System identification of steel framed structures with semi-rigid connections

Katkhuda, Hasan; Dwairi, Hazim M.; Shatarat, Nasim

doi:10.12989/sem.2010.34.3.351

Cited by 26 publications

(15 citation statements)

References 22 publications

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“…Step 5: formulate the dynamic equation of sub-structure as a matrix form. More details for the derivation can be found in the study of Wang and Haldar (1994) and Katkhuda et al (2010). The matrix form equation can be expressed as: where [ B ] is a matrix of size (3× n )× nes ; n is the total number of sample time points; and nes the total number of elements and damping coefficients in the sub-structure.…”

Section: Algorithm Developmentmentioning

confidence: 99%

“…The main objective of this paper is to detect the damages in steel structures that have a high number of DOF with less number of accelerometer sensors compared with other methods available in the literature (Wang and Haldar, 1994; Ling and Haldar, 2004; Vo and Haldar, 2004; Katkhuda et al , 2005; Wu and Smyth, 2007; Katkhuda et al , 2010). The two-dimensional steel-framed structures are modeled with semi-rigid connections to represent the real behavior of such structures.…”

Section: Introductionmentioning

confidence: 99%

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Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

Katkhuda

Shatarat

Hyari

2017

IJSI

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Purpose The purpose of this paper is to detect damages in steel structures with actual connections, i.e. semi-rigid connections. The method will detect the damages by tracking the changes in the stiffness of structural members using only a limited number of dynamic responses and without knowing the type or time history of the dynamic force applied on the structure. Design/methodology/approach The paper proposes a technique that combines the iterative least-square and unscented Kalman filter (UKF) methods to identify the stiffness of beams and columns in typical two-dimensional steel-framed structures with semi-rigid connections. The detection of damages is by using nonlinear time-domain structural health monitoring method. Findings The technique is verified by using numerical examples using noise-free and noise-included dynamic responses from two different types of dynamic forces: harmonic and blast loads. The results showed that the UKF method with iterative least-square is a powerful approach to identify and detect damages in structures that have nonlinear behavior and the method was able to detect the damages in beams with a very high accuracy for noise-free and noise-included dynamic responses. In addition, the optimum number and locations of dynamic responses (accelerometer sensors) required for damage detection were determined. Originality/value This paper fulfills an identified need to detect damages in steel structures using only a limited number of accelerometer sensors.

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Section: Algorithm Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

Katkhuda

Shatarat

Hyari

2017

IJSI

Self Cite

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“…As can be observed in the previous studies, different structural responses with some advantages and drawbacks have been used to identify damage [15][16][17]. The natural frequencies are severely susceptible to environmental changes such as temperature and humidity.…”

Section: Introductionmentioning

confidence: 99%

Damage identification in connections of moment frames using time domain responses and an optimization method

Pahnabi

Seyedpoor

2021

Front. Struct. Civ. Eng.

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Damage is defined as changes to the material and/or geometric properties of a structural system, comprising changes to the boundary conditions and system connectivity, adversely affecting the system's performance. Inspecting the elements of structures, particularly critical components, is vital to evaluate the structural lifespan and safety. In this study, an optimization-based method for joint damage identification of moment frames using the timedomain responses is introduced. The beam-to-column connection in a metallic moment frame structure is modeled by a zero-length rotational spring at both ends of the beam element. For each connection, an end-fixity factor is specified, which changes between 0 and 1. Then, the problem of joint damage identification is converted to a standard optimization problem. An objective function is defined using the nodal point accelerations extracted from the damaged structure and an analytical model of the structure in which the nodal accelerations are obtained using the Newmark procedure. The optimization problem is solved by an improved differential evolution algorithm (IDEA) for identifying the location and severity of the damage. To assess the capability of the proposed method, two numerical examples via different damage scenarios are considered. Then, a comparison between the proposed method and the existing damage identification method is provided. The outcomes reveal the high efficiency of the proposed method for finding the severity and location of joint damage considering noise effects.

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“…Under prolonged cyclic loading, the threaded bolts may also get loosened, affecting the joint stiffness. Especially for the steel structures, damage in joint is observed to be more frequent than member failure 1,2 . This is due to the design philosophy that ascertains the joints to fail before the members.…”

Section: Introductionmentioning

confidence: 99%

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

Aswal

Sen

Mevel

2022

Structural Contr & Hlth

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To avert catastrophic failure in the structures, joints are typically designed to yield, but not fail, so that energy accumulated under cyclic loading is dissipated. Eventually, this renders the structural joints to be characteristically weaker and more vulnerable than the members. Yet, damage detection research mostly assumes damage in the members only. This article proposes a model-based predictor-corrector algorithm that uses an interacting filtering approach to efficiently estimate joint damage in the presence of input and measurement uncertainties. For the predictor model, a novel straindisplacement relationship specific to semi-rigid frames is developed to map nodal displacements to corresponding strain measurements. The proposed estimation method embeds robustness against non-stationary input (e.g., seismic excitation) in the state filter, itself. For this, an output injection technique is integrated within the state filter. The modified state filter (robust Kalman filter) runs within an enveloping parameter filter (particle filter) to simultaneously estimate the system states and joint damage parameters, respectively, using the response signal. Strain has been adopted as measurement since it is frame independent (beneficial for seismic activity) and also comparatively cheaper to use. Numerical studies are performed on a two-dimensional (2-D) three-story three-bay shear frame for different joint damage locations and severities. The sensitivity and the stability of the proposed approach are further investigated. Experimental validation of the proposed algorithm is carried out on a 2-D steel frame.

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System identification of steel framed structures with semi-rigid connections

Cited by 26 publications

References 22 publications

Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

Damage identification in connections of moment frames using time domain responses and an optimization method

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

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