Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms

Zar, Ali; Kang, Fei; Li, Junjie; Wu, Yingrui

doi:10.1007/s40996-022-00906-w

Cited by 11 publications

(1 citation statement)

References 51 publications

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“…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. These studies have enriched the structural health monitoring study of concrete dams under seismic loading.…”

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

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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.

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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

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Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

Zar,

Hussain,

Akbar

et al. 2024

Int J Mech Mater Des

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In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

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A Two-Stage Method for Damage Identification in Concrete Gravity Dams by Using a Modal Curvature-Based Index and Pathfinder Algorithm

Mohebian,

Motahari,

Aval

2023

Arab J Sci Eng

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Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms

Cited by 11 publications

References 51 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

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

A Two-Stage Method for Damage Identification in Concrete Gravity Dams by Using a Modal Curvature-Based Index and Pathfinder Algorithm

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