The objective of traditional approaches to project risk management is to identify risks that can lead to project failure and to implement effective strategies to manage them. Information on the relevant causes and conditions in which failures arise is usually required as necessary input for determining possible risk strategies. Performing these tasks for underground construction projects, however, is not straightforward in practice. Explicit and integrated knowledge about the relevant causes and conditions that lead to major failures is often absent. This research considers these issues and addresses the extent to which integrated risk-related knowledge, in the form of probabilistic causal models, can be used to provide useful risk information in real projects. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, the causal models can be applied and provide guidance as to the use of specific remedial measures on a case-by-case basis. The models’ limitations are described in this thesis.
|Award date||16 Nov 2012|
|Place of Publication||Enschede|
|Publication status||Published - 16 Nov 2012|