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Abstract
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There is a growing concern about the safety of dams and dikes in modern society. The new regulations demand an increasing level of safety. Therefore, the technical community related to dams is making an effort to understand the failure mechanisms that threaten dam safety, prioritize actions with informed criteria, and develop more efficient solutions to heighten the safety of new and existing dams with the limited resources available.
The safety of dams and dikes involves multiple issues such as the understanding of the behavior, even in extreme conditions near failure; proper design, construction, and exploitation; and also logical and efficient management and assignment of available economical and personal resources.
The overtopping of embankment dams has been the main cause of dam failure in the last decades. Consequently, the main challenge related to dam safety is to find efficient solutions to quantify risk and avoid the failure of new and existing dams due to overtopping. Hydrological safety is one of the most active areas in dam engineering research, involving a considerable technical community all over the world.
The rest of the failure mechanisms (internal erosion, sliding, concrete cracking, etc.), related to dam behavior and geostructural safety usually affect parameters such as seepage, movements, or interstitial pressures that can be controlled by means of visual inspection and the analysis of the data provided by the monitoring system.
Predictive models are developed with the purpose of detecting anomalous dam behavior that could potentially be a symptom of the onset of an incident or dam failure. Physically based models, such as finite element models, or data models are useful for that. Both types of models have experienced enormous development in the last years and today a huge effort is being made to enhance their prediction accuracy.
Data models were traditionally based on multiple linear regression (HST model and a long list of models derived from that). Machine learning and artificial intelligence techniques are now being investigated to develop models that more closely adapt to the complexity of the dam-foundation system. Better accuracy and more profound understanding is being achieved by these methods, previously developed in different complex areas of knowledge such as sociology or the Internet.
In the field of dam safety management, risk analysis has also experienced a rapid dissemination. A lot of effort is concentrated on developing fragility curves for the different types of failure mechanisms, which are essential for a rigorous application of risk analysis.
A deep understanding of the physical processes involved in dam failure mechanisms is essential for modeling the behavior of dams and dikes in extreme situations, close to catastrophic failure, and the definition of reliable fragility curves...
Language: en |