ARCOM

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 22 results ...

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.

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.

• Type: Journal Article
• Keywords: Building information modeling (BIM); Risk identification; Database; Dynamic; Project risk; WBS;
• ISBN/ISSN: 2046-6099
• URL: https://doi.org/10.1108/SASBE-09-2019-0123
• Abstract:
  Merging and updating project information and recording changes can give dynamic risk identification at all stages of the project. The main purpose of this research is to create an integration in construction information. In this research, the 5D model was prepared and then all model information was entered into the database designed in SQL Server, the project report tables were coded, and finally, a database with four groups of information was ready for risk identification. Creating an integrated risk identification platform reduced rework and time and cost control and change management, which were positive effects of risk identification at the right time. In order to identify risks, creating multilateral databases whose information integration enables timely completion of the project and compliance with the planning. This research is the basis for identifying project risks within the framework of building information modeling and can be an effective contribution to increasing the risk-taking efficiency of the project. As a matter of fact, marked time and cost are terrific motivating forces for the building industry, materializing with identify risk well-time. In any case, identifying risk engaged with all of the dimensions depend on this industry.

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.