Abstract:The dam-break induced loads and their effects on buildings are of vital importance for assessing the vulnerability of buildings in flood-prone areas. A comprehensive methodology, for risk assessment of buildings subject to flooding, is nevertheless still missing. This research aims to take a step forward by following previous research. To this aim, (1) five statistical procedures including: simple correlation analysis, multiple linear regression model, stepwise multiple linear regression model, principal component analysis and cluster analysis are used to study relationship between mean normalized force on structure and other related variables; (2) a new and efficient variable that can take into account both the shape of the structure and flow conditions is proposed; (3) a new and practical formula for predicting the mean normalized force is suggested for different types of obstacles, which is missing in the previous research.
Predicting peak breach discharge due to embankment dam failure is of vital importance for dam failure prevention and mitigation. Because, when dams fail, property damage is certain, but loss of life can vary depending on flood area and population. Many parametric breach models based on regression techniques have been developed so far. In this study, an efficient model is proposed to forecast peak discharge from the height of the water and volume of water behind the dam at failure, respectively, by using the Kriging approach. The previous studies, which consist of 13 numerical models, are used as a benchmark for testing the proposed new model, by employing five different error criteria. Moreover, a new database is compiled by extending the previous one. In addition, it is demonstrated that R 2 , which only quantifies the dispersion between measurements and predictions, should not be considered alone for checking the model capabilities. At least, the other criteria should be employed together with R 2 . As a result, it is shown that one can forecast the peak flow discharge with more significant accuracy by the proposed model than other previous models developed so far.
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