.org/10.1007/s10784-020-09495-3   Google Scholar

THORPE D., ENSHASSI A., MOHAMED S., ABUSHABAN S., COURS S., 2010, The impacts of con-struction and the built environment, Willmott Dixon, London.
  Google Scholar

UN, 2015, UN Sustainability Goals, https://www.home.sandvik/en/about-us/sustainable-business/global-commitments/UN-global-goals-index/?gclid=EAIaIQobChMIibLSzZvO_AIVc0eRBR1WqAeMEAAYASAAEgIDjPD_BwE.
  Google Scholar

UN, 2020, The Paris Agreement, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.
  Google Scholar

W, Z., JIANG M., CAI Y., WANG H., LI S., 2019, What hinders the development of green building? An investigation of China, International Journal of Environmental Research and Public Health 16(17): 3140.
DOI: https://doi.org/10.3390/ijerph16173140   Google Scholar

WAN Y., ZHAI Y., WANG X., CUI C., 2022, Evaluation of indoor energy-saving optimization design of green buildings based on the intelligent GANN-BIM model, Mathematical Problems in Engineering 1: 10.
DOI: https://doi.org/10.1155/2022/3130512   Google Scholar

WANG C., 2021, Evaluation algorithm of ecological energy-saving effect of green buildings based on Gray correlation degree, Journal of Mathematics 1: 10.
DOI: https://doi.org/10.1155/2021/6705220   Google Scholar

WANG W., TIAN Z., XI W., TAN Y. R., DENG Y., 2021, The influencing factors of China’s green build-ing development: An analysis using RBF-WINGS method, Building and Environment 188: 107425.
DOI: https://doi.org/10.1016/j.buildenv.2020.107425   Google Scholar

WEI Y., 2021, The development of green building technology, IOP Conference Series: Earth and Envi-ronmental Science 812(1): 012011.
DOI: https://doi.org/10.1088/1755-1315/812/1/012011   Google Scholar

WORLDGBC.ORG, 2019, About Green Building, https://www.worldgbc.org/what-green-building.
  Google Scholar

WUNI I. Y., SHEN G. Q., OSEI-KYEI R., 2019, Scientometric review of global research trends on green buildings in construction journals from 1992 to 2018, Energy and Buildings 190: 69-85.
DOI: https://doi.org/10.1016/j.enbuild.2019.02.010   Google Scholar

XUE F., ZHAO J., 2021, Application calibration based on energy consumption model in optimal design of green buildings, Advances in Materials Science and Engineering 1: 9.
DOI: https://doi.org/10.1155/2021/5360443   Google Scholar

YANG B., LV Z., WANG F., 2022, Digital twins for intelligent green buildings, Buildings 12(6): 856.
DOI: https://doi.org/10.3390/buildings12060856   Google Scholar

ZAKHAROV A.N., KALASHNIKOV D.B., 2020, Environmental problems of China’s industrial devel-opment, Russian Foreign Economic Bulletin 1: 40-50.
  Google Scholar

ZHANG Y., WANG H., GAO W., WANG F., ZHOU N., KAMMEN D., YING X., 2019, A survey of the status and challenges of green building development in various countries, Sustainability 11(19): 5385.
DOI: https://doi.org/10.3390/su11195385   Google Scholar

ZHANG Y., WANG J., HU F., WANG Y., 2017, Comparison of evaluation standards for green building in China, Britain, United States, Renewable and Sustainable Energy Reviews 68: 262-271.
DOI: https://doi.org/10.1016/j.rser.2016.09.139   Google Scholar

ZHAO X.G., GAO C.P., 2022, Research on energy-saving design method of green building based on BIM technology, Scientific Programming 1: 10.
DOI: https://doi.org/10.1155/2022/2108781   Google Scholar

Download


Published
2023-07-07

Cited by

Chen, Z., & He, Y. (2023). Artificial Intelligence and Environmental Protection of Buildings . Problemy Ekorozwoju, 18(2), 254–262. https://doi.org/10.35784/preko.4039

Authors

Zheng Chen 

China
https://orcid.org/0000-0001-8134-8129

Authors

Yu He 
yu.he1@yahoo.com
Guilin University of Electronic Technology, School of Art and Design, Guilin, China China
https://orcid.org/0000-0001-9732-9766

Statistics

Abstract views: 538
PDF downloads: 427