Hafiz Alaka

TL;DR: This paper presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry and discusses the future potential of such technologies across the multiple domain-specific sub-areas of theConstruction industry.

TL;DR: It was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria, and an overall better performance model can only be found by informed integration of tools to form a hybrid model.

TL;DR: In this article, the authors developed a Building Information Modelling based Deconstructability Assessment Score (BIM-DAS) for determining the extent to which a building could be deconstructed right from the design stage.

TL;DR: In this article, the authors explored factors impeding the effectiveness of existing waste management strategies, as well as strategies for reducing waste intensiveness of the construction industry, and suggested that six factors are requisites.

TL;DR: In this paper, the authors assess the expectations of stakeholders on how BIM could be employed for construction and demolition waste (CDW) management, and identify five major groups of BIM expectations for CDW management, which are: (i) BIM-based collaboration for waste management, (ii) waste-driven design process and solutions, (iii) waste analysis throughout building lifecycle, innovative technologies for waste intelligence and analytics, and (v) improved documentation.