Dynamic condensation approach to the calculation of eigensensitivity

Weng, Shun; Zhu, Ai-Zhu; Zhu, Hongping; Xia, Yong; Mao, Ling; Li, Penghui

doi:10.1016/j.compstruc.2013.10.012

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…It is known that the modeling difficulty and computational complexity will increase with the expansion of physical geometry size, especially for some full-scale bridges and high-rise buildings. These problems are always reflected in the following aspects: (1) increasing nodes and elements in the FEM cause the sizes of structural stiffness and mass matrices to be very large, then the existing solution method cannot meet the requirements, further inducing the incorrect results [42]; and (2) the iterative calculations in the damage identification need to repeatedly assemble the structural overall stiffness matrix, the more complex structure, the lower convergence speed in the optimization process.…”

Section: The Proposed Damage Identification Methodmentioning

confidence: 99%

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Chen,

Zhang,

Yuan

2024

Advances in Civil Engineering

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Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity-based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full-scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full-scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full-scale engineering structure.

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Section: The Proposed Damage Identification Methodmentioning

confidence: 99%

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Chen,

Zhang,

Yuan

2024

Advances in Civil Engineering

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“…A sophisticated long-term SHM system has been established in the tower since the construction period and become a SHM benchmark to supertall structures. 38–41 After attempts and comparisons of different approaches, the GPS system is chosen as a practical way to monitor structural displacement in long term. The accuracy of the GPS-measured displacement is sometimes not sufficient in extreme weathers such as storm and typhoon.…”

Section: Field Experiments On a Supertall Structurementioning

confidence: 99%

Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors

Zhu

Gao

Xia

et al. 2019

Structural Health Monitoring

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Accurate measurement of dynamic displacement is important for the structural health monitoring and safety assessment of supertall structures. However, the displacement of a supertall structure is difficult to be accurately measured using the conventional methods because they are either inaccurate or inconvenient to be set up in practice. This study provides an accurate and economical method to measure dynamic displacement of supertall structures accurately by fusing acceleration and strain data, which are generally available in the structural health monitoring system. Dynamic displacement is first derived from the measured longitudinal strains based on geometric deformation without requiring mode shapes. An optimization technique is utilized to optimize the deployment of strain sensors for achieving more accurate strain-derived displacement. The strain-derived displacement is then combined with measured acceleration via a multi-rate Kalman filtering approach. Applications to a numerical supertall structure and a laboratory cantilever beam verify that the proposed method accurately estimates displacement including both high-frequency and pseudo-static components, under different noise cases and sampling rates. A full-scale field test on the 600 m-high Canton Tower is implemented to validate the applicability of the proposed method to real supertall structures. Error analysis demonstrates that the data fusion displacement is more accurate than the global position system-measured displacement in the time and frequency domains.

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“…Dynamic analysis and damage identification based on a large model usually takes up considerable computation storage and is time-consuming. Many strategies have been developed to improve the computational efficiency of model-based methods, such as substructuring methods (Weng et al, 2009;Zhu et al, 2013), model condensation methods (Weng et al, 2014(Weng et al, , 2017Xia and Lin, 2004) and so on. These simplifications on FE model are difficult to release computational load essentially, and the model-based approaches are inherently computationally inefficient.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering of time-series model to damage identification of structures

Zeng

Yan

Weng

et al. 2018

Advances in Structural Engineering

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Time-series methods have been popularly used for damage identification of civil structure because of its output-only and non-model approach. Since the existence of structural damage is usually vague and not focussed on any particular time point, the switches in damage patterns from one time state to another are necessary to be treated in a fuzzy way. This article develops a damage identification method based on the fuzzy clustering of time-series model. The changes of model coefficients of time-series model are proposed to indicate the undamaged and damaged states by the fuzzy c-means clustering algorithm. The residual errors of time-series model are used to identify the damage location and damage severity. The proposed method is applied to an experimental segment lining and a numerical study of a practical bridge. The results verify that the proposed method is accurate and efficient to detect the structural damage location and severity. Since the computational process of time-series model and fuzzy clustering require low computational cost, the proposed data-based damage identification method is applicable to the online structural health monitoring system of large-scale civil structures.

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Dynamic condensation approach to the calculation of eigensensitivity

Cited by 17 publications

References 26 publications

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors

Fuzzy clustering of time-series model to damage identification of structures

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