A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems

Manish Pandey (), Masoud Karbasi (), Mehdi Jamei (), Anurag Malik () and Jaan H. Pu ()
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Manish Pandey: National Institute of Technology Warangal
Masoud Karbasi: University of Zanjan
Mehdi Jamei: Shahid Chamran University of Ahvaz
Anurag Malik: Regional Research Station
Jaan H. Pu: University of Bradford

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 9, No 21, 3745-3767

Abstract: Abstract Several bridges failed because of scouring and erosion around the bridge elements. Hence, precise prediction of abutment scour is necessary for the safe design of bridges. In this research, experimental and computational investigations have been devoted based on 45 flume experiments carried out at the NIT Warangal, India. Three innovative ensemble-based data intelligence paradigms, namely categorical boosting (CatBoost) in conjunction with extra tree regression (ETR) and K-nearest neighbor (KNN), are used to accurately predict the scour depth around the bridge abutment. A total of 308 series of laboratory data (a wide range of existing abutment scour depth datasets (263 datasets) and 45 flume data) in various sediment and hydraulic conditions were used to develop the models. Four dimensionless variables were used to calculate scour depth: approach densimetric Froude number (Fd50), the upstream depth (y) to abutment transverse length ratio (y/L), the abutment transverse length to the sediment mean diameter (L/d50), and the mean velocity to the critical velocity ratio (V/Vcr). The Gradient boosting decision tree (GBDT) method selected features with higher importance. Based on the feature selection results, two combinations of input variables (comb1 (all variables as model input) and comb2 (all variables except Fd50)) were used. The CatBoost model with Comb1 data input (RMSE = 0.1784, R = 0.9685, MAPE = 10.4724) provided better accuracy when compared to other machine learning models.

Keywords: Abutment; Scour depth; Extra tree regression; CatBoost; Gradient boosting decision tree; Feature selection (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03525-w

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