A New Approach for Multiple Loads Identification Based on the Segmental Area of the Influence Lines

The dynamic responses of bridges under multi moving loads are an essential precursor for their structural health monitoring (SHM). To enable the precise identification of the main moving load(s) among multiple moving loads, this study proposes an improved multi-source dynamic load identification alg...

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Vydáno v:Infrastructures (Basel) Ročník 10; číslo 11; s. 308
Hlavní autoři: Liu, Ping, Qiu, Weiwei, Kaewunruen, Sakdirat
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2025
Témata:
Accuracy
Algorithms
Bridges
Deep learning
dynamic load inversion algorithm
Dynamic loads
Errors
Fourier transforms
Identification
influence line
Influence lines
Inverse problems
Kalman filters
Laws, regulations and rules
Load
load identification
Moving loads
Neural networks
Regularization methods
Sensors
Strain gauges
Structural health monitoring
Traffic speed
Vehicles
Vision systems
ISSN:2412-3811, 2412-3811
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Shrnutí:The dynamic responses of bridges under multi moving loads are an essential precursor for their structural health monitoring (SHM). To enable the precise identification of the main moving load(s) among multiple moving loads, this study proposes an improved multi-source dynamic load identification algorithm based on the segmental area of the influence line (SAI). Firstly, the segmental area of the influence line was calculated according to the velocity of loads and the distance between two loads, and then, the moving load could be isolated based on the law of the minimal error combining the base area of the original influence line. In addition, experiments were conducted employing laser displacement sensor systems to acquire structural dynamic responses. The results showed the following for the segmental area of the influence line: (1) identification errors for a single moving load could be controlled within 5%, while an error within 10% was achieved under two moving loads; (2) vehicle displacement identification error remained consistently below 5%; and (3) the proposed algorithm exhibited a speed-insensitive characteristic, enabling effective load identification across varying vehicle speeds. The experimental findings confirm that this method accurately identifies the main moving loads in a small deformation condition and can be extended to similar applications.
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ISSN:2412-3811
2412-3811
DOI:10.3390/infrastructures10110308