یک رویکرد فازی برای پروژه ساخت و ساز ارزیابی ریسک و تجزیه و تحلیل: پروژه ساخت و ساز سیستم مدیریت ریسک

The construction industry is plagued by risk, and poor performance has often been the result. Although risk management techniques have been applied, the lack of a formalised approach has produced inconsistent results. In this paper, a hierarchical risk breakdown structure is described to represent a formal model for qualitative risk assessment. The relationships between risk factors, risks, and their consequences are represented on case and effect diagrams. Risk descriptions and their consequences can be defined using descriptive linguistic variables. Using fuzzy approximation and composition, the relationships between risk sources and the consequences on project performance measures can be identified and quantified consistently.

The construction industry, perhaps more than others, has been plagued by risk [1] and [2] and this has not always been dealt with adequately, often resulting in poor performance with increasing costs and time delays. With the need for improved performance in the construction industry [3] and increasing contractual obligations [4], the requirement of an effective risk management approach has never been more necessary. There are a proliferation of risk management software tools on the market, and while the facilities offered by these packages vary widely, their underlying methodologies are often founded on the principles and techniques developed for operational research in the 1950s and 60s. Hence, the use of statistical techniques is commonplace and little else is considered. Construction projects are becoming increasingly complex and dynamic in their nature, and the introduction of new procurement methods means that contractors have to rethink their approach to the way risks are treated within their projects and organisations.

A common language for describing risks and remedial actions, grounded in a taxonomy of risks and actions based on a HRBS, has been described. The language uses a number of taxons and constructs to define generic risk and action terms which can then be stored in generic catalogues. Project specific risk information is based on the items contained within these catalogues, but it is customised for each project. However, the heritage of these items is not hidden, enabling a comparison to be made between similar risks on different projects. The language allows for consistent quantification of risk likelihood and impact values. The relationships between risk factors, risks, and their consequences have been developed and demonstrated on cause and effect diagrams. Additionally, fuzzy association and composition have been applied to identify relationships between risk sources and their consequences on project performance measures. The use of fuzzy logic allows for the use of descriptive linguistic variables for the description of risks and their consequences, whilst allowing consistent quantification throughout.