Abstract
Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known as bridge backwater or afflux, can threaten the stability or service of bridges and riverbanks. It is postulated that applications of estimation models with more precise afflux predictions can enhance the safety of bridges in flood-prone areas. In this study, eight machine learning (ML) models were developed to estimate bridge afflux utilizing 202 laboratory and 66 field data. The ML models consist of Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Gradient Boost Regressor (GBR), eXtreme Gradient Boosting (XGBoost) for Regression (XGBR), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN). To the best of the authors’ knowledge, this is the first time that these ML models have been applied to estimate bridge afflux. The performance of ML-based models was compared with those of artificial neural networks (ANN), genetic programming (GP), and explicit equations adopted from previous studies. The results show that most of the ML models utilized in this study can significantly enhance the accuracy of bridge afflux estimations. Nevertheless, a few ML models, like SVR and ABR, did not show a good overall performance, suggesting that the right choice of an ML model is important.