Stochastic simulation methodology for resilience assessment of water distribution networks

Water distribution systems enable social and economic development and sustain people quality of life. However, these systems face significant performance challenges including ageing, natural disruptive events, and man-made disruptions. Physical protection of networked infrastructure distributed over large geographical areas is unfeasible. A cost-effective alternative is to enhance the water distribution system resilience: ensuring reduced system damage in case of disruption, and enhancing system capacity to recover lost performance within acceptable time and cost limits. This paper presents a novel network-based methodology to evaluate resilience of water distribution systems. This methodology utilises stochastic simulation on a network model to generate statistical data on the resilience probability of the actual water infrastructure system. The methodology is a management decision support tool for enhancing system preparedness, enabling acceptable recovery parameters, and improving the evaluation of capital investment alternatives. The network-based approach can be extended to complex networks integrating physical and non-physical assets.

[1]  Consolación Gil,et al.  Resilience Indexes for Water Distribution Network Design: A Performance Analysis Under Demand Uncertainty , 2011 .

[2]  Michel Bruneau,et al.  Framework for analytical quantification of disaster resilience , 2010 .

[3]  Larry W. Mays.  Water Supply Systems Security , 2004 .

[4]  Behnido Y. Calida,et al.  Regional industries as critical infrastructures: a tale of two modern cities , 2012, Int. J. Crit. Infrastructures.

[5]  L. Dueñas-Osorio,et al.  Probabilistic Response of Interdependent Infrastructure Networks , 2004 .

[6]  Ezio Todini,et al.  Looped water distribution networks design using a resilience index based heuristic approach , 2000 .

[7]  Barry J. Goodno,et al.  Interdependent Response of Networked Systems , 2007 .

[8]  Agner Fog,et al.  Sampling Methods for Wallenius' and Fisher's Noncentral Hypergeometric Distributions , 2008, Commun. Stat. Simul. Comput..

[9]  Nii O. Attoh-Okine,et al.  Formulation of Resilience Index of Urban Infrastructure Using Belief Functions , 2009, IEEE Systems Journal.

[10]  Feniosky Peña-Mora,et al.  System Dynamics Modeling of a Safety Culture Based on Resilience Engineering , 2010 .

[11]  Michel Bruneau,et al.  A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities , 2003 .

[12]  Agner Fog,et al.  Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution , 2008, Commun. Stat. Simul. Comput..

[13]  Thomas D. O'Rourke,et al.  Los Angeles Water Supply Response to 7.8 M w Earthquake , 2009 .

[14]  Wilson H. Tang,et al.  Probability concepts in engineering planning and design , 1984 .

[15]  J. Chesson.  A non-central multivariate hypergeometric distribution arising from biased sampling with application to selective predation , 1976 .

[16]  Stephanie E. Chang,et al.  Linking Infrastructure and Urban Economy: Simulation of Water-Disruption Impacts in Earthquakes , 2002 .

[17]  Dan Rosbjerg,et al.  Choice of reliability, resilience and vulnerability estimators for risk assessments of water resources systems / Choix d’estimateurs de fiabilité, de résilience et de vulnérabilité pour les analyses de risque de systèmes de ressources en eau , 2004 .