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…
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.
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…
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.