Dam risk analysis and dynamic decision making

The main objectives of this thesis are to develop a landslide dam database and a set of empirical models for the prediction of breaching parameters based on the database, establish a new model for the analysis of human risks from dam break floods, and develop a framework of risk-based dynamic decision making for dam break emergency management.;Landslide dams differ from embankment dams in geometry, soil compositions, and flood control facilities. The differences largely influence the failure modes of these two types of dams. A large database of landslide dams with 1267 cases from all over the world has been compiled in this study. Based on the database, a set of empirical models are developed for estimating five important breaching parameters (peak flow rate, breaching time, breach depth, breach top width, and breach bottom width) of landslide dams. The breaching parameters of landslide dams and man-made earth and rockfill dams are compared. Direct application of the empirical models for man-made earth and rockfill dams to landslide dams would, on average, overestimate the breach size by more than 60% and the peak outflow rate by approximately 200%, and underestimate the breaching duration by approximately 50%.;Analysis of human risks due to dam-break floods is very complex because both objective uncertainties and subjective uncertainties such as human thinking, decision, and behaviour are involved. A HUman Risk Analysis Model for dam-break floods (HURAM) is established in this thesis using Bayesian networks. The model is able to take into account a large number of important parameters (fourteen) and their inter-relationships in a systematic structure; include the uncertainties of these parameters and their inter-relationships; incorporate more available information of physical mechanisms and historical data; and update the predictions using Bayes' theorem based on available information in specific cases. HURAM allows not only cause-to-result inference, but also result-to-cause inference by updating the Bayesian network with specific information from the study case. A change in any parameter in the model may affect other parameters and the loss of life. The uncertainties of the parameters and their relationships are studied both at the global level using multiple sources of information and at the local level by updating the prior probabilities. Evacuation, sheltering, and building damage are also simulated in HURAM, which is required in estimating evacuation costs and flood damage.;Emergent evacuation of the population at risk (PAR) before a dam-break is an efficient way to save human life and properties. A late decision may lead to loss of lives and properties, but a very early evacuation will incur unnecessary expenses. A risk-based framework of dynamic decision making for dam-break emergency management (DYDEM) is developed in this study. A decision criterion is suggested to decide whether to evacuate the population at risk or to delay the decision. The optimum time for evacuating the PAR is obtained by minimizing the expected total loss, which integrates the time related probabilities and flood consequences. The time-related probability of dam failure is taken as a stochastic process and estimated using a time-series analysis method. The time-related flood consequences, including evacuation costs, flood damage and monetized loss of life, are taken as functions of warning time and evaluated using HURAM. When a delayed decision is chosen, the decision making can be updated with available new information. (Abstract shortened by UMI.)...