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.

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Quality tests applied to hydraulic concrete such as compressive, tension, and bending strength are used to guarantee proper characteristics of materials. All these assessments are performed by destructive tests (DTs). The trend is to carry out quality analysis using nondestructive tests (NDTs) as has been widely used for decades. This paper proposes a framework for predicting concrete compressive strength and modulus of rupture by combining data from four NDTs: electrical resistivity, ultrasonic pulse velocity, resonant frequency, and hammer test rebound with DTs data. The model, determined from the multiple linear regression technique, produces accurate indicators predictions and categorizes the importance of each NDT estimate. However, the model is identified from all the possible linear combinations of the available NDT, and it was selected using a cross-validation technique. Furthermore, the generality of the model was assessed by comparing results from additional specimens fabricated afterwards.