Risk-Averse Proactive Seismic Rehabilitation Decision-Making for Water Distribution Systems
Publication: Pipelines 2022
ABSTRACT
Earthquakes could have enormous destructive impacts on water distribution networks. Utility managers are challenged to make proactive rehabilitation decisions under seismic and network uncertainties. These utility managers have different risk appetites. However, existing seismic rehabilitation decision-making models of water distribution networks do not consider decision-makers’ attitudes toward risk making existing models practically limited. The objective of this research is to formulate a risk-averse stochastic combinatorial optimization model to identify the critical pipes of a water distribution network for proactive seismic rehabilitation with controllable risk aversion levels. The functionality of the water distribution system is quantified by the post-earthquake serviceability index, the expected value of which is maximized by the objective function. A value-at-risk (VaR) constraint is used to control risk levels. This methodology includes four steps: seismic repair rate calculations, integrated multi-physics modeling, Monte Carlo simulation, and risk-averse stochastic combinatorial optimization. The repair rate of each pipe subjected to seismic loads was calculated using empirical fragility curves. These curves were generated based on the locations of the pipes, soil corrosivity in different locations, pipe diameters, pipe materials, and pipe joint properties. Network’s hydraulic behavior and seismic vulnerability assessment were simulated using an integrated multi-physics model. Monte Carlo simulations were performed to consider the probabilistic nature of damages to the water distribution systems. These damages were represented by leaks as well as breaks in the individual pipes. The model used to ascertain the susceptibility of the water distribution system to earthquake hazard was fused with a stochastic formulation of combinatorial optimization to maximize the serviceability index of the distribution system while minimizing risk. The solution to the optimization problem of detecting the critical pipes for a given resource constraint was obtained through a risk-averse simulated annealing approach. The approach was implemented on a widely used benchmark network to detect the critical pipelines of that water network. The introduction of risk-averse stochastic combinatorial optimization models equips decision makers with a proper model to make rehabilitation decisions at a controllable risk aversion level.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abrahamson, N. A., and Silva, W. J. (2007). Abrahamson-Silva NGA ground-motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. Berkeley, CA: Pacific Earthquake Engineering Research Center, Univ. of California.
Adachi, T. (2007). Impact of cascading failures on performance assessment of civil infrastructure systems (Doctoral dissertation, Georgia Institute of Technology).
ALA (American Lifelines Alliance). (2001). Seismic fragility formulations for water systems. Washington, DC: ALA.
Artzner, P., Delbaen, F., Eber, J., and Heath, D. (1999). “Coherent measures of risk.” Mathematical Finance 9, 203–228.
Center of Water Systems. (2018). “Large problems.” Accessed September 1, 2021.
Duffie, D., and Pan, J. (1997). An overview of value at risk. Journal of derivatives, 4(3), 7–49.
Fragiadakis, M., and Christodoulou, S. E. (2014). Seismic reliability assessment of urban water networks. Earthquake engineering & structural dynamics, 43(3), 357–374.
Frangopol, D. M., and Liu, M. (2007). “Maintenance and management of civil infrastructure based on condition, safety, optimization, and lifecycle cost.” Struct. Infrastruct. Eng. 3 (1): 29–41.
Guo, E. D., Wang, X. J., Zhang, L. N, et al. (2008). Technical Report of the Scientific Investigation Team on Water Supply System in the Wenchuan Earthquake, Harbin: Institute of Engineering Mechanics, China Earthquake Administration. (in Chinese).
Hwang, H. H., Lin, H., and Shinozuka, M. (1998). Seismic performance assessment of water delivery systems. Journal of Infrastructure Systems, 4(3), 118–125.
Jiménez-Rodríguez, E., Feria-Domínguez, J. M., and Sebastián-Lacave, A. (2018). AssePSIng the health-care risk: The Clinical-VaR, a key indicator for sound management. International journal of environmental research and public health, 15(4), 639.
Jorion, P. (1996). Risk2: Measuring the risk in value at risk. Financial analysts journal, 52(6), 47–56.
Jorion, P. (2000). Value at risk.
Kang, Y., Batta, R., and Kwon, C. (2014). Value-at-risk model for hazardous material transportation. Annals of Operations Research, 222(1), 361–387.
Kirkpatrick, S., Gelatt, C. D., Jr., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671–680.
Linsmeier, T. J., and Pearson, N. D. (1996). Risk measurement: An introduction to value at risk (No. 1629-2016-134959).
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). “Equation of state calculations by fast computing machines.” J. Chem. Phys. 21 (6): 1087–1092.
O’Rourke, T. D. (1996). “Lessons learned for lifeline engineering from major urban earthquakes.” In Proc., 11th World Conf. of Earthquake Engineering. Tokyo: International Association for Earthquake Engineering.
O’Rourke, T. D., Jeon, S. S., Toprak, S., Cubrinovski, M., Hughes, M., Van Ballegooy, S., and D. Bouziou. (2014). “Earthquake response of underground pipeline networks in Christchurch, NZ.” Earthquake Spectra 30 (1): 183–204.
Pudasaini, B., Shahandashti, S. M., and Razavi, M. (2017). Identifying critical links in water supply systems subject to various earthquakes to support inspection and renewal decision making. In Computing in civil engineering 2017 (pp. 231–238).
Pudasaini, B., and Shahandashti, S. M. (2018). “Identification of critical pipes for proactive resource‐constrained seismic rehabilitation of water pipe networks.” Journal of Infrastructure Systems, 24(4), 04018024.
Pudasaini, B., and Shahandashti, S. M. (2020). Seismic Resilience Enhancement of Water Pipe Networks Using Hybrid Metaheuristic Optimization. In Pipelines 2020 (pp. 428–436). Reston, VA: American Society of Civil Engineers.
Pudasaini, B., and Shahandashti, M. (2020). Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks. Computer‐Aided Civil and Infrastructure Engineering, 35(10), 1101–1114.
Pudasaini, B., and Shahandashti, M. (2021). Seismic Rehabilitation Optimization of Water Pipe Networks Considering Spatial Variabilities of Demand Criticalities and Seismic Ground Motion Intensities. Journal of Infrastructure Systems, 27(4), 04021028.
Roy, A., Pudasaini, B., and Shahandashti, M. (2021). Seismic Vulnerability Assessment of Water Pipe Networks under Network Uncertainties. In Pipelines 2021 (pp. 171–179).
Shahandashti, S. M., and Pudasaini, B. (2019). “Proactive seismic rehabilitation decision‐making for water pipe networks using simulated annealing.” Natural Hazards Review, 20(2), 04019003.
Shi, P. (2006). Seismic response modeling of water supply systems. Ithaca, NY: Cornell University.
Toumazis, I., and Kwon, C. (2013). Routing hazardous materials on time-dependent networks using conditional value-at-risk. Transportation Research Part C: Emerging Technologies, 37, 73–92.
Trautman, C. H., Khater, M. M., O’Rourke, T. D., and Grigoriu, M. D. (2013). Modeling water supply systems for earthquake response analysis. Structures and stochastic methods, 215.
USGS. (2018). “Unified hazard tool.” Earthquake Hazards Program. Accessed September 10, 2021.
Wang, Y., Au, S.‐K., and Fu, Q. (2010). “Seismic risk assessment and mitigation of water supply systems.” Earthquake Spectra, 26(1), 257– 274
Wang, H., and Chen, X. (2015). Optimization of Maintenance Planning for Water Distribution Networks under Random Failures. Journal of Water Resources Planning and Management, 142(2), 04015063.
Yazdi, J., Lee, E. H., and Kim, J. H. (2014). Stochastic multiobjective optimization model for urban drainage network rehabilitation. Journal of Water Resources Planning and Management, 141(8), 04014091.
Information & Authors
Information
Published In
Pipelines 2022
Pages: 81 - 90
History
Published online: Jul 28, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Abhijit Roy, Sushil Bhatta, Mohsen Shahandashti, Proactive Seismic Rehabilitation Decision-Making for Water Pipe Networks Considering Earthquake-Induced Transient Strains and Geotechnical Instability, ASCE Inspire 2023, 10.1061/9780784485163.112, (979-988), (2023).
- Sumaya Sharveen, Mohsen Shahandashti, Optimal Proactive Seismic Rehabilitation of Gas Pipeline Networks, Pipelines 2023, 10.1061/9780784485033.026, (240-250), (2023).
- Abhijit Roy, Jay M. Rosenberger, Mohsen Shahandashti, Impact of Water Network Uncertainties on Seismic Rehabilitation Decision-Making for Water Pipelines, Pipelines 2023, 10.1061/9780784485019.039, (364-372), (2023).
- Anil Baral, Pooya Darghiasi, Mohsen Shahandashti, Risk-Averse Maintenance Decision Framework for Roadside Slopes along Highway Corridors, International Conference on Transportation and Development 2023, 10.1061/9780784484883.034, (383-392), (2023).