Material Waste in the Northern Ireland Construction Industry: On-Site Management Causes and Methods of Prevention

The construction industry in Northern Ireland is one of the major contributors of construction waste to landfill each year. The aim of this research paper is to identify the core on-site management causes of material waste on construction sites in Northern Ireland and to illustrate various methods of prevention which can be adopted. The research begins with a detailed literature review and is complemented with the conduction of semi-structured interviews with 6 professionals who are experienced and active within the Northern Ireland construction industry. Following on from the literature review and interviews analysis, a questionnaire survey is developed to obtain further information in relation to the subject area. The questionnaire is based on the key findings of the previous stages to direct the research towards the most influential factors. The analysis of the survey responses reveals that the core causes of waste generation include a rushed program, poor handling and on-site damage of materials, while the principal methods of prevention emerge as the adequate storage, the reuse of material on-site and efficient material ordering. Furthermore, the role of the professional background in the shaping of perceptions relevant to waste management is also investigated and significant differences are identified. The findings of this research are beneficial for the industry as they enhance the understanding of construction waste generation causes and highlight the practices required to reduce waste on-site in the context of sustainable development.

Efficiently Scheduling Task Dataflow Parallelism: A Comparison Between Swan and QUARK

Increased system variability and irregularity of parallelism in applications put increasing demands on the ef- ficiency of dynamic task schedulers. This paper presents a new design for a work-stealing scheduler supporting both Cilk- style recursively parallel code and parallelism deduced from dataflow dependences. Initial evaluation on a set of linear algebra kernels demonstrates that our scheduler outperforms PLASMA’s QUARK scheduler by up to 12% on a 16-thread Intel Xeon and by up to 50% on a 32-thread AMD Bulldozer.