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A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

  • Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybridComplex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHWKstar) to predict scour depth (ds) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45◦ wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d50/l) and relative submergence (d50/h) were considered for the prediction of relative scour depth (ds/l). A portion of the dataset was used for the calibration (70%), and the remaining used for model validation. Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of Fe, d50/l and h/l, while for semicircular and 45◦ wing the combination of the Fe and d50/l is the most effective input parameter combination. Our results show that incorporating Fe, d50/l and h/l lead to higher performance while involving d50/h reduced the models prediction power for vertical abutment shape and for semicircular and 45◦ wing involving h/l and d50/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45◦ wing.show moreshow less

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Metadaten
Document Type:Preprint
Author: Khabat Khosravi, Zohreh Sheikh Khozani, Luka Mao
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.4388Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210311-43889Cite-Link
Language:English
Date of Publication (online):2021/02/23
Date of first Publication:2021/03/04
Release Date:2021/03/11
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Pagenumber:43
GND Keyword:maschinelles Lernen
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:56 Bauwesen
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)
Note:
This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/abs/pii/S0022169421001475?via%3Dihub ; https://doi.org/10.1016/j.jhydrol.2021.126100