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http://dx.doi.org/10.3741/JKWRA.2021.54.12.1275

Prediction of time dependent local scour around bridge piers in non-cohesive and cohesive beds using machine learning technique  

Choi, Sung-Uk (Department of Civil and Environmental Engineering, Yonsei University)
Choi, Seongwook (Department of Civil and Environmental Engineering, Yonsei University)
Choi, Byungwoong (Yeongsan River Environment Research Center, National Institute of Environmental Research, Ministry of Environment)
Publication Information
Journal of Korea Water Resources Association / v.54, no.12, 2021 , pp. 1275-1284 More about this Journal
Abstract
This paper presents a machine learning technique applied to prediction of time-dependent local scour around bridge piers in both non-cohesive and cohesive beds. The support vector machines (SVM), which is known to be free from overfitting, is used. The time-dependent scour depths are expressed by 7 and 9 variables for the non-cohesive and cohesive beds, respectively. The SVM models are trained and validated with time series data from different sources of experiments. Resulting Mean Absolute Percentage Error (MAPE) indicates that the models are trained and validated properly. Comparisons are made with the results from Choi and Choi's formula and Scour Rate in Cohesive Soils (SRICOS) method by Briaud et al., as well as measured data. This study reveals that the SVM is capable of predicting time-dependent local scour in both non-cohesive and cohesive beds under the condition that sufficient data of good quality are provided.
Keywords
Time-dependent local scour; Bridge pier; Support vector machines; Machine learning; Cohesive bed;
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Times Cited By KSCI : 2  (Citation Analysis)
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