Maximum Likelihood Finite-Element Model Updating of Civil Engineering Structures Using Nature-Inspired Computational Algorithms

Jiménez‐Alonso, Javier Fernando; Naranjo-Pérez, Javier; Pavić, Aleksandar; Sáez, Andrés

doi:10.1080/10168664.2020.1768812

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…For acceleration-based EDMF, falsification thresholds were defined for natural frequencies and MAC values of the mode shapes for all four modes. Threshold values were defined iteratively, initially allowing natural frequencies to deviate ±5% relative to the experimental values, and the absolute MAC values being at least 0.90, following the recommendation by [53] of a 'good correlation' between the FE model and experiment. For strain-based EDMF, the initial falsification thresholds were set to ±5% relative to the experimental values in sensors SG_01, SG_02, SG_03, and SG_04.…”

Section: Femu Results: Residual Minimisationmentioning

confidence: 99%

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data

Hekič,

Ribeiro,

Anžlin

et al. 2024

Sensors

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Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young’s modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure’s design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25–50% overestimated design stiffness of internal main girders.

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Section: Femu Results: Residual Minimisationmentioning

confidence: 99%

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data

Hekič,

Ribeiro,

Anžlin

et al. 2024

Sensors

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“…The first type returns a single value of each considered parameter and the second type returns an interval of possible values of each considered parameter. The maximum likelihood method, a point estimator, has been considered herein to perform this problem due to its efficiency and accuracy in the solution of the updating problem (Jiménez-Alonso et al, 2017).…”

Section: Parameter Identification Methods Of Dynamic Systems Using a Hmentioning

confidence: 99%

“…More recently, Naranjo-Perez et al (2020) proposed a hybrid UKF–harmony search (HS) algorithm for the FE model updating of complex civil engineering structures. This hybrid algorithm combined the virtues of two individual algorithms as follows: (1) the (local) UKF algorithm, which reduces the uncertainty associated with the parameter identification based on experimental data; and (2) the (global) HS algorithm (Geem et al, 2001), which reduces significantly simulation times when compared to other classical metaheuristic algorithms (Jiménez-Alonso et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

Naranjo-Pérez

Jiménez‐Alonso

Sáez

2020

Advances in Structural Engineering

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Soil–structure interaction is a key aspect to take into account when simulating the response of civil engineering structures subjected to dynamic actions. To this end, and due to its simplicity and ease of implementation, the dynamic Winkler model has been widely used in practical engineering applications. In this model, soil–structure interaction is simulated by means of spring–damper elements. A crucial point to guarantee the adequate performance of the approach is to accurately estimate the constitutive parameters of these elements. To this aim, this article proposes the application of a recently developed parameter identification method to address such problem. In essence, the parameter identification problem is transformed into an optimization problem, so that the parameters of the dynamic Winkler model are estimated by minimizing the relative differences between the numerical and experimental modal properties of the overall soil–structure system. A recent and efficient hybrid algorithm, based on the combination of the unscented Kalman filter and multi-objective harmony search algorithms, is satisfactorily implemented to solve the optimization problem. The performance of this proposal is then validated via its implementation in a real case-study involving an integral footbridge.

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“…Another possible classification is to divide iterative methods into deterministic and stochastic methods [21]. The iterative deterministic maximum likelihood method transforms the model updating problem into an optimization problem, and the objective function is defined in terms of residuals between different types of numerically and experimentally obtained data sets; most often, natural frequencies and mode shapes are used [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Mass and Stiffness Correlation Using a Transformation Matrix

García Fernández,

Fernández Fernandez,

Brincker

et al. 2024

Infrastructures

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Model correlation techniques are methods used to compare two different models, usually a numerical model and an experimental model. According to the structural dynamic modification theory, the experimental mode shapes estimated by modal analysis can be expressed as a linear combination of the numerical mode shapes through a transformation matrix T. In this paper, matrix T is proposed as a novel model correlation technique to detect discrepancies between the numerical and the experimental models in terms of mass. The discrepancies in stiffness can be identified by combining the numerical natural frequencies and the matrix T. This methodology can be applied to correlate the numerical and experimental results of civil (bridges, dams, towers, buildings, etc.), aerospace and mechanical structures and to detect damage when using structural health monitoring techniques. The technique was validated by numerical simulations on a lab-scaled two-span bridge considering different degradation scenarios and experimentally on a lab-scaled structure, which was correlated with two numerical models.

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Maximum Likelihood Finite-Element Model Updating of Civil Engineering Structures Using Nature-Inspired Computational Algorithms

Cited by 13 publications

References 32 publications

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

Mass and Stiffness Correlation Using a Transformation Matrix

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