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

Lorenzen, Steven Robert ; Riedel, Henrik ; Rupp, Maximilian Michael ; Schmeiser, Leon ; Berthold, Hagen ; Firus, Andrei ; Schneider, Jens (2022)
Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.
In: Sensors, 2022, 22 (22)
doi: 10.26083/tuprints-00022977
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
Language: English
Date: 19 December 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Sensors
Volume of the journal: 22
Issue Number: 22
Collation: 17 Seiten
DOI: 10.26083/tuprints-00022977
Corresponding Links:
Origin: Secondary publication DeepGreen

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 evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

Uncontrolled Keywords: moving load localisation, nothing-on-road, free-of-axle-detector, bridge weigh-in-motion, structural health monitoring, field validation, continuous wavelet transformation, machine learning, fully convolutional networks
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-229771
Additional Information:

This article belongs to the Special Issue Advances in Condition Monitoring of Railway Infrastructures

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 690 Building and construction
Divisions: 13 Department of Civil and Environmental Engineering Sciences > Institute für Structural Mechanics and Design
Date Deposited: 19 Dec 2022 12:32
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22977
PPN: 503239836
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