Consequence-based framework for buried infrastructure systems: A Bayesian belief network model. (December 2018)

• Record Type: Journal Article
• Authors: Kabir, Golam; Balek, Ngandu Balekelayi Celestin; Tesfamariam, Solomon
• Is Part Of: Reliability engineering & system safety. Volume 180(2018)
• Page Start: 290
• Page End: 301
• Publication Date: 2018-12
• Subjects: Buried infrastructure -- Consequence assessment -- Bayesian belief network (BBN) -- Uncertainty -- Conditional probabilities -- Risk analysis; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.07.037
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Bayesian belief network (BBN) based buried infrastructure consequence model. Assess the consequence index and to prioritize the buried infrastructures for maintenance/ rehabilitation/ replacement. Estimate the health & safety impact, environmental impact, social impact, and economical & organizational impact due to failure. Quantify uncertainties and handle the nonlinear and sophisticated relationships between several factors. Applied to assess the consequence of the City of Vernon, BC sewer network system. Abstract: The failure of municipal buried infrastructures (potable water supply, wastewater systems, and stormwater systems) may cause crucial consequences to the environment, society, health, and economy. The buried infrastructure management has transformed from reactive to the preventive action plan. In this study, a Bayesian belief network (BBN) based buried infrastructure consequence model is developed to assess the consequence index and to prioritize the buried infrastructures for maintenance/ rehabilitation/ replacement. The causal relationships between different parameters are constructed based on published literature and expert knowledge. The proposed model can provide information at pipe level by estimating the health & safety impact, environmental impact, social impact, and economical & organizational impact due to failure. The proposed model is also capable of highlighting the most sensitive and vulnerable pipes within the network. The applicability of the proposed model is demonstrated on the wastewater collection network of the City of Vernon, BC. Results indicate that proposed BBN-based consequence model can explicitly quantify uncertainties and handle the nonlinear and sophisticated relationships between several factors.