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Scour depth estimation using standalone metaheuristic algorithms and their combinations with CatBoost.

Nasrin Eini1, Saeid Janizadeh1, Sayed M Bateni1,2

  • 1Department of Civil, Environmental and Construction Engineering & Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.

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Summary
This summary is machine-generated.

This study introduces advanced machine learning and optimization techniques to accurately predict bridge pier scour depth, improving structural safety and reducing maintenance costs. The optimized models significantly outperform traditional methods, offering more reliable engineering solutions.

Keywords:
Bridge pierCatBoostExplicit equationSHAPScour depth

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Area of Science:

  • Civil Engineering
  • Hydraulic Engineering
  • Computational Mechanics

Background:

  • Scouring around bridge piers is a major cause of structural failure, necessitating accurate prediction of equilibrium scour depth (Seq).
  • Existing empirical equations often lack accuracy due to the complex, nonlinear nature of hydraulic processes influencing scour.
  • Advanced computational methods are needed to overcome limitations of traditional scour prediction models.

Purpose of the Study:

  • To develop and evaluate novel approaches for estimating equilibrium scour depth (Seq) around bridge piers.
  • To compare the performance of optimized machine learning models and derived explicit equations against existing methods.
  • To identify key factors influencing scour depth through explainability analyses.

Main Methods:

  • Optimizing a Categorical Boosting (CatBoost) machine learning model using five metaheuristic algorithms: Harris Hawk Optimization (HHO), Moth-Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Pelican Optimization Algorithm (POA), and Fox Optimization Algorithm (FOX).
  • Developing explicit scour depth prediction equations derived from the same metaheuristic optimization algorithms.
  • Utilizing SHapley Additive exPlanations (SHAP) and sensitivity analyses to understand model behavior and factor importance.

Main Results:

  • The HHO-CatBoost hybrid model demonstrated superior performance, achieving a coefficient of determination (R2) of 0.9670, root-mean-square error (RMSE) of 0.0286 m, and mean absolute error (MAE) of 0.0178 m.
  • The HHO-derived explicit equation, excluding the Reynolds number, outperformed 18 existing equations with R2=0.828, RMSE=0.066 m, and MAE=0.043 m.
  • SHAP analysis identified pier diameter as the most influential factor, while sensitivity analysis highlighted the pier diameter to flow depth ratio as most significant under turbulent conditions.

Conclusions:

  • Optimized machine learning models and derived explicit equations offer significant improvements in predicting equilibrium scour depth compared to traditional methods.
  • Metaheuristic algorithms, particularly HHO, are effective in enhancing the accuracy of both machine learning models and explicit scour prediction equations.
  • Understanding the influence of factors like pier diameter and flow depth is crucial for accurate bridge scour assessment and risk management.