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Mohammadi, Sahar, Gorji Mahlabani, Yousef, Karimi, Fariborz, Mohammadhosseini, Babak. (1401). The Effect of Architectural Design Parameters on IEQ in Accomplishing School Smartification. سامانه مدیریت نشریات علمی, 3(5), 223-248. doi: 10.22054/dcm.2022.67853.1089
Sahar Mohammadi; Yousef Gorji Mahlabani; Fariborz Karimi; Babak Mohammadhosseini. "The Effect of Architectural Design Parameters on IEQ in Accomplishing School Smartification". سامانه مدیریت نشریات علمی, 3, 5, 1401, 223-248. doi: 10.22054/dcm.2022.67853.1089
Mohammadi, Sahar, Gorji Mahlabani, Yousef, Karimi, Fariborz, Mohammadhosseini, Babak. (1401). 'The Effect of Architectural Design Parameters on IEQ in Accomplishing School Smartification', سامانه مدیریت نشریات علمی, 3(5), pp. 223-248. doi: 10.22054/dcm.2022.67853.1089
Mohammadi, Sahar, Gorji Mahlabani, Yousef, Karimi, Fariborz, Mohammadhosseini, Babak. The Effect of Architectural Design Parameters on IEQ in Accomplishing School Smartification. سامانه مدیریت نشریات علمی, 1401; 3(5): 223-248. doi: 10.22054/dcm.2022.67853.1089

The Effect of Architectural Design Parameters on IEQ in Accomplishing School Smartification

International Journal of Digital Content Management
مقاله 10، دوره 3، شماره 5، شهریور 2022، صفحه 223-248    اصل مقاله (1.36 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22054/dcm.2022.67853.1089
نویسندگان
Sahar Mohammadi1؛ Yousef Gorji Mahlabani* 1؛ Fariborz Karimi* 1؛ Babak Mohammadhosseini2
1Faculty of Architecture and Urbanism, Imam Khomeini International University, Qazvin, Iran
2Department of Physics, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
چکیده
Purpose: The study aims to study the effect of architectural design parameters on IEQ in accomplishing school smartification.
Method: The research was conducted in school buildings located in Tabriz, Iran. The indicators chosen to represent IEQ are the adaptive PMV model used for thermal comfort, imageless daylight glare probability used for visual comfort, and CO2 concentration used for IAQ assessment. The simulation technique was used to collect data for a generative parametric school model. The method of data analysis includes a multivariate linear regression algorithm, t-test statistic, and one-way analysis of variance. The studied variables are dimensions of classrooms with the fixed area, Percentage of window area on a wall, window height, Shading, and protrusions in plan design. The stepwise method for multivariate linear regression in SPSS was used to assess the vital IEQ indicator in terms of thermal and visual comfort and CO2 concentration.
Findings: The study found that among studied indicators, the south facade window ratio significantly correlates with IEQ. The other CRI parameters are the north window ratio and north window height. the findings revealed that to increase the IEQ in schools, facade design is more critical than the plan. The higher the window surface on the south, north, west, and east faces, the greater the thermal comfort and glare probability is.
Conclusion: However, increasing the height of the windows can reduce glare and also increase thermal comfort. Thermal comfort improves as the length of the southern classrooms rises. On the bright side, it has no noticeable glare effect.
کلیدواژه‌ها
IEQ؛ thermal comfort؛ visual comfort؛ IAQ؛ optimization
مراجع
Alghamdi, S., Tang, W., Kanjanabootra, S., & Alterman, D. (2022). Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings. Buildings, 12(3), 329.

Asadi, I., Mahyuddin, N., & Shafigh, P. (2017). A review on indoor environmental quality (IEQ) and energy consumption in building based on occupant behavior. Facilities.

Bahaj, A. S., James, P. A., & Jentsch, M. F. (2008). Potential of emerging glazing technologies for highly glazed buildings in hot arid climates. Energy and buildings, 40(5), 720-731.

Buratti, C., Moretti, E., Belloni, E., & Cotana, F. (2013). Unsteady simulation of energy performance and thermal comfort in non-residential buildings. Building and environment, 59, 482-491.

Catalina, T., & Iordache, V. (2012). IEQ assessment on schools in the design stage. Building and environment, 49, 129-140.

Cetin, M. (2016). A Change in the Amount of CO2 at the Center of the Examination Halls: Case Study of Turkey. Studies on Ethno-Medicine, 10(2), 146-155.

Chaloeytoy, K., Ichinose, M., & Chien, S.-C. (2020). Determination of the Simplified Daylight Glare Probability (DGPs) Criteria for Daylit Office Spaces in Thailand. Buildings, 10(10), 180.

Choudhury, A. R., Majumdar, P., & Datta, C. (2011). Factors affecting comfort: human physiology and the role of clothing. In Improving comfort in clothing (pp. 3-60): Elsevier.

Comite'Europe'en de Normalisation, C. (2007). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. EN 15251.

Diaz, M., Piderit, M. B., & Attia, S. (2021). Parameters and indicators used in Indoor Environmental Quality (IEQ) studies: a review. Paper presented at the Journal of Physics: Conference Series.

energy plus weather data. (2021). 

Fisk, W. J. (2000). Review of health and productivity gains from better IEQ.

Fisk, W. J. (2002). How IEQ affects health, productivity. ASHRAE journal, 44(LBNL-51381).

Fisk, W. J., Black, D., & Brunner, G. (2011). Benefits and costs of improved IEQ in US offices. Indoor Air, 21(5), 357-367.

Handbook-Fundamentals, A., & Edition, S. (2009). Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers. Inc.(see page 14.14 for summary description of RP-1171 work on uncertainty in design temperatures).

Hollberg, A., & Ruth, J. (2016). LCA in architectural design—a parametric approach. The International Journal of Life Cycle Assessment, 21(7), 943-960.

Holzer, D., Hough, R., & Burry, M. (2007). Parametric design and structural optimisation for early design exploration. International Journal of Architectural Computing, 5(4), 625-643.

Jones, N. L. (2019). The Imageless Method for Spatial and Annual Glare Analysis.

Kabele, K., Veverková, Z., & Urban, M. (2019). Methodology for assessing the indoor environmental quality in low energy buildings in the Czechia. Paper presented at the IOP Conference Series: Materials Science and Engineering.

Kapalo, P., Vilcekova, S., & Voznyak, O. (2014). Using experimental measurements of the concentrations of carbon dioxide for determining the intensity of ventilation in the rooms. Chemical Engineering Transactions, 39, 1789-1794.

Karapetsis, A., & Alexandri, E. (2016). Indoor Environmental Quality and its Impacts on Health–Case Study: School Buildings. Paper presented at the Proceedings of the EinB2016—5th International Conference “ENERGY in BUILDINGS.

Lang, X., Wargocki, P., & Liu, W. (2022). Investigating the relation between electroencephalogram, thermal comfort, and cognitive performance in neutral to hot indoor environment. Indoor Air, 32(1), e12941.

Latha, P., Darshana, Y., & Venugopal, V. (2015). Role of building material in thermal comfort in tropical climates–A review. Journal of Building Engineering, 3, 104-113.

Lee, E. S., & Tavil, A. (2007). Energy and visual comfort performance of electrochromic windows with overhangs. Building and environment, 42(6), 2439-2449.

Lyons, P. R., Arasteh, D., & Huizenga, C. (2000). Window performance for human thermal comfort. Transactions-American Society of Heating Refrigerating and Air Conditioning Engineers, 106(1), 594-604.

Mazzeo, D., & Kontoleon, K. J. (2020). The role of inclination and orientation of different building roof typologies on indoor and outdoor environment thermal comfort in Italy and Greece. Sustainable Cities and Society, 60, 102111.

Middel, A., Selover, N., Hagen, B., & Chhetri, N. (2016). Impact of shade on outdoor thermal comfort—a seasonal field study in Tempe, Arizona. International journal of biometeorology, 60(12), 1849-1861.

Mogas, J., Palau, R., Fuentes, M., & Cebrián, G. (2021). Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution. Learning Environments Research, 1-19.

Pereira, L. D., Lamas, F. B., & da Silva, M. G. (2019). Improving energy use in schools: from IEQ towards energy-efficient planning—method and in-field application to two case studies. Energy Efficiency, 12(5), 1253-1277.

Rahbar, M., Mahdavinejad, M., Markazi, A. H., & Bemanian, M. (2022). Architectural layout design through deep learning and agent-based modeling: A hybrid approach. Journal of Building Engineering, 47, 103822.

Razmi, A., Rahbar, M., & Bemanian, M. (2022). PCA-ANN integrated NSGA-III framework for dormitory building design optimization: Energy efficiency, daylight, and thermal comfort. Applied Energy, 305, 117828.

Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies. International Journal of Educational Technology in Higher Education, 17(1), 1-21.

Saini, M. K., & Goel, N. (2019). How smart are smart classrooms? A review of smart classroom technologies. ACM Computing Surveys (CSUR), 52(6), 1-28.

Singh, M., Garg, S., & Jha, R. (2008). Different glazing systems and their impact on human thermal comfort—Indian scenario. Building and environment, 43(10), 1596-1602.

Suk, J. Y., Schiler, M., & Kensek, K. (2013). Development of new daylight glare analysis methodology using absolute glare factor and relative glare factor. Energy and buildings, 64, 113-122.

Tzempelikos, A., & Chan, Y.-C. (2016). Estimating detailed optical properties of window shades from basic available data and modeling implications on daylighting and visual comfort. Energy and buildings, 126, 396-407.

Wargocki, P., Wyon, D. P., Sundell, J., Clausen, G., & Fanger, P. O. (2000). The effects of outdoor air supply rate in an office on perceived air quality, sick building syndrome (SBS) symptoms and productivity. Indoor Air, 10(4), 222-236.

Wei, L., Tian, W., Zuo, J., Yang, Z.-Y., Liu, Y., & Yang, S. (2016). Effects of building form on energy use for buildings in cold climate regions. Procedia Engineering, 146, 182-189.

Wei, W., Wargocki, P., Zirngibl, J., Bendžalová, J., & Mandin, C. (2020). Review of parameters used to assess the quality of the indoor environment in Green Building certification schemes for offices and hotels. Energy and buildings, 209, 109683.

Wienold, J. (2007). Dynamic simulation of blind control strategies for visual comfort and energy balance analysis. Paper presented at the Building Simulation.

Wienold, J., & Christoffersen, J. (2005). Towards a new daylight glare rating. Lux Europa, Berlin, 157-161.

Wienold, J., & Christoffersen, J. (2006). Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras. Energy and buildings, 38(7), 743-757.

Zhang, X., Weerasuriya, A. U., Wang, J., Li, C. Y., Chen, Z., Tse, K. T., & Hang, J. (2022). Cross-ventilation of a generic building with various configurations of external and internal openings. Building and environment, 207, 108447.

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