Using limited roving sensors to monitor bridge subjected to random traffic load

Faridi, Md. Arif; Kuncham, Eshwar; Roy, Koushik; Singhal, Vaibhav

doi:10.1007/s13349-023-00748-8

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…Multiplying both sides of Equation ( 5) by H + on the left yields the virtual excitation vector l j : l j = H + r j (6) where the superscript + denotes the generalised inversion. The excitation PSD matrix S xx (ω) can be derived as follows:…”

Section: Theory Of Wind Load Identification For Offshore Wind Turbine...mentioning

confidence: 99%

“…Current research within the domain of frequency-domain load identification has progressed from the initial efforts of early investigators [5], expanding its scope to include a variety of engineering structures, such as bridge monitoring [6] and bearing fault diagnosis [7]. This broadening scope demonstrates the increasing applicability and significance of frequency-domain analysis techniques in structural engineering.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Wind Load Exerted on the Jacket Wind Turbines from Optimally Placed Strain Gauges Using C-Optimal Design and Mathematical Model Reduction

Zhu,

Zhang,

et al. 2024

JMSE

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Wind turbine towers experience complex dynamic loads during actual operation, and these loads are difficult to accurately predict in advance, which may lead to inaccurate structural fatigue and strength assessment during the structural design phase, thereby posing safety risks to the wind turbine tower. However, online monitoring of wind loads has become possible with the development of load identification technology. Therefore, an identification method for wind load exerted on wind turbine towers was developed in this study to estimate the wind loads using structural strain, which can be used for online monitoring of wind loads. The wind loads exerted on the wind turbine tower were simplified into six equivalent concentrated forces on the topside of the tower, and the initial mathematical model for wind load identification was established based on dynamic load identification theory in the frequency domain, in which many candidate sensor locations and directions were considered. Then, the initial mathematical model was expressed as a linear system of equations. A numerical example was used to verify the accuracy and stability of the initial mathematical model for the wind load identification, and the identification results indicate that the initial mathematical model combined with the Moore–Penrose inverse algorithm can provide stable and accurate reconstruction results. However, the initial mathematical model uses too many sensors, which is not conducive to engineering applications. Therefore, D-optimal and C-optimal design methods were used to reduce the dimension of the initial mathematical model and determine the location and direction of strain gauges. The C-optimal design method adopts a direct optimisation search strategy, while the D-optimal design method adopts an indirect optimisation search strategy. Then, four numerical examples of wind load identification show that dimensionality reduction of the mathematical model leads to high accuracy, in which the C-optimal design algorithm provides more robust identification results. Moreover, the fatigue damage calculated based on the load identification wind loads closely approximates that derived from finite element simulation wind load, with a relative error within 6%. Therefore, the load identification method developed in this study offers a pragmatic solution for the accurate acquisition of the actual wind load of a wind turbine tower.

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Section: Theory Of Wind Load Identification For Offshore Wind Turbine...mentioning

confidence: 99%