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The massif rupture is not always reached under saturated conditions; therefore, the analysis of the unsaturated phenomenon is necessary in some cases. This study performed a probabilistic approach for unsaturated and transient conditions to understand the contribution of physical and hydraulic parameters involved in slope stability. The proposed slope stability model was based on the infinite slope method and a new unsaturated constitutive shear strength model proposed in 2021 by Cavalcante and Mascarenhas. The first-order second-moment method, which incorporated multiple stochastic variables, was used in the probabilistic analysis, allowing the incorporation of seven independent variables for the probability of failure analysis as well as for quantifying the contribution of the variables to the total variance of a factor of safety at any state of moisture. This implementation allows a more realistic estimative for the probability of failure, showing in a practical way the decrease and increase of the probability of failure during a rain event. The model provided promising results highlighting the need to migrate from deterministic analyses to more robust probabilistic analyses, considering the most significant number of stochastic variables. The proposed model helps to understand the influence of moisture content on slope stability, being a possible tool in natural disaster risk management. [ABSTRACT FROM AUTHOR]

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. [ABSTRACT FROM AUTHOR]