Spatial transformer-based Machine learning architecture for bridge damage detection via car-mounted sensors

Resumo

Bridge infrastructure plays a vital role in facilitating transportation networks and is subject to various

types of damage that can compromise its structural integrity. Early detection of bridge damage is crucial for en-
suring public safety and minimizing maintenance costs. The present work proposes a spatial transformer-based

machine learning architecture for the detection of bridge damage through computational simulation of vibration
data. Traditional methods for bridge damage detection predominantly rely on visual inspections or expensive

sensor networks deployed on bridges. These methods are time-consuming, expensive, and often suffer from lim-
itations such as human subjectivity and limited coverage. To overcome these challenges, the proposed solution

leverages the advancements in machine learning that allow the detection of damages that can be easily overlooked
during the inspection process. Spatial Transformers are a type of neural network module that can learn to perform

spatial transformations on the input data. These transformations help the network align and focus on relevant re-
gions of the input data, which can be particularly useful in tasks that involve object recognition, image alignment,

and other spatially related problems. The advantages of the proposed system include its non-intrusive nature,
cost-effectiveness, and scalability.