Condition identification of bolted connections using a virtual viscous damper

Biswal, Suryakanta; Chryssanthopoulos, Marios K.; Wang, Ying

doi:10.1177/14759217211009217

Cited by 11 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the proposed method in such a situation, this study will use numerical simulation data as the source domain data and laboratory test data as the target domain, referred to as case 2 thereafter. In this case, calibrated FE and state-space models are employed to generate time-domain vibration responses of the prototype structure that match the actual test data almost precisely [20]. Te results demonstrate the similarity of the data between the source domain and target domain, so the negative transfer can be avoided.…”

Section: Methodsmentioning

confidence: 98%

“…For each accelerometer, a total of 110 sets of structural responses were recorded. Tese datasets were not only used to construct SHMnet and to perform the two-stage model updating [20] but also as the training data source for the present study. More details on the experimental works can be found in [24].…”

Section: Casementioning

confidence: 99%

“…Simply speaking, model updating is to fnd the best match of FEderived parameters to those obtained from the monitoring data by iteratively changing the physical parameters in the FE model using an optimization algorithm, which has been widely applied to structural condition identifcation [18,19]. For bolted connection damage, a virtual damper concept and a two-stage model updating scheme have been proposed, which achieved time-domain simulation consistency with real test results [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

Bao

Zhang

Huang

et al. 2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data are almost always insufficient because the real-time monitoring data of a structure only reflect a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. First, disparate subsets of test data are used as training data, and the identification accuracy of the whole dataset is evaluated. The results demonstrate that the proposed approach can achieve high identification accuracy with limited types of training data, with the identification accuracy increasing up to 8.57%. Second, numerical simulation data are used as training data, and then different TL strategies and different DL architectures are compared on the performance of structural condition identification. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGNet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data.

show abstract

Section: Methodsmentioning

confidence: 98%

Section: Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

Bao

Zhang

Huang

et al. 2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Viscous fluid damper is a passive control device (Biswal et al, 2022; Khedmatgozar Dolati et al, 2021; Rajeswaran and Wijeyewickrema, 2022; Zhou et al, 2016). Its basic principle is that the fluid inside the damper generates throttling resistance through a specific hole or device clearance and then dissipates the energy input to the damper from the outside.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of a novel high performance multi-walled carbon nano-polyvinylpyrrolidone/silicon-based shear thickening fluid damper

Sun,

Wang,

Zhang

2024

Journal of Intelligent Material Systems and Structures

View full text Add to dashboard Cite

A novel high performance multi-walled carbon nano-polyvinylpyrrolidone/silicon-based shear thickening fluid (MWCNTs-PVP/SiO2-STF), abbreviated and subsequently referred to as MPS-STF, is developed in this paper. The rheological properties of the MPS-STF are investigated, and the viscosity model of MPS-STF is established. Furthermore, the MPS-STF based viscous fluid damper (MPS-STF-VFD) is designed according to the rheological characteristics of the novel fluid. The impact of loading frequencies, displacement amplitudes and the numbers of piston holes on the dynamic performance of the damper is studied through sophisticated multiple cases loading tests using MTS facility. The test results show that the loading frequency, displacement amplitudes and the number of piston holes have great influence on the rheological properties of MPS-STF. This directly affects the maximum damping force and heat dissipation capacity of MPS-STF-VFD. Finally, the mechanical model of the damper is established based on the principle of fluid mechanics. The simulation results agree well with the experimental data. The high damping performance of the MPS-STF-VFD can be achieved based on the characteristics of the modified fluid. Relevant results reported in this paper can provide an important solution for the development and application of damping technology in engineering structures.

show abstract

“…Montalva et al [24] conducted a study on how the damage level affects the damping in carbon fiber-reinforced laminates and concluded that the damping always increases overall with damage, but the individual variation of the modal damping factor is very uncertain. Biswal et al [25] proposed a virtual damper concept to update the system matrix of the finite element model, and the results show that the virtual damper is not only effective in estimating the time-domain response but also has the potential for condition identification of the bolted connection.…”

Section: Introductionmentioning

confidence: 99%

Support condition identification of wind turbines based on a statistical time-domain damping parameter

Liu,

Liang,

Wang

2023

Inverse Problems

Self Cite

View full text Add to dashboard Cite

Owing to the harsh environment, the support conditions of wind turbines inevitably degrade/change over their lifetime, however, the evolution mechanism is not yet well understood. Although the damping parameters are sensitive to structural support and connection conditions, they are difficult to measure and quantify, which is a challenging inverse problem. This study aims to develop an approach to obtain a statistical time-domain damping parameter (STDP) based on operational vibration signals, and to utilize the parameter to identify support conditions of wind turbines. The proposed approach transforms operational vibration signals to free vibration signals by using the random decrement technique and then performs nonparametric statistical analysis to quantify the statistically significant changes in the damping characteristics of a structure. The effectiveness of the STDP method is verified by two challenging cases of bolted connection damage and soil-structure interaction condition changes. The regression analysis demonstrates the ability of the STDP method for the identification of structural overall damping. In contrast with classic modal analysis methods, the proposed method provides a monotonic relationship between the STDP and support conditions, which is significant for structural condition identification.

show abstract

Condition identification of bolted connections using a virtual viscous damper

Cited by 11 publications

References 31 publications

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

Experimental investigation of a novel high performance multi-walled carbon nano-polyvinylpyrrolidone/silicon-based shear thickening fluid damper

Support condition identification of wind turbines based on a statistical time-domain damping parameter

Contact Info

Product

Resources

About