Using belief networks to assess risk

McCabe, Brenda; Ford, D.

doi:10.1109/wsc.2001.977483

Cited by 2 publications

(1 citation statement)

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“…BBN represents the causal probability of variables by using nodes (representing variables or events or states) and arrows (depicting the causality chains). According to McCabe and Ford [12], BBN has many advantages for analytical purpose. They include: (a) easy data fusion -the possibility to combine various data sources, including: hard, historical data and subjective expert judgment; (b) intuitively appealing -users or analysts with limited background on advance probabilistic could use the model intuitively.…”

Section: Introductionmentioning

confidence: 99%

Bayesian-based Project Monitoring: Framework Development and Model Testing

Hartono

Ayuningtyas

Mulyani

2016

jti

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During project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. The objective of this study is to develop a general framework that addresses such a concern. The developed framework consists of three interrelated major building blocks, namely: Risk Register (RR), Bayesian Network (BN), and Project Time Networks (PTN) for dynamic project monitoring. RR is used to list and to categorize identified project risks. PTN is utilized for modeling the relationship between project activities. BN is used to reflect the interdependence among risk factors and to bridge RR and PTN. A residential development project is chosen as a working example and the result shows that the proposed framework has been successfully applied. The specific model of the development project is also successfully developed and is used to monitor the project progress. It is shown in this study that the proposed BN-based model provides superior performance in terms of forecast accuracy compared to the extant models.

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Section: Introductionmentioning

confidence: 99%