Exploring Users' Perception on The State of Application of Intelligent Building System [IBSS] and Non-Intelligent Building System [NIBS] Components
DOI:
https://doi.org/10.26437/mqn2av58Keywords:
Automation. construction. industry 4.0. informatics. intelligentAbstract
Purpose: The concept of automation has brought innovation in the construction and maintenance of buildings, leading to the evolution of the Intelligent building concept. The aim of research work is to investigate the components of intelligent and non-intelligent systems with a view to improving their functionality.
Design/Methodology/Approach: The sample included respondents from intelligent and non-intelligent building occupiers, including buildings with intelligent components and non-intelligent buildings, drawn from the population frame of 10 buildings. A sample size of eighty (80) comprising intelligent and non-intelligent building users of the ten buildings was used. The results are presented in tables and figures. Statistical tools were used to process the variables, including the Relative Importance Index, Simple percentages, Spearman's Rank, the Mann-Whitney U test, the Student T-Test, and the Chi-square test. The study also explores relevant hypotheses to further confirm the discovery.
Findings: The study discovered that application of intelligent component accessories in home has gradually gained ground in Nigeria, with application of features which includes the following: Automatic Fire Alarm Control E-System and Controls; Intelligent Perimeter Security Controls System; Air movement Purification System, General Access Monitoring System; Intelligent water purification and distribution; Intelligent HVAC System; Intelligent Perimeter Lighting control Systems; Occupants Energy Management system; Automated Parking Access and control system.
Research Limitation: Data sampling was limited to Abuja and Lagos, which are centres of buildings with intelligent components, compared to other locations in the country.
Practical Implication: The study recommends adopting concurrent innovation management styles, establishing strategic operational management, and timely management of the internal and external accessory environment to prolong component durability.
Social Implication: The high cost of intelligent buildings makes them an exclusive preserve for the wealthy, while digital literacy among users is another challenge. A significant amount of wealth distribution is required for effective automation in Buildings.
Originality/Value: The study identified new components that can be easily integrated into buildings to achieve building intelligence. It includes the following: Air movement Purification System, Intelligent water purification and distribution and Occupants Energy Management system.
References
Afolabi, A. O., Oyeyipo, O. O., Ojelabi, R. A., & Amusan, L. M. (2018). Construction
Professionals' Perception of a Web-Based Recruiting System for Skilled Labour. Journal of Theoretical & Applied Information Technology, 96(10)285-289.
Ako-Adjei, J. T., Alhassan, T., Adjei, P. O., & Asante, A. (2024). Level of Awareness of
Industrialised Building Systems (IBS) in The Ghanaian Construction Industry. African Journal of Applied Research, 10(2), 109-122. DOI: https://doi.org/10.26437/ajar.v10i2.797
Arun, M., Barik, D., Chandran, S. S., Praveenkumar, S., & Tudu, K. (2025). Economic,
policy, social, and regulatory aspects of AI-driven smart buildings. Journal of Building Engineering, 99, 111666. DOI: https://doi.org/10.1016/j.jobe.2024.111666
Atayero, A. A., Ademu-Eteh, V., Popoola, S. I., Takpor, T. O., & Badejo, J. A. (2017).
Occupancy Controlled Lighting System for Smart Buildings. Proceedings of the World Congress on Engineering and Computer Science 2017 Vol II WCECS 2017, October 25-27, 2017, San Francisco, USA.
Billanes, J. D., Ma, Z. G., & Jørgensen, B. N. (2025). Data-driven technologies for energy
optimization in smart buildings: A scoping review. Energies, 18(2), 290. DOI: https://doi.org/10.3390/en18020290
Brooks, D. J. (2012). Intelligent buildings: an investigation into current and emerging
security vulnerabilities in automated building systems using an applied defeat methodology. DOI: 10.4018/978-1-4666-2659-1.ch001. DOI: https://doi.org/10.4018/978-1-4666-2659-1.ch001
Brooks, D. J., Coole, M., Haskell-Dowland, P., Griffiths, M., & Lockhart, N. (2017).
Building Automation & Control Systems: An Investigation into Vulnerabilities, Current Practice & Security Management Best Practice. ASIS Foundation Project.
Buckman, A. H., Mayfield, M., & Beck, S. (2014). What is a smart building? Journal of Smart DOI: https://doi.org/10.1108/SASBE-01-2014-0003
and Sustainable Built Environment, 3(2), 92-109.
Clements-Croome, D. (2011). Sustainable intelligent buildings for people: A review. Intelligent DOI: https://doi.org/10.1080/17508975.2011.582313
Buildings International, 3(2), 67-86.
DEGW, T. (1992). The Intelligent Building in Europe.
Delinchant, B., & Ferrari, J. (2021). Standards and technologies from building sector, iot, and DOI: https://doi.org/10.1007/978-3-030-76477-7_3
open-source trends. In Towards Energy Smart Homes: Algorithms, Technologies, and Applications (pp. 49-111). Cham: Springer International Publishing.
Elattar, M. S. (2013). Smart structures and material technologies in architecture applications.
Scientific Research and Essays, 8(31), 1512-1521 Gartner. (2013). Gartner IT glossary. https://www.gartner.com/en/informationtechnology/ Glossary.
Etemadi, N., Van Gelder, P., & Strozzi, F. (2021). An ism modeling of barriers for
blockchain/distributed ledger technology adoption in supply chains towards cybersecurity. Sustainability, 13(9), 4672. DOI: https://doi.org/10.3390/su13094672
Ghafoor, S., Gurmu, A., Sadick, A. M., & Kite, J. (2025). Compliance risks in the construction
of residential buildings: a systematic literature review. Smart and Sustainable Built Environment.
Ghansah, F. A., Owusu-Manu, D. G., Ayarkwa, J., Edwards, D. J., & Hosseini, M. R. (2021).
Exploration of latent barriers inhibiting project management processes in adopting smart building technologies (SBTs) in the developing countries. Construction Innovation, 21(4), 685-707. DOI: https://doi.org/10.1108/CI-07-2020-0116
GSA (2005). General Services/Energy & Building Automation. Annual Performance
Measurement Report. GSA. Washington DC, GSA: 4.
Hansson, S. O. (2025). Trialability. Studies in history and philosophy of science, 112, 153– DOI: https://doi.org/10.1016/j.shpsa.2025.07.002
160.
Jia, M., Komeily, A., Wang, Y., & Srinivasan, R. S. (2019). Adopting Internet of Things for
the development of smart buildings: A review of enabling technologies and applications. Automation in construction, 101, 111-126. DOI: https://doi.org/10.1016/j.autcon.2019.01.023
Jiang, Y., Wang, X., & Yuen, K. F. (2021). Augmented reality shopping application usage:
The influence of attitude, value, and characteristics of innovation. Journal of Retailing and Consumer Services, 63, 102720.
Jiao, Z., Du, X., Liu, Z., Liu, L., Sun, Z., Shi, G., & Liu, R. (2023). A review of theory and
application development of intelligent operation methods for large public buildings. Sustainability, 15(12), 9680. DOI: https://doi.org/10.3390/su15129680
Khan, O., Parvez, M., Alansari, M., Farid, M., Devarajan, Y., & Thanappan, S. (2023).
Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions. Scientific Reports, 13(1), 8200.
Khanda, K., Salikhov, D., Gusmanov, K., Mazzara, M., & Mavridis, N. (2017, March).
Microservice-based IoT for smart buildings. In 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA) (pp. 302-308). IEEE. DOI: https://doi.org/10.1109/WAINA.2017.77
Khosh-Amadi, N., Talebi, S., Cheung, F., & Al-Adhami, M. (2025). Development of a
spatially contextualised and AI-enabled digital twin for asthma-specific indoor air quality management. Journal of Building Engineering, 115027.
Kozlovska, M., Petkanic, S., Vranay, F., & Vranay, D. (2023). Enhancing energy efficiency
and building performance through BEMS-BIM integration. Energies, 16(17), 6327. DOI: https://doi.org/10.3390/en16176327
Liu, D., Son, S., & Cao, J. (2023). The determinants of public acceptance of telemedicine
apps: an innovation diffusion perspective. Frontiers in public health, 11, 1325031.
Lourenço, M. P., Arantes, A., & Costa, A. A. (2025). Barriers to building information
modeling (BIM) implementation in late-adopting EU countries: the case of Portugal. Buildings, 15(10), 1651. DOI: https://doi.org/10.3390/buildings15101651
Masroor, M., Rezazadeh, J., Ayoade, J., & Aliehyaei, M. (2023). A survey of intelligent
building automation with machine learning and IoT. Advances in Building Energy Research, 17(3), 345-378.
Michailidis, P., Michailidis, I., Minelli, F., Coban, H. H., & Kosmatopoulos, E. (2025). Model
Predictive Control for Smart Buildings: Applications and Innovations in Energy Management. Buildings, 15(18), 3298. DOI: https://doi.org/10.3390/buildings15183298
Mishra, P., & Singh, G. (2023). Energy management systems in sustainable smart cities based DOI: https://doi.org/10.1007/978-3-031-33354-5
on the internet of energy: A technical review. Energies, 16(19), 6903.
Omar, O. (2018). Intelligent building, definitions, factors and evaluation criteria of selection. DOI: https://doi.org/10.1016/j.aej.2018.07.004
Alexandria engineering journal, 57(4), 2903-2910.
Ontoyin, E. N., Essandoh-Yeddu, J., & Bamfo-Agyei, E. (2025). A climate-responsive green
building framework for Ghana: Integrating mitigation and adaptation strategies. African Journal of Applied Research, 11(2), 77-93. DOI: https://doi.org/10.26437/ajar.v11i2.972
Osawaru, F., Amusan, L., Awotinde, O., Akanya, C., Asiyanbola, O., & Akinbo, F. (2018,
July). Planning materials supply for construction works. In IOP Conference Series: Earth and Environmental Science 173, (1), 012002. IOP Publishing. DOI: https://doi.org/10.1088/1755-1315/173/1/012002
Pereira, P. F., Ramos, N. M., & Simões, M. L. (2020). Data-driven occupant actions prediction
to achieve an intelligent building. Building research & information, 48(5), 485-500.
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An
integrated approach to communication theory and research (pp. 432-448). Routledge.
Salikhov, R. B., Abdrakhmanov, V. K., & Pavlov, A. V. (2020, October). Automated system for
monitoring the ammonia concentration in the air of agricultural complexes. In 2020 International Conference on Electrotechnical Complexes and Systems (ICOECS) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICOECS50468.2020.9278469
Shrivastava, D., & Goswami, P. (2025). Internet of things driven hybrid neuro-fuzzy deep
learning building energy management system for cost and schedule optimization. Frontiers in Artificial Intelligence, 8, 1544183.
Wang, Q., Yin, Y., Chen, Y., & Liu, Y. (2024). Carbon peak management strategies for
achieving net-zero emissions in smart buildings: Advances and modeling in digital twin. Sustainable Energy Technologies and Assessments, 64, 103661.
Yitmen, I., & Alizadehsalehi, S. (2021). Overview of cyber-physical systems and enabling
technologies in cognitive computing for smart built environment. In BIM-enabled Cognitive Computing for Smart Built Environment (pp. 1-20). CRC Press.
Zand, M., Nasab, M. A., Padmanaban, S., & Khoobani, M. (2022). Big data for SMART Sensor
and intelligent electronic devices–Building application. In Smart buildings digitalization (pp. 11-28). CRC Press.
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