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Dams' safety is highly important for authorities around the world. The impacts of a dam failure can be enormous. Models for investigating dam safety are required for helping decision-makers to mitigate the possible adverse consequences of flooding. A model for earth dam safety must specify clearly possible contributing factors, failure modes and potential consequences of dam failure. Probabilistic relations between variables should also be specified. Bayesian networks (BNs) have been identified as tools that would assist dam engineers on assessing risks. BNs are graphical models that facilitate the construction of a joint probability distribution. Most of the time, the variables included in a model for earth dam risk assessment involve continuous quantities. The presence of continuous random variables makes the implementation of discrete BNs difficult. An alternative to discrete BNs is the use of non-parametric continuous BNs, which will be briefly described in this article. As an example, a model for earth dams' safety in the State of Mexico will be discussed. Results regarding the quantification of conditional rank correlations through ratios of unconditional rank correlations have not been presented before and are introduced herein. While the complete application of the model for the State of Mexico is presented in an accompanying paper, here some results regarding model use are shown for demonstration purposes. The methods presented in this article can be applied for investigating risks of failure of civil infrastructures other than earth dams. [ABSTRACT FROM AUTHOR]

Dams are civil engineering structures to hinder water flows. Criteria such as purpose, size and construction material are useful to categorise them. The latter is used to classify ‘earth dams’, which tend to have higher risk levels than other types. The failure of a dam leads to significant economic loss and usually to catastrophic impacts. In an effort to comprehensively examine the variables that influence earth dam breaks and describe their interactions, a model has been developed in such a way that it allows to assess risks and to prioritise the allocation of resources for maintenance activities. The research was carried out by systematically reviewing the literature, which led to the choice of Bayesian Networks (BNs) as a tool for assessing risks. Using data from seven case studies in Mexico, a model was built, which helped to rank the dams under study, leading to results comparable with those reported in the literature. While the particular type of BN used and its quantification is presented more extensively in an accompanying paper, the model may be of interest for dam owners, managers, practitioners and academics on their efforts to manage earth dams’ risks. [ABSTRACT FROM AUTHOR]