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In a circular economy, the goal is to keep materials values in the economy for as long as possible. For the construction industry to support the goal of the circular economy, there is the need for materials reuse. However, there is little or no information about the amount and quality of reusable materials obtainable when buildings are deconstructed. The purpose of this paper, therefore, is to develop a reusability analytics tool for assessing end-of-life status of building materials.

A review of the extant literature was carried out to identify the best approach to modelling end-of-life reusability assessment tool. The reliability analysis principle and materials properties were used to develop the predictive mathematical model for assessing building materials performance. The model was tested using the case study of a building design and materials take-off quantities as specified in the bill of quantity of the building design.

The results of analytics show that the quality of the building materials varies with the building component. For example, from the case study, at the 80th year of the building, the qualities of the obtainable concrete from the building are 0.9865, 0.9835, 0.9728 and 0.9799, respectively, from the foundation, first floor, frame and stair components of the building.

As a contribution to the concept of circular economy in the built environment, the tool provides a foundation for estimating the quality of obtainable building materials at the end-of-life based on the life expectancy of the building materials.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

This paper aims to explore the intellectual structure shaping the circular economy (CE) discourse within the built environment in Africa.

The study adopted a bibliometric analysis approach to explore the intellectual structure of CE in the built environment in Africa. The authors collected 31 papers published between 2005 and 2021 from the Scopus database and used VOSviewer for data analysis.

The findings show that there are six clusters shaping the intellectual structure: demolition, material recovery and reuse; waste as a resource; cellulose and agro-based materials; resilience and low-carbon footprint; recycling materials; and the fourth industrial revolution. The two most cited scholars had three publications each, while the top journal was Resources, Conservation and Recycling. The dominant concepts included CE, sustainability, alternative materials, waste management, lifecycle, demolition and climate change. The study concludes that there is low CE research output in Africa, which implies that the concept is either novel or facing resistance.

The data were drawn from one database, Scopus; hence, adoption of alternative databases such as Web of Science, Google Scholar and Dimensions could potentially have yielded a higher number of articles for analysis which potentially would result in different conclusions on the subject understudy.

This study made a significant contribution by articulating the CE intellectual structure in the built environment, identified prominent scholars and academic platforms responsible for promoting circularity in Africa.

Automation facilitates production activities within offsite construction (OSC) projects through computer-controlled and mechanised systems that can be programmed to deliver various products in a self-regulating sequence. Despite known benefits of automation to offsite production, the level of automation adoption in New Zealand is low. This study is an effort to understand the current status of automation within the New Zealand construction industry and to identify the barriers and enablers to its uptake.

This study utilises the qualitative approach of semi-structured interviews (open-ended questions). Using a referral sampling strategy (snowballing), fifteen New Zealand industry experts were interviewed, and the data collected were analysed using qualitative content analysis.

The study found that there is a weak business case for full automation. Four main categories of barriers to the uptake of automated OSC were identified, including requirement of high capital cost, lack of education about automation and OSC and non-existence of regulations to support OSC. It was noted that financial supports to the OSC sub-sector in form of subsidies, tax waivers, and enhanced leasing model could enhance the uptake of automation. Further to this more awareness about OSC's automation and regulations suitable for OSC could enhance the confidence of business owners to invest in this area.

Originality of this paper stems from the fact that, not much attention has been paid to investigating the uptake of automation for OSC sub-sector of construction industry in New Zealand context.