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dc.contributor.authorAlzahrani, Hamdan
dc.contributor.authorArif, Mohammed
dc.contributor.authorKaushik, Amit
dc.contributor.authorGoulding, Jack
dc.contributor.authorHeesom, David
dc.date.accessioned2020-03-13T11:08:13Z
dc.date.available2020-03-13T11:08:13Z
dc.date.issued2020-04-17
dc.identifier.citationAlzahrani, H., Arif, M., Kaushik, A., Goulding, J. and Heesom, D. (2020) Artificial neural network analysis of teachers’ performance against thermal comfort, International Journal of Building Pathology and Adaptation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJBPA-11-2019-0098en
dc.identifier.issn2398-4708en
dc.identifier.doi10.1108/IJBPA-11-2019-0098
dc.identifier.urihttp://hdl.handle.net/2436/623133
dc.descriptionThis is an accepted manuscript of an article published by Emerald in International Journal of Building Pathology and Adaptation on 17/04/2020, available online at: https://doi.org/10.1108/IJBPA-11-2019-0098 The accepted manuscript may differ from the final published version.en
dc.description.abstractPurpose: The impact of thermal comfort in educational buildings continues to be of major importance in both the design and construction phases. Given this, it is also equally important to understand and appreciate the impact of design decisions on post-occupancy performance, particularly on staff and students. This study aims to present the effect of IEQ on teachers’ performance. This study would provide thermal environment requirements to BIM-led school refurbishment projects. Design: This paper presents a detailed investigation into the direct impact of thermal parameters (temperature, relative humidity and ventilation rates) on teacher performance. In doing so, the research methodological approach combines explicit mixed-methods using questionnaire surveys and physical measurements of thermal parameters to identify correlation and inference. It was conducted through a single case study using a technical college based in Saudi Arabia. Findings: Findings from this work were used to develop a model using an Artificial Neural Network to establish causal relationships. Research findings indicate an optimal temperature range between 23°C and 25°C, with a 65% relative humidity and 0.4m/s ventilation rate. This ratio delivered optimum results for both comfort and performance.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherEmeralden
dc.relation.urlhttps://www.emeraldgrouppublishing.com/products/journals/journals.htm?id=ijbpaen
dc.subjectthermal comforten
dc.subjectartificial neural networken
dc.subjectTeacher's Performanceen
dc.subjectIndoor environment qualityen
dc.titleArtificial neural network analysis of teachers’ performance against thermal comforten
dc.typeJournal articleen
dc.identifier.journalInternational Journal of Building Pathology and Adaptationen
dc.date.updated2020-03-10T21:11:38Z
dc.date.accepted2020-03-09
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW13032020DHen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-12-01en
refterms.dateFCD2020-03-13T11:07:38Z
refterms.versionFCDAM
refterms.dateFOA2020-03-13T11:08:13Z


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