Role of knowledge in managing construction change

Purpose-Unplanned changes in construction projects are common and lead to disruptive effects such as project delays, cost overruns and quality deviations. Rework due to unplanned changes can cost 10-15 per cent of contract value. By managing these changes more effectively, these disruptive effects can be minimised. Previous research has approached this problem from an information-processing view. In this knowledge age, the purpose of this paper is to argue that effective change management can be brought about by better understanding the significant role of knowledge during change situations. Design/methodology/approach - Within this knowledge-based context, the question of how construction project teams manage knowledge during unplanned change in the construction phase within collaborative team settings is investigated through a selected case study sample within the UK construction industry. Findings- Case study findings conclude that different forms of knowledge are created and shared between project team members during change events which is very much socially constructed and centred on tacit knowledge and experience of project personnel. Originality/value- Building on the case study findings the paper finally offers a model that represents the role of knowledge during managing project change.

Reliability in the whole life cycle of building systems

Purpose – The purpose of this research is to show that reliability analysis and its implementation will lead to an improved whole life performance of the building systems, and hence their life cycle costs (LCC). Design/methodology/approach – This paper analyses reliability impacts on the whole life cycle of building systems, and reviews the up-to-date approaches adopted in UK construction, based on questionnaires designed to investigate the use of reliability within the industry. Findings – Approaches to reliability design and maintainability design have been introduced from the operating environment level, system structural level and component level, and a scheduled maintenance logic tree is modified based on the model developed by Pride. Different stages of the whole life cycle of building services systems, reliability-associated factors should be considered to ensure the system's whole life performance. It is suggested that data analysis should be applied in reliability design, maintainability design, and maintenance policy development. Originality/value – The paper presents important factors in different stages of the whole life cycle of the systems, and reliability and maintainability design approaches which can be helpful for building services system designers. The survey from the questionnaires provides the designers with understanding of key impacting factors.

High value information in engineering organisations

The management of information in engineering organisations is facing a particular challenge in the ever-increasing volume of information. It has been recognised that an effective methodology is required to evaluate information in order to avoid information overload and to retain the right information for reuse. By using, as a starting point, a number of the current tools and techniques which attempt to obtain ‘the value’ of information, it is proposed that an assessment or filter mechanism for information is needed to be developed. This paper addresses this issue firstly by briefly reviewing the information overload problem, the definition of value, and related research work on the value of information in various areas. Then a “characteristic” based framework of information evaluation is introduced using the key characteristics identified from related work as an example. A Bayesian Network diagram method is introduced to the framework to build the linkage between the characteristics and information value in order to quantitatively calculate the quality and value of information. The training and verification process for the model is then described using 60 real engineering documents as a sample. The model gives a reasonable accurate result and the differences between the model calculation and training judgements are summarised as the potential causes are discussed. Finally, several further issues including the challenge of the framework and the implementations of this evaluation assessment method are raised.

An engineering model of the human colon

The development and performance of a three-stage tubular model of the large human intestine is outlined. Each stage comprises a membrane fermenter where flow of an aqueous polyethylene glycol solution on the outside of the tubular membrane is used to control the removal of water and metabolites (principally short chain fatty acids) from, and thus the pH of, the flowing contents on the fermenter side. The three stage system gave a fair representation of conditions in the human gut. Numbers of the main bacterial groups were consistently higher than in an existing three-chemostat gut model system, suggesting the advantages of the new design in providing an environment for bacterial growth to represent the actual colonic microflora. Concentrations of short chain fatty acids and Ph levels throughout the system were similar to those associated with corresponding sections of the human colon. The model was able to achieve considerable water transfer across the membrane, although the values were not as high as those in the colon. The model thus goes some way towards a realistic simulation of the colon, although it makes no pretence to simulate the pulsating nature of the real flow. The flow conditions in each section are characterized by low Reynolds numbers: mixing due to Taylor dispersion is significant, and the implications of Taylor mixing and biofilm development for the stability, that is the ability to operate without washout, of the system are briefly analysed and discussed. It is concluded that both phenomena are important for stabilizing the model and the human colon.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.