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.

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.