Building Information Modelling (BIM)-Integrated Thermal Comfort Assessment System

Abstract

Thermal comfort affects occupant satisfaction, health, productivity, and energy consumption in buildings. In this study, a thermal comfort assessment system for office buildings is developed that enriches occupant feedback with necessary contextual information (e.g., building elements and geometry) and presents it to decision-makers integrated with the Building Information Model (BIM). The methodology includes developing a thermal comfort measurement model using Structural Equation Modelling, creating a prototype of the proposed BIM-integrated thermal comfort assessment system, and validating it through a case study in an office building. The results demonstrate that the developed system effectively collects and analyses occupant feedback. The proposed system is expected to optimise energy consumption and improve thermal comfort in offices, and thereby enhance occupant satisfaction, health, and productivity.

Keywords

• Building performance
• occupant feedback
• thermal comfort
• facility management
• building information modelling (BIM)
• construction management

Supporting Institution

Scientific and Technological Research Council of Turkey (TUBITAK) (Grant No. 116M177)

Project Number

TÜBİTAK 116M177

Ethical Statement

The study was approved by the Ethical Committee of Istanbul Technical University.

Thanks

This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) with project number 116M177.

Yapı Bilgi Modellemesi ile Tümleşik Termal Konfor Değerlendirme Sistemi

Abstract

Termal konfor yapılarda kullanıcı memnuniyetini, sağlığını, üretkenliğini ve enerji tüketimini etkilemektedir. Bu çalışmada, ofis binaları için kullanıcı geri bildirimlerinin gerekli bağlamsal bilgi öğeleriyle (örneğin, bina elemanları ve bina geometrisi) zenginleştirilerek toplanmasını sağlayan ve karar vericilere yapı bilgi modellemesi (BIM) ile entegre şekilde sunan bir termal konfor değerlendirme sistemi geliştirilmiştir. Çalışmanın yöntemi, Yapısal Eşitlik Modeli ile termal konfor ölçüm modeli geliştirilmesi ve önerilen BIM ile tümleşik termal konfor değerlendirme sisteminin prototipi oluşturularak bir ofis binasında vaka analiziyle doğrulamasını içerir. Sonuçlar, geliştirilen sistemin kullanıcı geri bildirimlerinin etkin şekilde toplanmasını ve analiz edilmesini sağladığını göstermektedir. Önerilen sistemin ofislerde enerji tüketimini optimize ederek termal konforu iyileştirmesi ve böylece kullanıcı memnuniyeti, sağlığı, üretkenliğine katkıda bulunması beklenmektedir.

Keywords

• Bina performansı
• kullanıcı geri bildirimi
• termal konfor
• tesis yönetimi
• yapı bilgi modellemesi (BIM)
• yapı işletmesi

Supporting Institution

TÜBİTAK (Proje No: 116M177)

Project Number

TÜBİTAK 116M177

Ethical Statement

Çalışma, İstanbul Teknik Üniversitesi Etik Kurulu tarafından onaylanmıştır.

Thanks

Bu çalışma 116M177 proje numarası ile Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir.

References

1. Tan, C. Y., Rahman, R. A., WELL building: Key design features for office environments, J Archit Eng, 29(2), 04023011, 2023.
2. Forooraghi, M., Miedema, E., Ryd, N., Wallbaum, H., Scoping review of health in office design approaches, Journal of Corporate Real Estate, 22(2), 155-180, 2020.
3. Wu, J., Hou, Z., Shen, J., Lian, Z., A method for the determination of optimal indoor environmental parameters range considering work performance, J. Build. Eng., 35, 101976, 2021.
4. Kawakubo, S., Sugiuchi, M., Arata, S., Office thermal environment that maximizes workers’ thermal comfort and productivity, Built Environ, 233, 110092, 2023.
5. Frontczak, M., Wargocki, P., Literature survey on how different factors influence human comfort in indoor environments, Build Environ, 46(4), 922-937, 2011.
6. Song, Y., Mao, F., Liu, Q., Human comfort in indoor environment: a review on assessment criteria, data collection and data analysis methods, IEEE Access, 7, 119774-119786, 2019.
7. Caro, R., Marrero, M. D. R., Martínez, A., Cuerda, E., del Mar Barbero-Barrera, M., Neila, J., ..., Ramos-Palacios, C. R., Data-driven research into the inaccuracy of traditional models of thermal comfort in offices, Build Environ, 248, 111104, 2024.
8. Mamulova, E., Loomans, M., Loonen, R., Schweiker, M., Kort, H., Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings, Built Environ, 242, 110502, 2023.

9. Sujana, C. M., Wicaksana, C. H., Level of awareness of stakeholders in the construction industry regarding the green building concept in Indonesia, IOP Conference Series: Earth and Environmental Science, 1324(1), 012052, 2024.
10. Antoniadou, P., Papadopoulos, A. M., Occupants’ thermal comfort: State of the art and the prospects of personalized assessment in office buildings, Energy Build, 153, 136-149, 2017.
11. Xie, J., Li, H., Li, C., Zhang, J., Luo, M., Review on occupant-centric thermal comfort sensing, predicting, and controlling, Energy Build, 226, 110392, 2020.
12. Ortiz, M. A., Kurvers, S. R., Bluyssen, P. M., A review of comfort, health, and energy use: Understanding daily energy use and wellbeing for the development of a new approach to study comfort, Energy Build, 152, 323-335, 2017.
13. Kim, J., Schiavon, S., Brager, G., Personal comfort models–A new paradigm in thermal comfort for occupant-centric environmental control, Build Environ, 132, 114-124, 2018.
14. Nicol, J. F., Humphreys, M. A., Adaptive thermal comfort and sustainable thermal standards for buildings, Energy Build, 34(6), 563-572, 2002.
15. Schweiker, M., Huebner, G. M., Kingma, B. R., Kramer, R., Pallubinsky, H., Drivers of diversity in human thermal perception–A review for holistic comfort models, Temperature, 5(4), 308-342, 2018.
16. Molina, G., Donn, M., Johnstone, M. L., MacGregor, C., The feeling of comfort in residential settings I: a qualitative model, Buildings and Cities, 4(1), 2023.
17. Rupp, R. F., Vásquez, N. G., Lamberts, R., A review of human thermal comfort in the built environment, Energy Build, 105, 178-205, 2015.
18. Zhao, Q., Lian, Z., Lai, D., Thermal comfort models and their developments: A review, Energy Built Environ, 2(1), 21-33, 2021.
19. Donkers, A., de Vries, B., Yang, D., Creating occupant-centered digital twins using the occupant feedback ontology implemented in a smartwatch app, Semantic Web, 1-26, 2022.
20. Mitkees, L., Heidarinejad, M., Sabatino, M., Stephens, B., Occupant thermal comfort and indoor environmental conditions in Mies van der Rohe’s SR Crown Hall, Build Res Inform, 52(3), 249-272, 2024.
21. Han M., May R., Zhang X., Wang X., Pan S., Yan D. and Xu L., A review of reinforcement learning methodologies for controlling occupant comfort in buildings, Sustainable cities and society, 51(101748), 2019.
22. Pritoni, M., Salmon, K., Sanguinetti, A., Morejohn, J., Modera, M., Occupant thermal feedback for improved efficiency in university buildings, Energy Build, 144, 241-250, 2017.
23. Koch, C., Neges, M., König, M., Abramovici, M., Natural markers for augmented reality-based indoor navigation and facility maintenance, Autom. Constr., 48, 18-30, 2014.
24. Hua, Y., Göçer, Ö., Göçer, K., Spatial mapping of occupant satisfaction and indoor environment quality in a Leed Platinum campus building, Build Environ, 79, 124-137, 2014.
25. Göçer, Ö., Hua, Y., Göçer, K., Completing the missing link in building design process: enhancing post-occupancy evaluation method for effective feedback for building performance, Build Environ, 89, 14-27, 2015.
26. Patlakas, P., Koronaios, G., Raslan, R., Neighbour, G., Altan, H., Case studies of environmental visualization, Energies, 10(10), 1459, 2017.
27. Pin, C., Medina, C., McArthur, J.J., Supporting post-occupant evaluation through work order evaluation and visualization in FM-BIM, International Symposium on Automation and Robotics in Construction (ISARC), Berlin-Almanya, 1-8, 20-25 Temmuz, 2018.
28. Alavi, H., Forcada, N., Bortolini, R., Edwards, D.J., Enhancing occupants’ comfort through BIM-based probabilistic approach, Autom Constr, 123, 103528, 2021.
29. Aguilar, A.J., de la Hoz-Torres, M.L., Ruiz, D., Martínez-Aires, M.D., Monitoring and assessment of indoor environmental conditions in educational building using building information modelling methodology, Int J Environ Re. Public Health, 19 (21), 13756, 2022.
30. Abdelrahman, M.M., Miller, C., Targeting occupant feedback using digital twins: adaptive spatial–temporal thermal preference sampling to optimize personal comfort models, Build Environ, 218, 109090, 2022.
31. Gray, F.M., Dibowski, H., Gall, J., Braun, S., Occupant feedback and context awareness: on the application of building information modeling and semantic technologies for improved complaint management in commercial buildings, 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna-Avusturya, 1, 101-108, 8-11 Eylül, 2020.
32. Alavi, H., Forcada, N., User-centric BIM-based framework for HVAC root-cause detection, Energies, 15(10), 3674, 2022.
33. Edirisinghe, R., Woo, J., BIM-based performance monitoring for smart building management, Facilities, 39(1/2), 19-35, 2020.
34. ASHRAE, ANSI/ASHRAE Standard 55, Thermal environmental conditions for human occupancy, Atlanta: American Society of Heating Refrigeration and Air Conditioning Engineers, 2023.
35. Nicol, F., Adaptive thermal comfort standards in the hot–humid tropics, Energy Build, 36(7), 628-637, 2004.
36. ISO 7730, Analytical determination and interpretation of thermal comfort using calculation of the hafta PMV and PPD indices and local thermal comfort criteria, ISO Standard No. 45001:2018, Yayın Tarihi Temmuz 7, 2007, Erişim Tarihi Mart 15, 2024.
37. Parkinson, T., De Dear, R., Thermal pleasure in built environments: physiology of alliesthesia, Build Res Inf, 43(3), 288-301, 2015.
38. Attia, S. G., Hensen, J. L., Investigating the impact of different thermal comfort models for zero energy buildings in hot climates, International Conference on Energy and Indoor Environment for Hot Climates, Doha-Qatar, 110-117, 24-26 Şubat, 2014.
39. Cole, R. J., Brown, Z., McKay, S., Building human agency: a timely manifesto. Build Res Inf, 38(3), 339-350, 2010.
40. De Dear, R., Brager, G. S., Developing an adaptive model of thermal comfort and preference, 1998.
41. Cabanac, M., Physiological role of pleasure: a stimulus can feel pleasant or unpleasant depending upon its usefulness as determined by internal signals, Science, 173(4002), 1103-1107, 1971.
42. De Dear, R., Revisiting an old hypothesis of human thermal perception: alliesthesia, Build Res Inf, 39(2), 108-117, 2011.
43. Ma, N., Chen, L., Hu, J., Perdikaris, P., Braham, W. W., Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification, Build Environ, 198, 107875, 2021.
44. Heydarian, A., McIlvennie, C., Arpan, L., Yousefi, S., Syndicus, M., Schweiker, M., Jazizadeh, F., Rissetto, R., Pisello, A. L., Piselli, C., Berger, C., Yan, Z., Mahdavi, A., What drives our behaviors in buildings? A review on occupant interactions with building systems from the lens of behavioral theories, Build Environ, 179, 106928, 2020.
45. Martins, L. A., Soebarto, V, Williamson, T., A systematic review of personal thermal comfort models, Built Environ, 207(A), 108502, 2022.
46. Akgül M. Ç., Dino, G. İ., Climate change impact assessment in residential buildings utilizing RCP4.5 and RCP8.5 scenarios, Journal of the Faculty of Engineering and Architecture of Gazi University, 3, 1665-1683, 2020.
47. Lovins, A., Air-conditioning comfort: Behavioral and cultural issues, 1992.
48. Leaman, A., Bordass, B., Thermal comfort and personal control: Understanding the psychological effects of temperature in the workplace, Environ Behav, 31(6), 817-845, 1999.
49. Nkurikiyeyezu, K., Suzuki, Y., Maret, P., Lopez, G., Itao, K., Conceptual design of a collective energy-efficient physiologically-controlled system for thermal comfort delivery in an office environment, SICE J Control Meas Syst Integr, 11(4), 312-320, 2018.
50. IPEEC Building Energy Efficiency Taskgroup, Building Energy Performance Gap Issues: An International Review, https://www.energy.gov.au/sites/default/files/the_building_energy_performance_gap-an_international_review-december_2019.pdf. Yayın Tarihi Kasım, 2019, Erişim Tarihi Mayıs 23, 2024.
51. Wyon, D., Indoor environmental effects on productivity, Paths to Better Building Environments, 96, 5-15, 1996.
52. Frontczak, M., Schiavon, S., Goins, J., Arens, E., Zhang, H., Wargocki, P., Quantitative relationships between occupant satisfaction and satisfaction aspects of indoor environmental quality and building design, Indoor air, 22(2), 119-131, 2012.
53. Huang, Y., Zhang, Y., Yang, L., Thermal comfort study in outdoor environments in a subtropical climate, Building and Environment, 51, 21-29, 2012.
54. Owolabi, H. O., Ayandele, J. K., Olaoye, D. D., A systematic review of structural equation model (SEM), Open J Educ Dev, 1(2), 27–39, 2020.
55. Kubo, S., Aoki, S., Nakano, M., Tsuji, H., Inoue, S., Mimura, E., Structural equation modeling for comfort and thermal sensation, J Jpn Soc Fuzzy Theory Intel Informat, 20(2), 164–170, 2008.
56. Koretsune, A., Kubo, S., Nakano, M., Tsuji, H., Jinno, Y., Mimura, E., Causal analysis for thermal comfort votes. 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, Tayvan, 6, 4854–4859, 8-11 Ekim, 2006.
57. Elnabawi, M. H., Jamei, E., The thermal perception of outdoor urban spaces in a hot arid climate: A structural equation modelling (SEM) approach, Urban Clim, 54, 101969, 2024.
58. Zainal, R., Khamis, N. S. N., Musa, S. M. S., Hamza, G., Enhancing thermal comfort in green residential colleges: A comprehensive model integrating building characteristics, occupant behaviour, and social factors, J Adv Res Fluid Mech Therm Sc., 132(1), 158–174, 2025.
59. Sugini, Nugraha, J., Thermo-adaptive-psychological thermal comfort index of PMVTAPSEM: Development of a PMVTAP index based on the SEM approach, McLellan, B. (Eds), Sustainable Future for Human Security. Springer, Singapore, 6, 237–249, 2018.
60. Andalas, B., Kusnoputranto, H., Utomo, S. W., Koestoer, R. H., Spatial perspective on thermal comfort and energy consumption: A PLS-SEM approach, EAI Endorsed Trans Energy Web, 7(28), 2020.
61. Gnecco, V. M., Pigliautile, I., Pisello, A. L., Exploring office comfort and productivity in living labs: A yearlong structural equation modeling study, Build Environ, 250, 111147, 2024.
62. Tekce, I., Ergen, E., & Artan, D., Structural equation model of occupant satisfaction for evaluating the performance of office buildings, Arabian J Sci Eng, 45(10), 8759–8784, 2020.
63. Chen, Y., Chen, Y., Chen, B., Deng, J., Xu, S., The integration model of objective and subjective data of residential indoor environment quality in northeast China based on structural equation modeling, Build Simul, 15, 1745–1758, 2021.
64. Zhang, D., Mui, K. W., Wong, L. T., Ten questions concerning indoor environmental quality (IEQ) models: The development and applications, Appl Sci, 13(5), 3343, 2023.
65. Loftness, V., Aziz, A., Choi, J., Kampschroer, K., Powell, K., Atkinson, M., Heerwagen, J., The value of post-occupancy evaluation for building occupants and facility managers, Intell Build Int, 1(4), 249-268, 2009.
66. Choi, J. H., Lee, K., Investigation of the feasibility of POE methodology for a modern commercial office building, Build Environ, 143, 591-604, 2018.
67. Hahn, J., Heiler, S., Kane, M. B., Park, S., Jensch, W., The information gap in occupant-centric building operations: lessons learned from interviews with building operators in Germany, Front Built Environ, 8, 838859, 2022.
68. Binalhaj, M., Liu, H., Sulaiman, M., Abudayyeh, O., Mobile crowdsourcing-based data collection for user-centered facility maintenance management, Can J Civ Eng, 48(12), 1652-1662, 2021.
69. Lazarova-Molnar, S., Logason, H. Þ., Andersen, G., Kjærgaard, M. B., Mobile crowdsourcing of data for fault detection and diagnosis in smart buildings, International Conference on Research in Adaptive and Convergent Systems, Odense-Danimarka, 12-17, 11-14 Ekim, 2016.
70. Taneja, O., Measures for maintaining low energy use in commercial buildings over their lifecycle, ASHRAE Transactions, 117, 525, 2011.
71. Yang, L., Yan, H., Lam, J. C., Thermal comfort and building energy consumption implications–a review, Appl Energy, 115, 164-173, 2014.
72. Alfano, F. R. D. A., Olesen, B. W., Palella, B. I., Riccio, G., Thermal comfort: Design and assessment for energy saving, Energy Build, 81, 326-336, 2014.
73. Tekce, I., Ergen, E., Artan, D., Structural equation model of occupant satisfaction for evaluating the performance of office buildings, Arabian J Sci Eng, 45(10), 8759-8784, 2020.
74. Lassen, N., Josefsen, T., Goia, F., Design and in-field testing of a multi-level system for continuous subjective occupant feedback on indoor climate, Build Environ, 189, 107535, 2021.
75. Andargie, M. S., Azar, E., An applied framework to evaluate the impact of indoor office environmental factors on occupants’ comfort and working conditions, Sustainable Cities Soc, 46, 101447, 2019.
76. Kwon, M., Remøy, H., Van den Bogaard, M., Influential design factors on occupant satisfaction with indoor environment in workplaces, Build Environ, 157, 356-365, 2019.
77. Korsavi, S. S., Montazami, A., Children's thermal comfort and adaptive behaviours; UK primary schools during non-heating and heating seasons, Energy Build, 214, 109857, 2020.
78. Ming, R., Yu, W., Zhao, X., Liu, Y., Li, B., Essah, E., Yao, R., Assessing energy saving potentials of office buildings based on adaptive thermal comfort using a tracking-based method, Energy Build,, 208, 109611, 2020.
79. Gou, Z., Siu‐Yu Lau, S., Post‐occupancy evaluation of the thermal environment in a green building, Facilities, 31(7/8), 357-371, 2013.
80. Shahzad, S., Brennan, J., Theodossopoulos, D., Calautit, J. K., Hughes, B. R., Does a neutral thermal sensation determine thermal comfort?, Build Serv Eng Res Technol, 39(2), 183-195, 2018.
81. Gong, X., Meng, Q., Yu, Y., A field study on thermal comfort in multi-storey residential buildings in the Karst area of Guilin, Sustainability, 13(22), 12764, 2021.
82. Li, P., Froese, T. M., Brager, G., Post-occupancy evaluation: State-of-the-art analysis and state-of-the-practice review, Build Environ, 133, 187-202, 2018.
83. Jazizadeh, F., Kavulya, G., Klein, L., Becerik-Gerber, B., Continuous sensing of occupant perception of indoor ambient factors, J Comput Civ Eng, 161-168, 2011.
84. Khan, D. S., Kolarik, J., Weitzmann, P., Design and application of occupant voting systems for collecting occupant feedback on indoor environmental quality of buildings–a review, Build Environ, 183, 107192, 2020.
85. Inyim, P., Carmenate, T., Hidalgo, D., Reyes, M., Leante, D., Bobadilla, L., Mostafavi, A., Smart applications for the integrated sensing, simulation, and feedback of occupant behaviors to enable personalized interventions for energy saving in buildings, San Juan-Porto Riko, 960-969, 31 Mayıs-2 Haziran, 2016.
86. Ramsauer, D., Dorfmann, M., Tellioglu, H., Kastner, W., Human perception and building automation systems, Energies, 15(5), 1745, 2022.
87. Shalabi, F., Turkan, Y., IFC BIM-based facility management approach to optimize data collection for corrective maintenance, J Perform Constr Facil, 31(1), 4016081, 2017.
88. Gupta, S.K., Atkinson, S., O’Boyle, I., Drogo, J., Kar, K., Mishra, S., Wen, J.T., BEES: real-time occupant feedback and environmental learning framework for collaborative thermal management in multi-zone, multi-occupant buildings, Energy Build, 125, 142-152, 2016.
89. Comfy, Intelligent Workplaces for Dynamic Businesses, https://comfyapp.com/. Erişim Tarihi: Mart 31, 2024.
90. TherMOOstat, TherMOOstat, https://thermoostat.ucdavis.edu/. Erişim Tarihi Kasım 29, 2022.
91. Rittenbruch, M., Donovan, J., Santo, Y., Mini-orb: a personal indoor climate preference feedback interface, Conference on Human-Computer Interaction (IFIP), Bamberg-Almanya, 134-149, 14-18 Eylül, 2015.
92. Clear, A., Mitchell Finnigan, S., Olivier, P., Comber, R., ThermoKiosk: investigating roles for digital surveys of thermal experience in workplace comfort management, CHI Conference on Human Factors in Computing Systems, Montreal QC- Kanada, 1-12, 21 - 26 Nisan, 2018.
93. Zhao, J., Lam, K., Ydstie, B., Loftness, V., Occupant-oriented mixed-mode energyplus predictive control simulation, Energy Build, 117, 362-371, 2016.
94. Kauppinen, T., Litvinova, E., Kallenbach, J., Capturing and linking human sensor observations with YouSense, Proceedings of the International Semantic Web Conference, Riva del Garda- İtalya, 1272, 19-23, 19-23 Ekim, 2014.
95. Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., Orosz, M., User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings, Energy Build, 70, 398-410, 2014.
96. Ghahramani, A., Tang, C., Becerik-Gerber, B., An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling, Build Environ, 92, 86-96, 2015.
97. Abdallah, M., Clevenger, C., Vu, T., Nguyen, A., Sensing occupant comfort using wearable technologies, Construction Research Congress, San Juan-Porto Riko, 940-950, 31 Mayıs-2 Haziran, 2016.
98. Barrios, L., Kleiminger, W., The comfstat-automatically sensing thermal comfort for smart thermostats, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kona, HI-Amerika Birleşik Devletleri, 257-266, 13-17 Mart, 2017.
99. Zhang, S., Cheng, Y., Fang, Z., Huan, C., Lin, Z., Optimization of room air temperature in stratum-ventilated rooms for both thermal comfort and energy saving, Appl Energy, 204, 420-431, 2017.
100. Jiang, J., Wang, D., Liu, Y., Xu, Y., Liu, J., A study on pupils' learning performance and thermal comfort of primary schools in China, Build Environ, 134, 102-113, 2018.
101. Saber, E. M., Iyengar, R., Mast, M., Meggers, F., Tham, K. W., Leibundgut, H., Thermal comfort and IAQ analysis of a decentralized DOAS system coupled with radiant cooling for the tropics, Build Environ, 82, 361-370, 2014.
102. Deng, Z., Chen, Q., Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems, Energy Build, 238, 110860, 2021.
103. Valančius, R., Jurelionis, A., Impact of temperature variation on energy consumption and productivity of the occupants in office buildings, Energetika, 58(3), 141–147, 2012.
104. Van Craenendonck, S., Lauriks, L., Vuye, C., Kampen, J., Local effects on thermal comfort: Experimental investigation of small-area radiant cooling and low-speed draft caused by improperly retrofitted construction joints, Build Environ, 147, 188-198, 2019.
105. Sansaniwal, S. K., Mathur, J., Mathur, S., Review of practices for human thermal comfort in buildings: present and future perspectives, Int J Ambient Energy, 43(1), 2097-2123, 2022.
106. MacDonald, R. R., Missing data-Quantitative applications in the social sciences, Br J Math Stat Psychol, 55, 193, 2002.
107. Taber, K. S., The use of Cronbach’s alpha when developing and reporting research instruments in science education, Res Sci Educ, 48, 1273-1296, 2018.
108. Byrne, B. M., Watkins, D., The issue of measurement invariance revisited, J Cross Cult Psychol 34(2), 155–175, 2003.
109. Hancock, G. R., Mueller, R. O. (Eds.), Structural equation modeling: A second course, Iap, 2013.
110. Wang, J., Wang, X., Structural equation modeling: Applications using Mplus, John Wiley & Sons, 2019.
111. Hoyle, R. H., Path analysis and structural equation modeling with latent variables, 2012.
112. Ergen, E., Kula, B., Guven, G., Artan, D., Formalization of Occupant Feedback and Integration with BIM in Office Buildings, J Comput Civ Eng, 35(1), 107-118, 2021.
113. Artan, D., Ergen, E., Kula B., Guven, G., RateWorkSpace: BIM Integrated Post-Occupancy Evaluation System for Office Buildings, J Inf Technol Constr, 27, 441-485, 2022.
114. Lund, A. M., Measuring usability with the use questionnaire, Usability Interface, 8(2), 3-6, 2001.
115. Brooke, J., SUS-A quick and dirty usability scale, Usability Evaluation in Industry, 189(194), 4-7, 1996.