No abstract

Utilising emerging innovative technologies and systems to improve construction processes in an effort towards digitalisation has been earmarked as critical to delivering resilience and responsive infrastructure. However, successful implementation is hindered by several challenges. Hence, this study evaluates the challenges facing the adoption of unmanned aerial vehicles towards the digitalisation of the built environment. The study adopted a quantitative survey of built environment stakeholders in developed and developing economies. A total of 161 completely filled forms were received after the survey, and the data were analysed using descriptive analysis and inferential statistics. The study’s findings show that there are different barriers experienced between developed and developing countries in the adoption of drones towards digitalising construction processes in the built environment. Moreover, economic/cost-related factors were identified as the most critical barriers to the adoption of drones, followed by technical/regulatory factors and education/organisation-related factors. The findings can assist the built environment in reducing the impact of these barriers and could serve as a policy instrument and helpful guidelines for governmental organisations, stakeholders, and others.

The innovation of technology, particularly Artificial Intelligence (AI), has rapidly changed the world. It is currently at a nascent stage worldwide in the construction industry throughout the lifecycle of projects. However, construction organisations of developing countries such as South Africa are still lagging in recognising the need to adopt emerging digital innovations such as AI to improve the built sector’s performance. This study aims to identify organisational factors imperative to driving the adoption of AI in construction organisations. The study uses a quantitative survey approach to collect data through snowball sampling of industry experts on factors associated with AI adoption. With data from 169 respondents, exploratory factor analysis was adopted to identify critical organisational factors to ease AI adoption in the industry. Furthermore, confirmatory factor analysis was employed to demonstrate the relationship among the constructs. The study proposes 17 factors to drive organisational AI, categorised into four components; innovative organisational culture, competence-based development, collaborative decision-making, and strategic analysis. However, previous studies have identified organisational factors of AI in the construction and allied industries. This study presented the organisational factors of AI in the construction industry using EFA and CFA, a method not used in articles presented in the SLR identified. The use of CFA improves the measurement of the constructs. It thus enhances understanding of the underlying components of a construct and its relationship with AI in the construction industry.

Building Information Modelling (BIM) for life cycle sustainability assessment is an emerging development considered valuable given its importance in enhancing the environmentally friendly performance of buildings by delivering eco-efficient structures. However, despite its benefits, adoption is low. Thus, this study examines the key drivers of a building’s BIM-based life cycle sustainability assessment. An interpretive structural modelling approach and Matrice d’Impacts croises-multipication applique a classement (MICMAC) analysis were adopted for this study. Nineteen key drivers were categorized into a seven-level ISM model, which revealed that the successful implementation of the driving factors for BIM-based LCSA would increase its adoption and encourage users to be proactive in exploring solutions, exerting best efforts, and advancing its usage. The primary drivers, such as organizational readiness, personal willingness to use, procurement methods, and organizational structure, amongst others, are crucial for discussing BIM-based LCSA adoption strategies and making guidelines and design decisions to guide the process. This paper therefore contributes to the growing discussion on BIM from the viewpoint of an assessment of a building’s life cycle sustainability. The study concludes that organizational, governmental, and institutional support, as well as capacity development, are essential to driving BIM-Based LCSA.

The construction industry has seen an increase in Artificial Intelligence(AI) in recent years, a paradigm shift in many industries. It puts under pressure for technological advancement. Therefore, AI is under great attention in the construction industry as a new strategic paver. This paper adopts a systematic literature review (SLR) approach and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to observe and understand the influencing factors and strategies for AI adoption. The SLR for AI-related research carried out between 2010 and 2020. Data was collected from ASCE Journals, Emerald Insight, Elsevier ScienceDirect, Engineering Village, Google Scholar, ICE virtual library, IOPscience, IEEE Xplore, ScienceDirect, Scopus, SpringerLink and Taylor & Francis. The paper identifies and classifies the new developments in AI research, making its implementation and adoption a reality in the construction industry. This review has the potential for construction industry stakeholders, especially those in developing countries, to utilise the accumulated evidence from selected systematic reviews to enable the usage of AI for infrastructure development