Application of Machine Learning Models to Bridge Afflux Estimation

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.

A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

“…The two main functions in GP are (i) the covariance function that quantifies the resemblance between the input vectors of the training and testing datasets, and (ii) the mean function that governs the complexity of the model. It is noteworthy that the former often holds a greater significance than the latter [27].…”

“…The neurons positioned within a specific layer are connected to the neurons in adjacent layers, while no connection among neurons of a single layer is allowed. This architecture enables ANN to serve as a suitable ML model in various fields of research, particularly for water quality [22][23][24][25] and other applications in water resources management [26,27].…”

“…Essentially, each weak learner has a weight based on its relative predictive ability, while adding new weak learners modifies the weights. The process of assembling weak learners continues until the error is minimized or a predefined threshold is met [27]. This study utilized three boosting-based algorithms including AdaBoost (AB), Gradient Boosting Regressor (GBR), and XGBoost Regressor (XGBR).…”

“…For this purpose, it employs a distance function. For continuous variables, the most frequently implemented distance functions include Euclidean, Manhattan, and Minkowski [27]. One of the most common distance functions is the Euclidean distance, which can be calculated using the following equation:…”

“…An SVM is a highly adaptable and potent supervised ML algorithm that addresses complex classification, regression, and outlier detection problems by executing optimal data transformations. These transformations create boundaries using an optimal hyperplane between the data points, based on pre-defined classes, labels, or output [27]. The hyperplane is positioned in such a way that it maximizes the margin between the classes being considered.…”