ARCOM

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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.

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.

• Type: Journal Article
• Keywords: Urban thermal comfort; Green Sidewalks; Hyper parameters learning algorithm; Supervised machine learning;
• ISBN/ISSN: 2046-6099
• URL: https://doi.org/10.1108/SASBE-03-2019-0028
• Abstract:
  This research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results. The tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output. The process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans. Limitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step. Finding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere. By 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time. The study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.

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.

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.

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.

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.

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.

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.