Artificial neural network analysis of teachers’ performance against thermal comfort

Alzahrani, Hamdan; Arif, Mohammed; Kaushik, Aditya; Goulding, Jack Steven; Heesom, David

doi:10.1108/ijbpa-11-2019-0098

Cited by 6 publications

(5 citation statements)

References 48 publications

(56 reference statements)

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“…The fifth sub-section presents occupant and building interaction, and it presents a review of occupant productivity and occupant behaviour. (Fanger, 1970;Haghighat et al, 2000;ISO, 2005;La Gennusa et al, 2007;Hoof, 2008;ASHRAE, 2010;De Dear, 2011;Orosa and Oliveira, 2011;Chen and Chang, 2012;Halawa and Van Hoof, 2012;Li, Yu and Li, 2012;Langevin, Wen and Gurian, 2013;Maiti, 2014;Wang et al, 2014;Martínez et al, 2015;Gangisetti et al, 2016;Moon and Jung, 2016;Martinez-Molina et al, 2017b;Alzahrani et al, 2018;B. Yang et al, 2018;Deng and Chen, 2018;Elizabeth Amudhini Stephen, 2018;Hang and Kim, 2018;Hong et al, 2018b;Jiang et al, 2018a;Zhang et al, 2018Zhang et al, , 2020Escandón, Ascione, et al, 2019b;Haddad, Osmond and King, 2019;Hellwig et al, 2019;Jindal, 2019;Kwak and Huh, 2019;Ma, Liu and Shang, 2019;Piasecki et al, 2019;Tewari et al, 2019;Xu, Li and Zhang, 2019;Ali et al, 2020;…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

“…Several studies have incorporated industry 4.0 technologies into the thermal comfort models to cope with the various factors influencing both models of thermal comfort. Artificial Intelligence, for examplethe use of Artificial Neural Network (ANN) methods, has been incorporated into several studies (Li, Yu and Li, 2012;Moon and Jung, 2016;Alzahrani et al, 2018;Deng and Chen, 2018;Escandón, Ascione, et al, 2019b;Ma, Liu and Shang, 2019;Gładyszewska-Fiedoruk and Sulewska, 2020;Palladino, Nardi and Buratti, 2020;. ANN can provide the personalisation of thermal comfort settings (Karyono et al, 2020).…”

Section: Thermal Comfort Model Developmentmentioning

confidence: 99%

“…Thermal Comfort conditions affect occupants' productivity (Wargocki et al, 1999;Edwards and Torcellini, 2002;De Giuli, Da Pos and De Carli, 2012;Boerstra et al, 2015;Al Horr, Katafygiotou, et al, 2016;Arif et al, 2016;Kang, Ou and Mak, 2017) and task performance (Akimoto et al, 2010;Hoque and Weil, 2016;Liu et al, 2018;Wargocki, Porras-Salazar and Contreras-Espinoza, 2019). Researchers in the field have focused on studies in a single zone, such as offices (Al Horr, Arif, et al, 2016b;Mustapa et al, 2016;Rijal, Humphreys and Nicol, 2017;Tarantini, Pernigotto and Gasparella, 2017;Amit Kaushik et al, 2020) or classrooms (Bakó-Biró et al, 2012;De Dear et al, 2015;Hoque and Weil, 2016;Jiang et al, 2018b;Lau, Zhang and Tao, 2019;Alzahrani et al, 2021;Kükrer and Eskin, 2021a). However, far too little attention has been paid to multipurpose buildings (Kükrer and Eskin, 2021b).…”

Section: Productivity and Task Performancementioning

confidence: 99%

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Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

et al. 2023

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The building sector is one of the most resource-exhausting areas in global energy consumption. Maintaining good thermal comfort for occupants is the leading energy demand in buildings. The primary purpose of the current study is to identify the development of research areas on occupant comfort, pinpoint the gaps in knowledge and recommend directions for future studies. A scientometric analysis and a comprehensive systematic literature review are conducted using 792 sources. It is evident from the exponential increase in published papers that scholars are highly interested in this research topic. However, discrepancies remain between the two fundamental models of evaluating thermal comfort. There is a pressing need to balance thermal comfort while increasing energy efficiency. The foundation of achieving this balance can only be done by correctly evaluating the surrounding environment of occupants and understanding all the factors influencing human thermal comfort conditions. There is also a high potential in employing industry 4.0 technologies to assist in designing more innovative solutions for thermal comfort. Furthermore, there is a need for local thermal standards targeting specific regions. The lack of interoperability between BIM 3D modelling and energy simulation tools remains an obstacle.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Section: Thermal Comfort Model Developmentmentioning

confidence: 99%

Section: Productivity and Task Performancementioning

confidence: 99%

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Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

et al. 2023

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“…Thermal comfort is defined according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) as "the mental condition that expresses satisfaction with the thermal environment and is evaluated subjectively" [3]. This can impact occupants' behavior, for example, in a classroom, where dissatisfaction with thermal comfort can cause a reduction in concentration and productivity, essential elements to improve the performance of students and teachers [4,5].…”

Section: Introductionmentioning

confidence: 99%

Variables That Affect Thermal Comfort and Its Measuring Instruments: A Systematic Review

et al. 2022

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Thermal comfort can impact the general behavior of the occupants, and considering that humans currently perform 90% of their daily work indoors, it is necessary to improve the accuracy of thermal comfort assessments, and a correct selection of variables could make this possible. However, no review integrates all the variables that could influence thermal comfort evaluation, which relates them to their respective capture devices. For this reason, this research identifies all the variables that influence the thermal comfort of a building, together with the measurement tools for these variables, evaluating the relevance of each one in the research carried out to date. For this purpose, a systematic literature review was carried out by analyzing a set of articles selected under certain defined inclusion/exclusion criteria. In this way, it became evident that the most used variables to measure thermal comfort are the same as those used by the predicted mean vote (PMV) model; however, research focused on the behavior of the occupants has focused on new variables that seek to respond to individual differences in human thermal perception.

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“…In a recent study published in the International Journal of Building Pathology and Adaptation, Alzahrani et al (2020) discovered through postoccupancy performance that IEQ has a strong correlation to teacher performance. School buildings constructed prior to 1975 were reported by Finnish teachers to cause health issues they attributed to indoor environmental quality (Turunen et al, 2014).…”

Section: Teachers and Iaqmentioning

confidence: 99%

Toxic schools? Teacher perceptions of indoor air quality and negative place attachment

Loraas¹

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The perception of indoor air quality (IAQ) in school buildings has garnered much attention. The self-reported experiences of teachers regarding the phenomenon of suffering from toxic IAQ was missing from scholarly work before the onset of the coronavirus. Toxic IAQ can be defined as the presence of toxic chemicals or compounds (including biological) in the air at levels that pose health risks and can affect a person's health, comfort, and performance (Environmental Protection Agency [EPA], 2018a). Since the onset of the pandemic, teachers are leaving the workforce in unprecedented numbers due to poor working conditions, unreasonable demands, and unrealistic expectations (Steiner and Woo, 2021). Addressing teacher retention is critical to stymie continuing teacher shortages and the adverse impact on students. This sequential mixed-methods study confronts the gap between place theory, specifically the negative emotional person-place bond, and perceived IAQ in public school buildings. Little research has been presented on the role the physical workplace has on teacher well-being and whether psychosociological environmental relationships can predict place attachment outcomes. The question of how teacher perceptions of IAQ relate to negative place attachment was explored using survey research of 242 educators in four public school districts in the Midwest. Survey data was collected April-May of 2021, with 13 follow-up purposive interviews, with the criteria of teachers' presenting negative place attachment feelings, during August 2021. The research revealed the more teachers realize their health concerns about toxic IAQ in their workplace, the more negative place attachment they feel. This involves the process of grieving, and feeling frustrated, angry, exhausted, and confused, like separation and divorce. When a teacher has crossed a threshold of divorced feelings toward the school building, they make choices: to stay employed, assigned to their building, feeling negative place attachment, ask to be reassigned, or leave. The two significant predictors of negative place attachment revealed through stepwise linear regression, were physical "healthy building" attributes and health concerns about the IEQ/IAQ in the school environment. Teachers' perceptions of aged buildings as being unhealthy, including the inoperability of classroom windows, aged carpet, and lack of ventilation were better understood by understanding what it means to occupy a workplace teachers perceive to have toxic IAQ. Employees with health conditions experienced feelings of being misunderstood, not taken seriously, and additionally faced a host of complicated social interactions with their administrators, co-workers, and family because of health ailments they attributed to their workplace. The study resulted in the creation of two new theoretic models: a revisiting of Tripartite Model of Place Attachment to include place detachment, the threshold crossed in absentia of any place attachment feelings, and an epidemiological model for addressing indoor air quality in schools and suggested interventions for practice. While these models help to develop methods, redress, and identification for negative place attachment due to indoor air quality, it was not possible to identify a consistent predictor of negative place attachment. This suggests that the themes identified in the interview process alongside a predictor model can help identify schools where intervention is essential.

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Artificial neural network analysis of teachers’ performance against thermal comfort

Cited by 6 publications

References 48 publications

Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

Variables That Affect Thermal Comfort and Its Measuring Instruments: A Systematic Review

Toxic schools? Teacher perceptions of indoor air quality and negative place attachment

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