首页|Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

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Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels.In this study,at first,a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels.The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions.A set of,data mining and machine learning algorithms including Random Forest(RF),M5P,Random Committee,KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel.Results indicated among these five models;RF method indicated the most precise results with the highest R2 value of 0.9.Finally,the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method(SKM)and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

compound channelmachine learningSKM modelshear stress distributiondata mining models

Zohreh SHEIKH KHOZANI、Khabat KHOSRAVI、Mohammadamin TORABI、Amir MOSAVI、Bahram REZAEI、Timon RABCZUK

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Institute of Structural Mechanics, Bauhaus Universität-Weimar Weimar D-99423, Germany

Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari 48181, Iran

Department of Civil and Environmental Engineering, Idaho State University, Pocatello, ID 83209, USA

School of the Built Environment, Oxford Brookes University, Oxford OX30BP UK eKando Kalman Faculty of Electrical Engineering, Obuda University, Budapest 1034, Hungary

Department of Civil Engineering, Bu-Ali Sina University, Hamedan 65178, Iran

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2020

结构与土木工程前沿
高等教育出版社

结构与土木工程前沿

CSTPCDSCIEI
影响因子：0.082
ISSN：2095-2430
年,卷(期)：2020.14(5)
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