A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes

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.

Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

“…In recent years, machine learning approaches have been widely used for regression [7][8][9][10] and classification [11,12] in water engineering, such as scour depth prediction [13], riprap stone size prediction [14], and rivers dispersion coefficient prediction [15]; these approaches were also applied to predict the settlement of the soft foundation, and a list of these approaches is shown in Table 1. The support vector machine (SVM) was applied to predict the settlement of shallow foundations on cohesionless soils by Samui [16], and the simulation results showed that the SVM could be a practical tool for predicting the settlement of the foundation [17].…”

Stability Assessment of Rubble Mound Breakwaters Using Extreme Learning Machine Models

“…In the studies of Thompson [10] and Hanzawa [6], a damage parameter was proposed to describe the damage condition of the breakwater section, which was a function of the stone density, stone size, wave height, wave number and erosion area in a cross section, while in the studies of van der Meer [2,3] and Kajima [5], a simple damage level was proposed. A summary of the definitions is listed in Table 1 [11]. Table 1.…”