Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming

Oğuz, Kaya; Bor, Aslı

doi:10.1080/08839514.2021.2001734

Cited by 8 publications

(1 citation statement)

References 46 publications

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“…Melville and Chiew [8], Sheppard et al [38]; Ettema et al [39], Lanca et al [40], Oguz et al [41], and Nil et al [42] grouped the non-dimensional factors to develop a better scour depth predictive model. Maximum scour depth (d s ) was represented by the following connection in terms of non-dimensional parameters:…”

Section: Identification Of Influencing Parameters For Scour Depth Mod...mentioning

confidence: 99%

Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Kumar,

Baranwal,

Das

2023

Eng. Res. Express

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Scouring is a threat to the foundations of bridges, and it frequently results in bridge collapse. Recently, machine-learning approaches have become quite popular in solving hydraulic and hydrologic problems. The present study adopted adaptive neuro-fuzzy inference systems (ANFIS) and gene expression programming (GEP) to model bridge pier scour depth. A temporal scour depth relationship has been proposed based on ANFIS and GEP approaches. In this paper, 500 data sets have been used to model the temporal scour depth using ANFIS and GEP models, in which 80% (400) data sets for training and 20% (100) datasets for testing purposes are used to develop the model. The performance of the GEP model is validated by comparing the results of the developed ANFIS model and other existing empirical equations. It is found that the GEP-based scour depth prediction model is effective in training and validation. The present GEP model accurately predicts the scour depth with a mean absolute percentage error (MAPE) value of less than 12% and an R2 value greater than 0.85. Thus, the GEP model can be utilized to predict scour depth around the bridge pier for unsteady flow conditions.

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Section: Identification Of Influencing Parameters For Scour Depth Mod...mentioning

confidence: 99%

Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Kumar,

Baranwal,

Das

2023

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

Modelling of Temporal Clear Water Scour Depth Around Bridge Piers Using XGBoost and SVM-PSO

Gaurav,

Lohith Reddy,

Baranwal

et al. 2024

Lecture Notes in Civil Engineering

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Local Scour Depth Around Bridge Piers: Performance Evaluation of Dimensional Analysis-based Empirical Equations and AI Techniques

Ghumman,

Haider,

Al Salamah

et al. 2024

KSCE J Civ Eng

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Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming

Cited by 8 publications

References 46 publications

Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Modelling of Temporal Clear Water Scour Depth Around Bridge Piers Using XGBoost and SVM-PSO

Local Scour Depth Around Bridge Piers: Performance Evaluation of Dimensional Analysis-based Empirical Equations and AI Techniques

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