"Water distribution systems are important lifelines and a critical and complex infrastructure of a country. The performance of this system during unexpected rare events is important as it is one of the lifelines that people directly depend on and other factors indirectly impact the economy of a nation. In this thesis a couple of methods that can be used to predict damage and simulate the restoration process of a water distribution system are presented. Contributing to the effort of applying computational tools to infrastructure systems, Artificial Neural Network (ANN) is used to predict the rate of damage in the pipe network during seismic events. Prediction done in this thesis is based on earthquake intensity, peak ground velocity, and pipe size and material type. Further, restoration process of water distribution network in a seismic event is modeled and restoration curves are simulated using colored Petri nets. This dynamic simulation will aid decision makers to adopt the best strategies during disaster management. Prediction of damages, modeling and simulation in conjunction with other disaster reduction methodologies and strategies is expected to be helpful to be more resilient and better prepared for disasters"--Abstract, page iv.
Sarangapani, Jagannathan, 1965-
Dagli, Cihan H., 1949-
Engineering Management and Systems Engineering
M.S. in Systems Engineering
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Prediction of damage/repair rates in water distribution systems using artificial neural network
- Post earthquake recovery of water systems: discrete event simulation using colored petri nets
x, 81 pages
© 2008 Nandini Kavanal Balakrishnan, All rights reserved.
Thesis - Open Access
Water-supply -- Earthquake effects -- Mathematical models
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Balakrishnan, Nandini Kavanal, "Application of artificial neural networks and colored petri nets on earthquake resilient water distribution systems" (2008). Masters Theses. 4639.