This study aims at establishing machine learning models based on the support vector regression (SVR) for estimating local scour around complex piers under steady clear-water condition. A data set consisting of scour depth measurement cases has been collected to construct the prediction models. The data set includes eight inﬂuencing factors that consider aspects of pier geometry, ﬂow property, and river bed material. Moreover, to enhance the performance of the SVR model, ﬁlter and wrapper feature selection strategies are used. The research ﬁnding is that all feature selection approaches can help to improve the prediction accuracy compared with the SVR model that uses all available features. Notably, the feature selection method based on the variable neighborhood search (VNS) algorithm achieves the best performance (MAPE = 21.65%, R = 0.85). Accordingly, the prediction model produced by SVR and VNS can be useful for assisting decision makers in the task of structural health monitoring as well as the design phase of bridges. Keywords Scour depth prediction Bridge Scour Complex pier foundations Support vector regression Feature selection Variable neighborhood search 1 Introduction bridge failures in the United States are related to scour . More importantly, scour failures have the
Journal of Civil Structural Health Monitoring – Springer Journals
Published: Jun 2, 2018
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