Artificial neural network analysis of teachers’ performance against thermal comfort

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.

Citation

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, Vol. 39 No. 1, pp. 20-32. https://doi.org/10.1108/IJBPA-11-2019-0098