Design and construction for traditional house building

This study is concerned with quality and productivity aspects of traditional house building. The research focuses on these issues by concentrating on the services and finishing stages of the building process. These are work stages which have not been fully investigated in previous productivity related studies. The primary objective of the research is to promote an integrated design and construction led approach to traditional house building based on an original concept of 'development cycles'. This process involves the following: site monitoring; the analysis of work operations; implementing design and construction changes founded on unique information collected during site monitoring; and subsequent re-monitoring to measure and assess Ihe effect of change. A volume house building firm has been involved in this applied research and has allowed access to its sites for production monitoring purposes. The firm also assisted in design detailing for a small group of 'experimental' production houses where various design and construction changes were implemented. Results from the collaborative research have shown certain quality and productivity improvements to be possible using this approach, albeit on a limited scale at this early experimental stage. The improvements have been possible because an improved activity sampling technique, developed for, and employed by the study, has been able to describe why many quality and productivity related problems occur during site building work. Experience derived from the research has shown the following attributes to be important: positive attitudes towards innovation; effective communication; careful planning and organisation; and good coordination and control at site level. These are all essential aspects of quality led management and determine to a large extent the overall success of this approach. Future work recommendations must include a more widespread use of innovative practices so that further design and construction modifications can be made. By doing this, productivity can be improved, cost savings made and better quality afforded.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

Building up resilience of construction sector SMEs and their supply chains to extreme weather events

Wider scientific community now accept that the threat of climate change as real and thus acknowledge the importance of implementing adaptation measures in a global context. In the UK , the physical effects of climate change are likely to be directly felt in the form of extreme weather events, which are predicted to escalate in number and severity in future under the changing climatic conditions. Construction industry; which consists of supply chains running across various other industries, economies and regions, will also be affected due to these events. Thus, it is important that the construction organisations are well prepared to withstand the effects of extreme weather events not only directly affecting their organisations but also affecting their supply chains which in turn might affect the organisation concerned. Given the fact that more than 99% of construction sector businesses are SMEs, the area can benefit significantly from policy making to improve SME resilience and coping capacity. This paper presents the literature review and synthesis of a doctoral research study undertaken to address the issue of extreme weather resilience of construction sector SMEs and their supply chains. The main contribution of the paper to both academia and practitioners is a synthesis model that conceptualises the factors that enhances resilience of SMEs and their supply chains against extreme weather events. This synthesis model forms the basis of a decision making framework that will enable SMEs to both reduce their vulnerability and enhance their coping capacity against extreme weather. The value of this paper is further extended by the overall research design that is set forth as the way forward.

A new stylometric technique for the invesigation of the true authorship of the early works of Shakespeare

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