Sensor clustering-based approach for structural damage identification under ambient vibration

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Section: Data Collectionmentioning

confidence: 99%

Bridge modal property identification based on asynchronous mobile sensing data

Eshkevari¹,

Cronin

Matarazzo

et al. 2022

“…Therefore, the data source is easy to obtain and avoids the uncertainty caused by modal extraction. 2 Many studies have been done on time series-based damage identification methods.…”

Section: Introductionmentioning

confidence: 99%

“…SHM is a process of damage identification of a structure by measuring the vibration responses continuously through advanced techniques to assess the health of the structure, replacing traditional periodic visual inspections. [1][2][3] The steps of structural damage identification mainly include: (1) determining the existence of damage; (2) identifying the damage locations; (3) measuring the damage severity, and (4) estimating the residual life of the structure. 4 For the simplicity of research, it is usually assumed that the structural damage is linear damage because the structural component stiffness is reduced linearly.…”

Section: Introductionmentioning

confidence: 99%

Structural nonlinear damage identification based on the information distance of GNPAX/GARCH model and its experimental study

Heng

Guo

2023

In the structural health monitoring (SHM) of civil engineering, most of the structural damage is nonlinear damage, such as breathing cracks and bolt looseness. Under the excitation of external loads, the time-domain response data of the structure produced by these nonlinear damages have nonlinear features. In order to solve the time-domain nonlinear damage identification problem of complex structures, this paper proposes a nonlinear damage identification method based on the information distance of GNPAX/GARCH (general expression of system identification for linear and nonlinear with polynomial approximation and exogenous inputs/generalized autoregressive conditional heteroskedasticity) model. First, an order determination method based on Bayesian optimization to select the order of the GNPAX/GARCH model was proposed, and the GNPAX/GARCH model was established for damage identification. Then, the redundant structural items of GNPAX/GARCH model were removed by the model optimization method based on the structural pruning algorithm. Finally, the information distance of the GNPAX/GARCH model conditional heteroscedasticity series between the baseline state and test state was derived, and the structural damage source locations were determined according to the information distance. A three-story frame structure experiment and a stand structure experiment were used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively identify the nonlinear damages caused by the component breathing crack and joint bolt looseness, verifying its robustness to the nonlinear damage identification of the multi-story and multi-span complex structures.

“…Most large structures have been equipped with automated structural health monitoring (SHM) systems for operational status understanding in real-time. 1 A complete SHM system contains a large number of sensors, data formatting, data transmission, data storage, data analysis and modeling, and assessment. 2 Unfortunately, the working environment of some structures, such as dams, bridges, and tunnels, are always much more complicated.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous structural responses recovery based on multi-modal deep learning

Sun

et al. 2022

For structural health monitoring, a complete dataset is important for further analysis such as modal identification and risk early warning. Unfortunately, the missing data normally exist in current database due to sensor failures, transmission system interruption, and hardware malfunctions. Currently, most of the studies just deleted the dataset containing missing data or using mean values as imputation which could wrongly reflect the characteristics changes of the structure. The present study therefore develops a heterogeneous structural response recovery method based on multi-modal fusion auto-encoder which can consider temporal correlations and spatial correlations and correlations between heterogeneous structural responses simultaneously. Moreover, a parallel optimization method is proposed to optimize the parameters of the deep fusion networks. A dataset containing about 3 months and two input attributes is collected from a bridge and utilized for training and testing the proposed method and some benchmark methods. Statistical scores including root mean square error (RSME), mean absolute error (MAE), and mean relative error (MRE) are applied to evaluate the performance of the implemented models, respectively. Results show that the proposed method achieve the best imputation performance under different missing scenarios. Furthermore, the proposed method can achieve better performance when the missing rate is high. The results suggest that the consideration between heterogeneous structural responses is critical for missing data recovery.