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.
É notável na engenharia contemporânea que, o monitoramento dos índices físicos do solo é de suma importância na previsão do seu comportamento, como no monitoramento das deformações ocorrentes no maciço de uma barragem. Assim, motivados por essa necessidade, o presente trabalho estabelece a construção de um sistema de monitoramento de baixo custo com a utilização do sistema de software e hardware Arduino para a análise do teor de umidade em uma coluna de areia durante o fenômeno de ascensão capilar. Dessa forma, nota-se que, a análise em tempo real dos estados de umidade do solo tornase viável por meio de um sistema de monitoramento de baixo custo.
The characterization of unsaturated soils using hydromechanical methods is an essential requirement in soil science. However, current laboratory techniques used to obtain soil water retention and unsaturated hydraulic conductivity curves are time-consuming. To address this issue, a method based on indirect measures (electrical resistivity/electrical conductivity) was developed to quantitatively characterize soils. A novel unsaturated semi-empirical hydrogeophysical model of soils was developed by incorporating the hydrodynamic, geophysical, and petrophysical characteristics of soils. The model assumes that the parameters influencing the variation in the volumetric water content with matric suction and electrical resistivity are the same. The electrical resistivity characteristic curve (ERCC) defines a function that correlates environmental variables, electrical resistivity, soil water status, matric suction, hydraulic and petrophysical parameters, and fluid electrical resistivity. Model validation confirmed that the proposed approach can estimate the soil water retention curve (SWRC) via the indirect measures, and the results agreed with the experimental data. This indicates that it is possible to determine the SWRC and unsaturated hydraulic conductivity function of soil using the described approach.
The continuous monitoring of capillary rise via indirect measures aims to predict and generate alerts regarding the soil mass deformations, transport leachate from landfills to the soil surface, and carry salts that can damage buildings. Through time-lapse monitoring of the electromagnetic wave's electrical potential and speed, it is possible to correlate via petrophysical relations the measures of electrical potential, electrical resistivity, and dielectric permittivity to the volumetric water content and capillary height. For this, four acrylic columns filled with civil construction material were instrumented. Column 1 - silver electrodes to measure the potential difference with a bench multimeter that measures the spontaneous potential generated by water flow. Column 2 - low-cost soil moisture sensors that measured the electrical potential and converted to bits. Column 3 - resistivimeter that measured the voltage and that was later converted to electrical resistivity and, Column 4 - 2.6 GHz antenna that measured the speed of the electromagnetic wave that was later converted into dielectric permittivity. The instrumentation assembled proved to be satisfactory to monitor the phenomenon. The monitoring lasted 187 h, and it was found that the maximum capillary height remained constant for a long time.
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