A novel extreme gradient boosting algorithm based model for predicting the scour risk around bridge piers: application to French railway bridges

Abstract

Bridge failures inevitably cause severe social consequences in terms of the economic losses or even human fatalities. Scour is one of the leading causes for bridge failures. Due to its complexity and multidisciplinary nature, the mechanism of scour has not been completely understood yet. Inspired by the rapid development of Machine Learning (ML) techniques, this paper aims to construct a novel data-driven extreme gradient boosting (XGBoost) algorithm based model to predict the scour risk around bridge piers. Data used in the present study is provided by the French National Railway Company (SNCF). The performance of XGBoost-based model is compared with three commonly used algorithms: support vector machine, random forest and multilayer perceptron. Results show that the XGBoost classifier achieves high accuracy (0.959/0.938), precision (0.970/0.961), recall (0.974/0.956) and low false positive rate (0.085/0.114) for training and test set respectively. Moreover, the classifier obtains an area under the ROC curve (AUC) score equal to 0.974 for test set (a perfect classifier has an AUC equal to 1). This paper presents a cutting-edge application of XGBoost algorithm in the maintenance of railway bridges. The proposed methodology may allow engineers to determine the scour risk of bridge piers in an accurate and rapid way.

Keywords:

• Scour risk
• railway bridges
• machine learning
• XGBoost

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

1 The regions here are planned by SNCF and should be distinguished from the administrative regions in Metropolitan France. Nantes & Rennes, Metz & Nancy are considered as two regions.

2 https://pandas.pydata.org/

3 https://xgboost.readthedocs.io/en/stable/index.html

4 https://scikit-learn.org/stable/

5 https://keras.io/

Additional information

Funding

This work was supported by the French National Association of Research and Technology under Grant [2020/0750].