Damage identification of single-layer cylindrical latticed shells based on the model updating technique

Xu, Ying; Pan, Yaokun; Wang, Ying; Deng, Dewei; Han, Qinghua

doi:10.1007/s13349-021-00543-3

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Vibration-based diagnostic analysis [5,6] is a valuable tool to efectively identify and locate structural damage. By conducting a modal analysis of the dynamic data, parameters such as the frequencies and mode shapes of the structure can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Han

Dang

et al. 2023

Structural Control and Health Monitoring

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A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.

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Section: Introductionmentioning

confidence: 99%

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Han

Dang

et al. 2023

Structural Control and Health Monitoring

Self Cite

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“…Numerical simulations and experimental tests on steel plates with various defect depths showed that the algorithm is sensitive to the defect depth. Xu et al [21] used the model updating technique and the estimation of distribution algorithm for defect identification in a single-layer cylindrical latticed shell. Numerical and experimental results demonstrated the effectiveness of the presented defect detection algorithm in the presence of noise interference and multiple damage conditions.…”

Section: Introductionmentioning

confidence: 99%

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

et al. 2022

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Circular structures are used in a wide variety of engineering mechanisms and devices. In this paper, an effective algorithm on the basis of the complex mappings is proposed to identify defects in circular structures using Cartesian damage detection techniques. The efficacy of the proposed algorithm is demonstrated through damage identification in circular plates using the Cartesian wavelet analysis. The vibration and thermal responses of the structure, as two important structural signals, are imported into the proposed algorithm to evaluate the abilities of the signals in identifying the damage location and severity. Finally, two experimental tests are conducted to explore the efficacy of the proposed algorithm in real applications.

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Force evaluation of internal cable of prestressed grids based on field monitoring and hierarchical objective model updating

Qian

et al. 2023

J Civil Struct Health Monit

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Damage identification of single-layer cylindrical latticed shells based on the model updating technique

Cited by 6 publications

References 28 publications

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

Force evaluation of internal cable of prestressed grids based on field monitoring and hierarchical objective model updating

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