Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.

ABSTRACT: In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single

SUBMITTER: Lorenzen SR 

PROVIDER: S-EPMC9692483 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature