Evidence-Based Risk Management for Civil Engineering Projects Using Bayesian Belief Networks (BBN)

The authors are seeking new methods for improving the efficiency of the investments associated with the maintenance and operation of existing civil engineering structures. It is demonstrated how the knowledge about the elements of construction and operation phases and their relationships, combined with monitoring data can be used for more effective management of the risks associated with civil engineering projects. The methodology chosen for estimating the probabilities of risk events is known as Bayesian Belief Networks (BBNs). To better illustrate how the proposed approach works the authors use the example of multi-family residential building located in Gda?sk made in the wood-frame technology.

[1]  Krzysztof Wilde,et al.  Structural Health Monitoring System for Suspension Footbridge , 2017, 2017 Baltic Geodetic Congress (BGC Geomatics).

[2]  W. R. Shao,et al.  Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2008 .

[3]  Norman Fenton,et al.  Risk Assessment and Decision Analysis with Bayesian Networks , 2012 .

[4]  Jacek Zabielski,et al.  Technical Condition of Buildings Managed by the Agricultural Property Agency - Diagnosis and Assessment , 2017, 2017 Baltic Geodetic Congress (BGC Geomatics).

[5]  Dariusz Kowalski,et al.  The Cost Analysis of Corrosion Protection Solutions for Steel Components in Terms of the Object Life Cycle Cost , 2017 .

[6]  Edyta Plebankiewicz,et al.  Quantification of the Risk Addition in Life Cycle Cost of a Building Object , 2017 .

[7]  Kazimierz Jamroz,et al.  Methods of estimating the cost of traffic safety equipment’s life cycle , 2017 .

[8]  E. Plebankiewicz,et al.  Review of methods of determining the life cycle cost of buildings , 2015 .

[9]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[10]  M. W. Kembłowski,et al.  Ocena efektywności monitoringu obiektów inżynierskich za pomocą sieci Bayesa , 2016 .

[11]  Edyta Plebankiewicz,et al.  Life Cycle Cost Modelling of Buildings with Consideration of the Risk , 2016 .

[12]  Michał Krzemiński,et al.  Cost approach to the flow-shop construction scheduling , 2019 .