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Descripción
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| Dam monitoring and their safety are an important concern of dam engineers. Seepage collected data are indicators of structure behavior, since seepage is influenced by environmental actions, such as air temperature, water temperature, and water level variation, and seepage flow rate is greatly influence by the presence of fractures. Consequently, the analysis of seepage collected data is an important monitoring task, as variations in the seepage can be the alarm for subsequent failures. Seepage data are widely analyzed with statistical models. In this work, we assess the performance of support vector regression machine and random forest models to predict seepage at different points in a case study and identify the most important environmental variables affecting flow rate. | |
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Internacional
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Si |
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JCR del ISI
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No |
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Título de la revista
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Procedia Structural Integrity |
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ISSN
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2452-3216 |
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Factor de impacto JCR
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Información de impacto
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Volumen
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17 |
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DOI
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10.1016/j.prostr.2019.08.093 |
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Número de revista
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Desde la página
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698 |
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Hasta la página
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703 |
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Mes
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SIN MES |
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Ranking
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