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Leveraging AI and IoT for Improved Management of Educational Buildings

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Advances in Information Technology in Civil and Building Engineering (ICCCBE 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 357))

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Abstract

As countries around the world gradually return to life before the Covid-19 pandemic, it is important for facility management divisions across education sectors to use innovative technology and unique solutions to provide healthy and safe learning environments. This research aimed to improve the management of educational buildings by leveraging innovative technologies. The first objective was to develop a system for real-time occupancy levels within lecture venues. To achieve this, a branch of artificial intelligence known as computer vision was combined with existing CCTV cameras to count occupants in real-time. This was achieved by training a convoluted neural network on a dataset of 15 000 images of ‘human bodies’ extracted from Googles Open Images v6. The second objective was to measure indoor air quality. A medical grade air quality device was placed within the assessed lecture venues and real-time occupant count was correlated against real-time indoor air quality data. The results from this study demonstrate the successful use of computer vision combined with existing CCTV cameras to accurately count occupants in real-time. The study utilised open-source AI resources and provides a method for further computer vision research. Regarding indoor air quality within the assessed educational buildings, the results of this study indicate that even under significantly reduced occupancy levels, carbon dioxide accumulated within assessed venues, indicating inadequate ventilation. The Covid-19 pandemic will not be the last pandemic we encounter. However, facility management can utilise innovative technologies to ensure educational buildings are managed using data-driven strategies that ensure learning environments remain safe and accessible spaces for students.

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References

  1. Becerra JA, Lizana J, Gil M, Barrios-Padura A, Blondeau P, Chacartegui R (2020) Identification of potential indoor air pollutants in schools. J Clean Prod 242:118420. https://doi.org/10.1016/j.jclepro.2019.118420

  2. Busta H (2016) KPMG report: construction industry slow to adopt new technology. ConstructionDive. https://www.constructiondive.com/news/kpmg-report-construction-industry-slow-to-adopt-new-technology/426268/

  3. Dorizas PV, Assimakopoulos M-N, Santamouris M (2015) A holistic approach for the assessment of the indoor environmental quality, student productivity, and energy consumption in primary schools. Environ Monit Assess 187(5):259. https://doi.org/10.1007/s10661-015-4503-9

    Article  Google Scholar 

  4. Hess-Kosa K (2019) Indoor Air Quality: The Latest Sampling and Analytical Methods (Third). CRC Press Taylor & Francis Group

    Google Scholar 

  5. Sadat S, Montazami A, Mumovic D (2020) Indoor air quality (IAQ) in naturally-ventilated primary schools in the UK: occupant-related factors. Build Environ 180(March)

    Google Scholar 

  6. Shaw K (2020) Elon Musk Promised 1 Million Tesla Robotaxis by the End of 2020. Where Are They? Thedrive. https://www.thedrive.com/news/38129/elon-musk-promised-1-million-tesla-robotaxis-by-the-end-of-2020-where-are-they

  7. Tien PW, Wei S, Calautit JK, Darkwa J, Wood C (2020) A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions. Energy Build 226:110386. https://doi.org/10.1016/j.enbuild.2020.110386

    Article  Google Scholar 

  8. Twardella D et al (2012) Effect of classroom air quality on students’ concentration: results of a cluster-randomized cross-over experimental study. Indoor Air 22(5):378–387. https://doi.org/10.1111/j.1600-0668.2012.00774.x

  9. Vornanen-winqvist C et al (2020) Exposure to indoor air contaminants in school buildings with and without reported indoor air quality problems. Environ Int 141(April):105781. https://doi.org/10.1016/j.envint.2020.105781

    Article  Google Scholar 

  10. Wang D, Song C, Wang Y, Xu Y, Liu Y (2020) Energy & buildings experimental investigation of the potential influence of indoor air velocity on students ‘ learning performance in summer conditions. Energy Build 219:110015. https://doi.org/10.1016/j.enbuild.2020.110015

    Article  Google Scholar 

  11. Zhong B, Wu H, Ding L, Love PED, Li H, Luo H, Jiao L (2019) Mapping computer vision research in construction: developments, knowledge gaps and implications for research. Autom Constr 107(July):102919. https://doi.org/10.1016/j.autcon.2019.102919

    Article  Google Scholar 

  12. Aguilar AJ, De La Hoz-Torres ML, Costa N, Arezes P, Martínez-Aires MD, Ruiz DP (2022) Assessment of ventilation rates inside educational buildings in Southwestern Europe: analysis of implemented strategic measures. J Build Eng 51:104204. https://doi.org/10.1016/j.jobe.2022.104204

  13. Zivelonghi A, Lai M (2021) Mitigating aerosol infection risk in school buildings: the role of natural ventilation, volume, occupancy and CO2 monitoring. Build Environ 204:108139. https://doi.org/10.1016/J.BUILDENV.2021.108139

  14. Asif A, Zeeshan M (2020) Indoor temperature, relative humidity and CO2 monitoring and air exchange rates simulation utilizing system dynamics tools for naturally ventilated classrooms. Build Environ 180:106980. https://doi.org/10.1016/J.BUILDENV.2020.106980

  15. Majd E, McCormack M, Davis M, Curriero F, Berman J, Connolly F, Leaf P, Rule A, et al (2019) Indoor air quality in inner-city schools and its associations with building characteristics and environmental factors. Environ Res 170:83–91. https://doi.org/10.1016/J.ENVRES.2018.12.012

  16. Krawczyk DA, Wadolowska B (2018) Analysis of indoor air parameters in an education building. Energy Procedia 147:96–103. https://doi.org/10.1016/J.EGYPRO.2018.07.038

  17. Laaroussi Y, Bahrar M, El Mankibi M, Draoui A, Si-Larbi A (2020) Occupant presence and behavior: a major issue for building energy performance simulation and assessment. Sustain Cities and Soc 63:102420. https://doi.org/10.1016/J.SCS.2020.102420

  18. Spataru C, Gauthier S (2013) How to monitor people ‘smartly’ to help reducing energy consumption in buildings? 10(1–2):60–78. https://doi.org/10.1080/17452007.2013.837248

  19. Yoshino H, Hong T, Nord N (2017) IEA EBC annex 53: total energy use in buildings—analysis and evaluation methods. Energy Build 152:124–136. https://doi.org/10.1016/J.ENBUILD.2017.07.038

  20. Hong T, Taylor-Lange SC, D’Oca S, Yan D, Corgnati SP (2016) Advances in research and applications of energy-related occupant behavior in buildings. Energy Build 116:694–702. https://doi.org/10.1016/J.ENBUILD.2015.11.052

  21. Day JK, McIlvennie C, Brackley C, Tarantini M, Piselli C, Hahn J, O’Brien W, Rajus VS, et al (2020) A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort. Build Environ 178:106920. https://doi.org/10.1016/J.BUILDENV.2020.106920

  22. Rothman D (2018) Artificial Intelligence By Example. 1st edn. Packt Publishing. https://www.perlego.com/book/771638/artificial-intelligence-by-example-develop-machine-intelligence-from-scratch-using-real-artificial-intelligence-use-cases-pdf. Accessed 14 October 2022

  23. Macaulay T (2020) Human-centric AI news and analysis. https://thenextweb.com/neural/2020/02/18/elon-musk-everyone-developing-ai-must-be-regulated-even-tesla/. 29 September 2020

  24. Fang W, Ding L, Love PED, Luo H, Li H, Peña-Mora F, Zhong B, Zhou C (2020) Computer vision applications in construction safety assurance. Autom Constr 110:103013. https://doi.org/10.1016/J.AUTCON.2019.103013

  25. Martinez P, Al-Hussein M, Ahmad R (2019) A scientometric analysis and critical review of computer vision applications for construction. Autom Constr 107:102947. https://doi.org/10.1016/J.AUTCON.2019.102947

  26. Tien PW, Wei S, Calautit JK, Darkwa J, Wood C (2020) A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions. Energy Build 226:110386. https://doi.org/10.1016/J.ENBUILD.2020.110386

  27. Russell S, Norvig P (2009) Artificial Intelligence A Modern Approach. Third ed. Essex.

    Google Scholar 

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Authors and Affiliations

  1. Nelson Mandela University, Port Elizabeth, 6031, South Africa

    Ashvin Manga & Christopher Allen

Authors
  1. Ashvin Manga
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  2. Christopher Allen
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Correspondence to Ashvin Manga .

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Editors and Affiliations

  1. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Sebastian Skatulla

  2. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Hans Beushausen

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Manga, A., Allen, C. (2024). Leveraging AI and IoT for Improved Management of Educational Buildings. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-35399-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35398-7

  • Online ISBN: 978-3-031-35399-4

  • eBook Packages: EngineeringEngineering (R0)

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