The protection of critical engineering infrastructures is vital to today's society, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This control is based on the measurement of physical quantities that characterize the structural behavior, such as displacements, strains and stresses. The analysis of monitoring data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a wellknown approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recurrent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal.

Background Drowning is the third leading cause of death worldwide (WHO, 2010). According to Brenner et al (2009) participating in swimming lessons (SL) can reduce by 88% the risk of drowning in children 1–4 years old, despite some controversy regarding the most appropriate swimming programs (Ward, 2009). On the other hand no studies tested the skills acquired in SL in drowning prevention. So we proposed a test to evaluate the survivability of a child that accidentally falls into the water. Methods Twenty two children (4.68±0.82yrs) who practice SL (2.01±0.91yrs/practice) were subjected to two tests: aquatic readiness (AR) (Stallman et al, 2008); and survival test (ST), which consisted of an inflatable boat ride (unknown pool; presence of the usual swim teacher; wearing t-shirt) which 'accidentally' turns. Recorded images were later observed by 6 field experts whose observations obtained an overall index of concordance (IC) of 89%. Results In ST 15 children had positive responses (n=10, 100% IC; n=1, 66% IC; n=1, 83% IC; n=3, 50% IC) and 7 (n=6, 100% IC; n=1, 66% IC) would be in danger. Conclusions Through this test, we found that not all children whose result was positive in AR had a positive performance in the ST. This brings us to observe according to Langendörfer (2011) that the positive or negative response of the subject depends on the interaction of various constraints such as individual characteristics, the environment conditions, and the state in which the swimmer is. The application of ST suggests that the inclusion of safety and prevention activities in swimming lessons, aiming to develop attitudes and motor skills as a defense to drowning, must be taken into account. More studies are necessary to provide valid information to assist pediatricians in advising parents regarding what type of SL are more appropriate to reduce drowning.

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While Digital Preservation is making progress in terms of tool development, progressive establishment of standards and increasing activity in user communities, there is a lack of approaches to systematically assessing, comparing and improving how organizations go about achieving their preservation goals. This currently presents a challenge to many organizations for whom digital stewardship is a concern and reveals a substantial gap between theory and practice. To provide an interactive, focused venue for those interested in systematic approaches for assessment and improvement, we organized the first Capability