Abstract
Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.
Highlights
The Yangtze River is the largest river, and the most important navigation channel, in China.In recent decades, systematic river training works have been carried out on this waterway.Various channel training structures, such as spur dikes and revetments, have been built, which play important roles in protecting key shorelines, controlling unfavorable riverbed evolution, and increasing the navigability of the channel
The damage to the spur dike was classified into three levels
The 336 experiment data sets were categorized into these three damage levels
Summary
The Yangtze River is the largest river, and the most important navigation channel, in China.In recent decades, systematic river training works have been carried out on this waterway.Various channel training structures, such as spur dikes and revetments, have been built, which play important roles in protecting key shorelines, controlling unfavorable riverbed evolution, and increasing the navigability of the channel. As time has passed, water and sand scour have caused the channel training structures to sustain various levels of damage, impairing their ability to regulate the channel. Little research about evaluating the damage level of the channel training structures has been carried out so far. It is very important to evaluate the damage level of these structures in order to maintain them over time. There are many factors that could affect the running condition of the training structures. These factors are difficult to describe quantitatively. Some researchers use a number to describe the stability of hydraulic engineering structures; for example, Ns in Equation (1)
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