Structural system identification of thin web bridges by observability techniques considering shear deformation

Tomàs, Daniel; Galant, José Antonio Lozano; Ramos, Gonzalo; Coderque, José Turmo

doi:10.1016/j.tws.2017.11.017

Cited by 18 publications

(15 citation statements)

References 51 publications

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“…Later, structural system identification by numerical OM (NOM) was proposed by Nogal [21] to carry out numerical estimation of target parameters. Recently, Tomás et al [22] and Emadi et al [23] developed this method to deal with actual measurements on site, including shear deformations. The effects of measurement errors and simulation errors on the estimation accuracy were investigated in [24].…”

Section: Introduction 1existing Structural System Identification Methodsmentioning

confidence: 99%

Robust Static Structural System Identification Using Rotations

Lei

Galant

et al. 2021

Applied Sciences

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Deflections are commonly measured in the static structural system identification of structures. Comparatively less attention has been paid to the possibility of measuring rotations for structural system identification purposes, despite the many advantages of using inclinometers, such as a high resolution and being reference free. Although some work using rotations can be found in the literature, this paper, for the very first time, proposes a statistical analysis that justifies the theoretical advantage of measuring rotations. The analytical expressions for the target parameters are obtained via static structural system identification using the constrained observability method first. Combined with the inverse distribution theory, the probability density function of the estimations of the target parameters can be obtained. Comparative studies on a simply supported bridge and a frame structure demonstrate the advantage of measuring rotations regarding the unbiasedness and the extent of variation in the estimations. To achieve robust parameter estimations, four strategies to use redundant rotations are proposed and compared. Numerical verifications on a bridge structure and a high-rise building have shown promising results.

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Section: Introduction 1existing Structural System Identification Methodsmentioning

confidence: 99%

Robust Static Structural System Identification Using Rotations

Lei

Galant

et al. 2021

Applied Sciences

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“…In the numerical optimization, the objective function is to minimize the square sum of the residuals (equilibrium forces) of the observability equations from the last recursive step in SSI by OM while forcing the equality between the coupled variable and the product of its components . This method is used to find the essential sets in static SSI …”

Section: Methodsmentioning

confidence: 99%

“…According to the intrinsic characteristics of the structural response, SSI can be classified as static SSI or dynamic SSI . Compared with static SSI, dynamic SSI has been developed more extensively in the past decades.…”

Section: Introductionmentioning

confidence: 99%

“…It is commonly assumed that the degradation of structures is reflected in the change of these parameters. 2 According to the intrinsic characteristics of the structural response, SSI can be classified as static SSI [3][4][5][6][7][8][9][10][11][12][13][14] or dynamic SSI. [15][16][17][18][19][20] Compared with static SSI, dynamic SSI has been developed more extensively in the past decades.…”

Section: Introductionmentioning

confidence: 99%

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Structural system identification by measurement error‐minimizing observability method

Lei

Galant

et al. 2019

Struct Control Health Monit

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Summary This paper proposes a method for the finite element model updating using static load tests under the framework of observability analysis. Previous works included measurement errors in the coefficient matrix of the observability equations. This impeded the obtainment of accurate estimations. To deal with this issue, the proposed method relocates the errors and incorporates an optimization procedure to minimize the square sum of these errors. This method is able to identify the structural parameters of complex structures where the axial and bending behaviors are coupled, such as inclined beams or frame structures. Its application is illustrated by three structures. First, the method was validated in a beam‐like structure by comparing it with other methods in the literature. Then the effects of different factors were investigated in a multistory frame and a rigid frame bridge with inclined piers. These factors include the curvatures, the inclusion of rotation measurements, and the constraints on the range of unidentifiable parameters. The importance of rotation measurements is demonstrated in static structural system identification.

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“…The equations of static equilibrium derived from the FE method lead to a nonlinear system where stiffness are unknowns. Tomas et al (2018) apply observability to this system for establishing the subset of structural parameters that can be identified with a given subset of measures. The problem of finding the stiffness for each element is addressed via an optimization cross-entropy algorithm in Walsh and González (2009) and González et al (2013) for a beam and in Walsh et al (2010) for a plate model.…”

Section: Introductionmentioning

confidence: 99%

Regularization Methods Applied to Noisy Response from Beams under Static Loading

2020

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The estimation of flexural stiffness from static loading test data is the basis of many methods assessing the condition of structural elements. These methods are usually developed under the assumption of having sufficiently accurate data available. Hence, their performance deteriorates as the differences between the measured and true values of the response, often denoted as noise, increase. The proposed methodology is specifically designed to mitigate errors derived from noisy static data when estimating flexural stiffness. It relies on the linearization of the equations relating displacements to stiffness through the unit-force theorem, combined with regularization tools such as Lcurve and generalized cross-validation. The methodology is tested using theoretical simulations of the static response of a simply supported beam subjected to a 4-point flexural test for several levels of noise, two types of responses (deflections and rotations) and different levels of discretization. Recommendations for selecting the optimal regularization tool and parameter are provided. The use of rotations as inputs for predicting stiffness is shown to outperform deflections. Finally, the methodology is extended to a statically indeterminate beam.

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Structural system identification of thin web bridges by observability techniques considering shear deformation

Cited by 18 publications

References 51 publications

Robust Static Structural System Identification Using Rotations

Robust Static Structural System Identification Using Rotations

Structural system identification by measurement error‐minimizing observability method

Regularization Methods Applied to Noisy Response from Beams under Static Loading

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