A novel-tuned Custom ensemble machine learning model to predict abutment scour depth in clear water conditions

Abstract

Most bridge failures occur due to the development of scour holes around the abutment and pier. Therefore, accurate prediction of abutment scour depth is critical for designing and maintaining bridges to ensure their safety and longevity. Traditional methods for predicting abutment scour depth, such as empirical formulas and physical models, have accuracy, applicability, and cost limitations. Machine learning (ML), on the other hand, has the potential to overcome these limitations by leveraging large amounts of data and identifying complex patterns and relationships that are difficult to detect using traditional methods. ML models can be trained on various data sources, including field measurements, laboratory experiments, and numerical simulations, to predict abutment scour depth accurately. Therefore, the present study aims to develop a novel-tuned Custom ensemble ML model for predicting abutment scour depth in clear-water conditions. The proposed Custom ensemble model outperforms the ML models used to predict non-dimensional scour depth at abutments with an accuracy of 95.93%.