Alzahrani, H; Arif, M; Kaushik, A; Goulding, J and Heesom, D (2021) Artificial neural network analysis of teachers' performance against thermal comfort. International Journal of Building Pathology and Adaptation, 39(1), pp. 20-32. ISSN 23984708
Abstract
Purpose: 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/methodology/approach: 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. This 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 (ANN) to establish causal relationships. Research findings indicate an optimal temperature range between 23 and 25°C, with a 65% relative humidity and 0.4 m/s ventilation rate. This ratio delivered optimum results for both comfort and performance. Originality/value: This paper presents a unique investigation into the effect of thermal comfort on teacher performance in Saudi Arabia using ANN to conduct data analysis that produced indoor environmental quality optimal temperature and relative humidity range.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; indoor environment quality; teacher's performance; thermal comfort |
| Index terms: | data analysis, educational building, ventilation, artificial neural network, survey, methodology, questionnaire, relative humidity, design decision, case study, Saudi Arabia, indoor environment, comfort, investigation, indoor environmental quality, thermal comfort, design and construction, refurbishment project, thermal environment |
| Subjects: | infrastructure engineering, Geography, research methods, design practice, environmental health, environmental engineering, occupational health and safety management, air quality, data collection methods, construction type, environmental science, data analysis and analytics, contractual arrangements, modelling and simulation |
| Topics: | Procurement, Construction Technology, Sustainability, Health and Safety, Research Practice, Geographical Context, Engineering Principles, Design Practice |
| Descriptive scope: | 5 PCTEA |
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