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

ABSTRACT

The physics of local scour around bridge piers is fairly complex because of multiple forces acting on it. Existing empirical formulas cannot cover all scenarios and soft computing methods require ever greater amounts of data to cover all cases with a single formula or a neural network. The approach proposed in this study brings together observations from over 40 studies, grouping similar observations with hierarchical clustering, and using genetic programming with adaptive operators to evolve formulas specific to each cluster to predict the scour depth. The resulting formulas are made available along with a basic web-based user interface that finds the closest cluster for newly presented data and finds the scour depth using the formula for that cluster. All formulas have R² scores over 0.8 and have been validated with validation and testing sets to reduce overfitting. When compared to existing empirical formulas, the generated formulas consistently record higher R² scores.

Acknowledgments

We thank the anonymous reviewers for their valuable comments and feedback which have improved the manuscript significantly. We are grateful for the high-end system Assoc. Prof. Dr. Osman Doluca has provided to speed up the training of our data. We are also indebted to Simon Edward Mumford for his help in language editing and proofreading.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.