A bayesian belief networks approach to risk control in construction projects

Ibsen Chivata Cardenas, Saad H.S. Al-Jibouri, Johannes I.M. Halman

Research output: Contribution to conference without publisher/journalPaperResearchpeer-review

Abstract

Although risk control is a key step in risk management of construction projects, very often risk measures used are based merely on personal experience and engineering judgement rather than analysis of comprehensive information relating to a specific risk. This paper deals with an approach to provide better information to derive relevant and effective risk measures for specific risks. The approach relies on developing risk models to represent interactions between risk factors and carrying out analysis to identify critical factors on which risk measures must focus. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert knowledge is elicited to augment available information. The paper describes proposed modifications to the standard methods used to develop Bayesian Belief Networks in order to deal with divergent information originated from epistemic uncertainty of risks. The capacity of the proposed approach to provide better information to support risk related decision making is verified by means of an illustrative application to risk factors involved in the construction of cross passages between tunnels tubes in soft soils.
Original languageEnglish
Publication date2012
Publication statusPublished - 2012
Externally publishedYes
Event14th International Conference on Computing in Civil and Building Engineering - Moscow, Russian Federation
Duration: 27. Jun 201229. Jun 2023

Conference

Conference14th International Conference on Computing in Civil and Building Engineering
Country/TerritoryRussian Federation
CityMoscow
Period27/06/201229/06/2023

Fingerprint

Dive into the research topics of 'A bayesian belief networks approach to risk control in construction projects'. Together they form a unique fingerprint.

Cite this