Abstract
As part of the development of advanced, data-driven methods for predictive maintenance of railway infrastructure, this paper analyzes and evaluates more realistic predictions of eigenfrequencies of railway bridges, also referred to as natural frequencies, based on a population of already assessed, measured existing bridges using regression techniques. For this purpose, Machine Learning (ML) techniques such as Polynomial Regression (PR), ANN and XGBoost are consistently evaluated and the application of the XGBoost algorithm is identified as the most suitable prediction model for these eigenfrequencies, usable for dynamic train-bridge interactions. The results of the post-processing are incorporated into the safety architecture for bridge verification (risk management). The presented data-based techniques are a steppingstone towards digitalization of structural health monitoring and offer safety and longevity of the railway bridges. Furthermore, the use of these methods can save costs that would be incurred by physical in-situ measurements. The types of bridges analyzed with ML are Filler Beam Bridges (FBE), which outnumber other construction types of bridges in Germany (DB InfraGO AG). This methodology is applicable to any bridge type as long as sufficient data are gathered for training, validation and testing.
Published Version
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