Estimation of virtual masses for structural damage identification

Hou, Jilin; Li, Zhenkun; Jankowski, Łukasz; Wang, Sijie

doi:10.1002/stc.2585

Cited by 5 publications

(1 citation statement)

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“…This includes natural direct continuations, such as identification of the load magnitude besides its trajectory and multiple trajectory identification in case of several concurrently moving loads, as well as applications to human-induced loads [ 42 , 43 ] and other types of excitations [ 44 ]. There are also several further related open problems: optimum placement of available sensors [ 45 , 46 ], determination of the optimum load trajectory and/or magnitude for the purpose of damage identification [ 47 , 48 ], the coupled problem of online trajectory identification and optimum semi-active structural control [ 49 ], and effective utilization of substructural identification approaches [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Trajectory Identification for Moving Loads by Multicriterial Optimization

Gawlicki

Jankowski

2021

Sensors

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Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is performed indirectly, based on the measured mechanical response of the structure. However, trivial solutions that directly fit the mechanical response tend to be erratic due to measurement and modeling errors. To achieve physically meaningful results, these solutions need to be numerically regularized with respect to expected geometric characteristics of trajectories. This paper proposes a respective multicriterial optimization framework based on two groups of criteria of a very different nature: mechanical (to fit the measured response of the structure) and geometric (to account for the geometric regularity of typical trajectories). The state-of-the-art multiobjective genetic algorithm NSGA-II is used to find the Pareto front. The proposed approach is verified experimentally using a lab setup consisting of a plate instrumented with strain gauges and a line-follower robot. Three trajectories are tested, and in each case the determined Pareto front is found to properly balance between the mechanical response fit and the geometric regularity of the trajectory.

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