Work

Estimation of Equilibrium Scour Depth Around Abutments Using Artificial Intelligence

Year: 2025

Type: article

Abstract: The estimation of clear water scour depth around bridge piers directly affects the stability and safety of bridge structures. An accurate estimation of equilibrium scour depth (Dse) around bridge abutments is crucial for ensuring structural stability. This study employs Multiple Linear Regression (MLR), Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), XGBoost, and Artificial Neural Networks (ANN) to predict Dse. The dataset consists of 150 records with key hydraulic parameters: flow depth (Y), abutment length (L), channel width (B), flow velocity (V), and median grain size (D50). Performance evaluation was conducted using MSE, RMSE, and R² metrics. Results show that DTR achieved the highest accuracy (R² = 0.992, Accuracy = 99.28%), followed by XGBoost (R² = 0.990, Accuracy = 99.21%) and ANN (R² = 0.981, Accuracy = 98.77%). Traditional MLR exhibited lower accuracy (R² = 0.806, Accuracy = 81.14%), reinforcing the superiority of AI-based models. These findings highlight the effectiveness of machine learning in scour prediction, providing a reliable alternative to conventional methods. This study underscores the potential of AI in hydraulic engineering, facilitating efficient bridge design and maintenance strategies. Future research should explore real-time data integration and hybrid AI models for improved interpretability and robustness.

Source: Water

DOI: 10.3390/w17071010

Authors: Yusuf Uzun, Serife Yurdagül Kumcu

Institution: Necmettin Erbakan University

Language: English

Topic: Hydrology and Sediment Transport Processes

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137