Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

Cheng, Lin; Tong, Fei; Zheng, Dongjian

doi:10.1155/2019/7513261

Cited by 11 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, some studies have focused on identifying dynamic parameters and damage in concrete dam structures. For example, Lin Cheng et al [ 22 ] proposed an online modal parameter identification method for concrete dams using the subspace tracking-based technique, and a newly developed recursive stochastic subspace identification method based on the generalized subspace tracker algorithm was used to obtain the time-varying modal parameters of concrete dams during earthquakes. Zar Ali et al [ 23 ] developed a damage identification method for concrete arch dams based on vibration analysis, employing least-square support vector machines and salp swarm algorithms, and a numerical arch dam model example was used to verify the effectiveness of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Failure Experimental Study of a Gravity Dam Model on a Shaking Table and Analysis of Its Structural Dynamic Characteristics

Qiu,

He,

Zheng

et al. 2024

Sensors

View full text Add to dashboard Cite

Investigating the dynamic response patterns and failure modes of concrete gravity dams subjected to strong earthquakes is a pivotal area of research for addressing seismic safety concerns associated with gravity dam structures. Dynamic shaking table testing has proven to be a robust methodology for exploring the dynamic characteristics and failure modes of gravity dams. This paper details the dynamic test conducted on a gravity dam model on a shaking table. The emulation concrete material, featuring high density, low dynamic elastic modulus, and appropriate strength, was meticulously designed and fabricated. Integrating the shaking table conditions with the model material, a comprehensive gravity dam shaking table model test was devised to capture the dynamic response of the model under various dynamic loads. Multiple operational conditions were carefully selected for in-depth analysis. Leveraging the dynamic strain responses, the progression of damage in the gravity dam model under these diverse conditions was thoroughly examined. Subsequently, the recorded acceleration responses were utilized for identifying dynamic characteristic parameters, including the acceleration amplification factor in the time domain, acceleration response spectrum characteristics in the frequency domain, and modal parameters reflecting the inherent characteristics of the structure. To gain a comprehensive understanding, a comparative analysis was performed by aligning the observed damage development with the identified dynamic characteristic parameters, and the sensitivity of these identified parameters to different levels of damage was discussed. The findings of this study not only offer valuable insights for conducting and scrutinizing shaking table experiments on gravity dams but also serve as crucial supporting material for identifying structural dynamic characteristic parameters and validating damage diagnosis methods for gravity dam structures.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Failure Experimental Study of a Gravity Dam Model on a Shaking Table and Analysis of Its Structural Dynamic Characteristics

Qiu,

He,

Zheng

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…These parameters are fundamental indicators that provide insights into how a structure responds and behaves under dynamic loading conditions. They provide inherent features of the structure, carry clear physical significance, and serve as vital reference points for structural damage identification, model refinement, and the optimization design of structural dynamic characteristics [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

Zhu,

Qiu,

et al. 2024

Sensors

View full text Add to dashboard Cite

Modal parameter estimation is crucial in vibration-based damage detection and deserves increased attention and investigation. Concrete arch dams are prone to damage during severe seismic events, leading to alterations in their structural dynamic characteristics and modal parameters, which exhibit specific time-varying properties. This highlights the significance of investigating the evolution of their modal parameters and ensuring their accurate identification. To effectively accomplish the recursive estimation of modal parameters for arch dams, an adaptive recursive subspace (ARS) method with variable forgetting factors was proposed in this study. In the ARS method, the variable forgetting factors were adaptively updated by assessing the change rate of the spatial Euclidean distance of adjacent modal frequency identification values. A numerical simulation of a concrete arch dam under seismic loading was conducted by using ABAQUS software, in which a concrete damaged plasticity (CDP) model was used to simulate the dam body’s constitutive relation, allowing for the assessment of damage development under seismic loading. Utilizing the dynamic responses obtained from the numerical simulation, the ARS method was implemented for the modal parameter recursive estimation of the arch dam. The identification results revealed a decreasing trend in the frequencies of the four initial modes of the arch dam: from an undamaged state characterized by frequencies of 0.910, 1.166, 1.871, and 2.161 Hz to values of 0.895, 1.134, 1.842, and 2.134 Hz, respectively. Concurrently, increases in the damping ratios of these modes were observed, transitioning from 4.44%, 4.28%, 5.42%, and 5.56% to 4.98%, 4.91%, 6.61%, and 6.85%%, respectively. The correlation of the identification results with damage progression validated the effectiveness of the ARS method. This study’s outcomes have substantial theoretical and practical importance, facilitating the immediate comprehension of the dynamic characteristics and operational states of concrete arch dam structures.

show abstract

“…However, as discussed above, concrete dams exhibit time-variable dynamic properties when the dam is damaged during an earthquake, and some researchers have attempted to track time-variable modal parameter and the damage state evolution. For example, Cheng et al 31 proposed a new recursive stochastic subspace identification (RSSI) method based on the generalized yet another subspace tracker to track the time-variable modal parameters of concrete dams during earthquakes, and the proposed method was verified by tracking the time-variable characteristics of the Pacoima arch dam during three different earthquakes. Li et al 32 proposed an online modal parameter identification procedure based on the SSI algorithm and density-based spatial clustering of applications with noise (DBSCAN) algorithm for arch dams, and the proposed method was verified by the Dagangshan Dam.…”

Section: Introductionmentioning

confidence: 99%

Online structural damage state identification of concrete arch dams under dynamic loads using a recursive TVARX approach

Qiu

Zheng

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Concrete arch dams have been widely constructed worldwide, and many of these dams are located in areas with high seismic activity. However, strong seismic loading poses a severe threat to concrete arch dams, and dams have the risk of suffering damage and the possibility of an ensuing dam break. In past decades, seismic and structural health monitoring has undergone rapid growth in the context of arch dams to track the operation state of arch dams under strong seismic loading. In fact, arch dams with damage caused by seismic loading normally exhibit nonlinear dynamic behaviors and time-variable dynamic properties. To effectively identify the structural damage state development of arch dams under seismic loading, an online damage identification method using a novel recursive time-variable autoregressive with exogenous (TVARX) input model was presented in this paper. To improve the tracking capability for the time-variable coefficients of the TVARX model, an adaptive adjustment algorithm of variable weighting factors was incorporated into the recursive least-squares method. In the proposed method, the predicted model residual, a damage index named the fit ratio (FR), the difference between fit ratios (DF), and a damage index based on the change in ARX model parameters (CH) were proposed to identify the presence of damage, damage area, and relative damage extent for arch dams. Eventually, the proposed approach was used for the online damage state identification of arch dams in a numerical simulation example and a shaking table test, and the identification results demonstrated the effectiveness of the proposed approach.

show abstract

Online Modal Identification of Concrete Dams Using the Subspace Tracking‐Based Method

Cited by 11 publications

References 26 publications

Dynamic Failure Experimental Study of a Gravity Dam Model on a Shaking Table and Analysis of Its Structural Dynamic Characteristics

Dynamic Failure Experimental Study of a Gravity Dam Model on a Shaking Table and Analysis of Its Structural Dynamic Characteristics

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

Online structural damage state identification of concrete arch dams under dynamic loads using a recursive TVARX approach

Contact Info

Product

Resources

About