Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods
Daniel Cantero, Muhammad Zohaib Sarwar, Abdollah Malekjafarian, Robert Corbally, Mehrisadat Makki Alamdari, Prasad Cheema, Jatin Aggarwal, Hae Young Noh, Jingxiao Liu
Abstract
Abstract Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset.