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Adekunle, T O (2019) Field measurements of comfort, seasonal performance and cold stress in cross-laminated timber (CLT) school buildings. Smart and Sustainable Built Environment, 9(04), 655–73.

Aggarwal, A, Rani, A and Kumar, M (2019) A robust method to authenticate car license plates using segmentation and ROI based approach. Smart and Sustainable Built Environment, 9(04), 737–47.

Aggarwal, T and Solomon, P (2019) Quantitative analysis of the development of smart cities in India. Smart and Sustainable Built Environment, 9(04), 711–26.

Agyekum, K, Adinyira, E and Ampratwum, G (2020) Factors driving the adoption of green certification of buildings in Ghana. Smart and Sustainable Built Environment, 9(04), 595–613.

de Laat, P (2019) Resource depletion: where is an intervention most effective?. Smart and Sustainable Built Environment, 8(04), 307–21.

Dell'Anna, F, Bottero, M, Becchio, C, Corgnati, S P and Mondini, G (2020) Designing a decision support system to evaluate the environmental and extra-economic performances of a nearly zero-energy building. Smart and Sustainable Built Environment, 9(04), 413–42.

Dewan, S and Singh, L (2020) Use of blockchain in designing smart city. Smart and Sustainable Built Environment, 9(04), 695–709.

du Toit, J and Wagner, C (2020) The effect of housing type on householders' self-reported participation in recycling. Smart and Sustainable Built Environment, 9(04), 395–412.

Ekemode, B G (2019) Impact of urban regeneration on commercial property values in Osogbo, Osun State, Nigeria. Smart and Sustainable Built Environment, 9(04), 557–71.

Eslamirad, N, Malekpour Kolbadinejad, S, Mahdavinejad, M and Mehranrad, M (2020) Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran. Smart and Sustainable Built Environment, 9(04), 361–74.

Ghosh, S, Kochhar, K, Sharma, A, Kaushal, S, Agrawal, J, Garg, A, Kumar, A and Dugar, Y (2016) Investigating structure generated turbulence using an unmanned aerial vehicle: A prelude to optimal ventilation strategies in India’s upcoming smart cities. Smart and Sustainable Built Environment, 5(04), 372-92.

Ghosh, S, Kochhar, K, Sharma, A, Kaushal, S, Agrawal, J, Garg, A, Kumar, A and Dugar, Y (2016) Investigating structure generated turbulence using an unmanned aerial vehicle: A prelude to optimal ventilation strategies in India’s upcoming smart cities. Smart and Sustainable Built Environment, 5(04), 372-92.

Hopkins, E A (2016) Barriers to adoption of campus green building policies. Smart and Sustainable Built Environment, 5(04), 340-51.

Hussein, D (2020) A user preference modelling method for the assessment of visual complexity in building façade. Smart and Sustainable Built Environment, 9(04), 483–501.

Khan, N A, Ullah Khan, S, Ahmed, S, Farooqi, I H, Hussain, A, Vambol, S and Vambol, V (2019) Smart ways of hospital wastewater management, regulatory standards and conventional treatment techniques. Smart and Sustainable Built Environment, 9(04), 727–36.

Konstantinou, T, de Jonge, T, Oorschot, L, El Messlaki, S, van Oel, C and Asselbergs, T (2019) The relation of energy efficiency upgrades and cost of living, investigated in two cases of multi-residential buildings in the Netherlands. Smart and Sustainable Built Environment, 9(04), 615–33.

Krueger, K, Stoker, A and Gaustad, G (2019) “Alternative” materials in the green building and construction sector. Smart and Sustainable Built Environment, 8(04), 270–91.

Kumar, A, Jain, S and Yadav, D (2020) A novel simulation-annealing enabled ranking and scaling statistical simulation constrained optimization algorithm for Internet-of-things (IoTs). Smart and Sustainable Built Environment, 9(04), 675–93.

Kumar, V, Hundal, B S and Kaur, K (2019) Factors affecting consumer buying behaviour of solar water pumping system. Smart and Sustainable Built Environment, 8(04), 351–64.

Lau, J L and Hashim, A H (2019) Mediation analysis of the relationship between environmental concern and intention to adopt green concepts. Smart and Sustainable Built Environment, 9(04), 539–56.

Lau, J L, Hashim, A H, Samah, A A and Salim, A S S (2016) Understanding the environmental worldviews of Malaysian project managers. Smart and Sustainable Built Environment, 5(04), 307-24.

Loyola, M (2019) A method for real-time error detection in low-cost environmental sensors data. Smart and Sustainable Built Environment, 8(04), 338–50.

Moshtaghian, F, Golabchi, M and Noorzai, E (2020) A framework to dynamic identification of project risks. Smart and Sustainable Built Environment, 9(04), 375–93.

Ndlangamandla, M G and Combrinck, C (2019) Environmental sustainability of construction practices in informal settlements. Smart and Sustainable Built Environment, 9(04), 523–38.

Opawole, A, Babatunde, S O, Kajimo-Shakantu, K and Ateji, O A (2020) Analysis of barriers to the application of life cycle costing in building projects in developing countries. Smart and Sustainable Built Environment, 9(04), 503–21.

Opoku, D J, Ayarkwa, J and Agyekum, K (2019) Barriers to environmental sustainability of construction projects. Smart and Sustainable Built Environment, 8(04), 292–306.

Prakash, A (2019) Smart Cities Mission in India: some definitions and considerations. Smart and Sustainable Built Environment, 8(04), 322–37.

Rahman, F, Rowlands, I and Weber, O (2017) Do green buildings capture higher market valuations and lower vacancy rates? A Canadian case study of LEED and BOMA-BEST properties. Smart and Sustainable Built Environment, 6(04), 102-15.

Saadi, A and Belhadef, H (2020) Deep neural networks for Arabic information extraction. Smart and Sustainable Built Environment, 9(04), 467–82.

Sahebzadeh, S, Dalvand, Z, Sadeghfar, M and Heidari, A (2018) Vernacular architecture of Iran’s hot regions; elements and strategies for a comfortable living environment. Smart and Sustainable Built Environment, 9(04), 573–93.

Shooshtarian, S and Ridley, I (2016) Determination of acceptable thermal range in outdoor built environments by various methods. Smart and Sustainable Built Environment, 5(04), 352-71.

  • Type: Journal Article
  • Keywords: acceptable thermal range; direct and indirect measures; effective urban planning; outdoor thermal comfort; thermal perceptions; thermal preference
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/SASBE-06-2016-0010
  • Abstract:
    Purpose Assessment of outdoor thermal perception in urban spaces is of particular importance due to its financial, social and ecological consequences. Thermal perception includes four elements: thermal sensation votes (TSV), thermal preference (Tpref), overall thermal comfort (Tc) and thermal acceptability (Taccept). Thermal acceptability can offer a benchmark that specifies the acceptable thermal range (ATR), which is useful for urban planners, designers, and bio-meteorologists. ATR, however, can be defined either using direct or indirect measures. The purpose of this paper is to investigate the validity of the indirect measures of ATR, which are most commonly used in outdoor thermal comfort assessments. Design/methodology/approach This study was conducted in the context of Melbourne, which has an oceanic temperate climate (Cfb). Three sites forming RMIT University City Campus (RUCC) were selected as the case studies, which were located in the heart of Melbourne Central Business District. A field survey was conducted in RUCC during three seasons, from November 2014 (Spring) to May 2015 (Autumn), which consisted of concurrent field measurements and questionnaire surveys from 9:00 a.m. to 5:00 p.m. Findings In total, 1,059 valid questionnaires were collected from the three sites of RUCC. The results of comparative analysis between the different measures of ATR determination showed that the various elements of thermal perceptions expressed the users’ thermal judgements in different ways. Therefore, it was found that the instruction recommended by the thermal comfort standards on the definition of ATR failed to provide an appropriate estimation of ATR for outdoor built environments. The ATR, defined using TSV, therefore, was revised by the direct measure of thermal acceptability. The resulting range showed broader limits in acceptable thermal conditions in RUCC outdoor spaces users. Lastly, the results suggest that in the absence of directly measured acceptability of thermal conditions in field surveys, overall comfort is the most appropriate indirect measure to use. Originality/value Some indoor thermal comfort studies have used the alternatives for defining ATR. However, as the applicability of these four methods is yet to be fully explored in outdoor conditions with large weather variations, it is valuable to conduct a comparative analysis among these methods. This study also intended to understand the dynamics of comfort range under non-steady and non-uniform outdoor conditions. The resultant outcome has provided information on the relationship between different measures of thermal perceptions. Ultimately, this research aimed to explore the extent to which the indirect measures of acceptability are considered as a reliable source of information compared to the direct measure.

Susilo, A, Fitriah, F, Sunaryo, Ayu Rachmawati, E T and Suryo, E A (2020) Analysis of landslide area of Tulung subdistrict, Ponorogo, Indonesia in 2017 using resistivity method. Smart and Sustainable Built Environment, 9(04), 341–60.

Tunji-Olayeni, P, Kajimo-Shakantu, K and Osunrayi, E (2020) Practitioners' experiences with the drivers and practices for implementing sustainable construction in Nigeria: a qualitative assessment. Smart and Sustainable Built Environment, 9(04), 443–65.

van Stijn, A and Gruis, V (2020) Towards a circular built environment. Smart and Sustainable Built Environment, 9(04), 635–53.

Xia, B, Rosly, N, Wu, P, Bridge, A and Pienaar, J (2016) Improving sustainability literacy of future quantity surveyors. Smart and Sustainable Built Environment, 5(04), 325-39.