Discrete and Continuous Finite Element Models and Their Calibration via Vibration and Material Tests for the Seismic Assessment of Masonry Structures

Erdoğan, Yıldırım Serhat

doi:10.1080/15583058.2017.1332255

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…The learning process in this neural network is to optimize the functional generator f . Equation (15) shows that it is necessary to minimize the deviation ∆x t+1 between the estimated value and the monitored real data to bring the generated valuex t+1 close to the real one x t+1 .…”

Section: Mathematical Descriptionmentioning

confidence: 99%

“…The output model resulting from the ambient vibration experiment calibrated the FEA model and improved the correlation between the computational and experimental parameters. Erdogan calibrated the discrete and continuous FEA models thorough the combination of vibration and material tests for the purpose of the seismic assessment [ 15 ]. The procedure considers the stiffness of contact between adjacent stone units.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Bao

Chen

et al. 2020

Sensors

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The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.

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Section: Mathematical Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Bao

Chen

et al. 2020

Sensors

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“…Application of FE model updating to ancient masonry buildings is relatively recent. In [4,5,12,13,15,20,22,24,27,33,37,38,43] a vibration-based model updating is conducted, and preliminary FE models are tuned by using the dynamic characteristics determined through system identification techniques. In the papers cited above the modal analysis of the FE models are conducted via commercial codes, and the model updating procedure is implemented separately.…”

Section: Introductionmentioning

confidence: 99%

Finite element model updating for structural applications

Girardi

Padovani

Pellegrini

et al. 2020

Journal of Computational and Applied Mathematics

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A novel method for performing model updating on finite element models is presented. The approach is particularly tailored to modal analyses of buildings, by which the lowest frequencies, obtained by using sensors and system identification approaches, need to be matched to the numerical ones predicted by the model. This is done by optimizing some unknown material parameters (such as mass density and Young's modulus) of the materials and/or the boundary conditions, which are often known only approximately. In particular, this is the case when considering historical buildings.The straightforward application of a general-purpose optimizer can be impractical, given the large size of the model involved. In the paper, we show that, by slightly modifying the projection scheme used to compute the eigenvalues at the lowest end of the spectrum one can obtain local parametric reduced order models that, embedded in a trust-region scheme, form the basis for a reliable and efficient specialized algorithm.We describe an optimization strategy based on this approach, and we provide numerical experiments that confirm its effectiveness and accuracy.

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“…The above procedure allows to identify reliable and robust FE models to be subsequently employed for different structural purposes. Many of the studies available in literature report on applications of AVT and OMA with multiple goals, such as damage assessment (Betti et al, 2015;Clementi et al, 2017;Alkayem et al, 2018), seismic vulnerability assessment (Bartoli et al, 2015;Erdogan, 2017), structural health assessment and monitoring (Castellazzi et al, 2012;Ramos et al, 2013;Pieraccini et al, 2017;Bassoli et al, 2018), modal updating and identification (Aoki et al, 2008;Chiorino et al, 2011;Torres et al, 2017;Girardi et al, 2019;Pavlovic et al, 2019), bell swinging effects assessment (Diaferio et al, 2017;Bru et al, 2019) and evaluation of the effectiveness of retrofitting (Pierdicca et al, 2019). While the vibration-based structural assessment is today a methodology particularly appreciated in the field of historic structures (because of its non-invasiveness and non-destructiveness) several issues still arise concerning the dynamic test setups to be adopted for each building typology.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Identification as a Tool to Constrain Numerical Models for Structural Analysis of Historical Buildings

Lacanna

Betti

Ripepe

et al. 2020

Front. Built Environ.

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Operational modal analysis (OMA), also known as output-only or ambient vibration test (AVT), has become in last decades a powerful approach for a wide range of applications in the field of civil engineering. When historical structures are investigated this approach is particularly appealing avoiding shaking the structure artificially. This paper discusses the assessment of the dynamic behavior of the Baptistery of San Giovanni in Firenze (Italy). Based on experimental results obtained through a temporary network of seismic sensors, the enhanced frequency domain decomposition (EFDD) technique is employed to evaluate frequencies and mode shapes. These modal parameters are subsequently used to calibrate a 3D finite element (FE) model of the Baptistery. Genetic algorithm (GA) technique is employed for calibration, thus allowing to obtain an accurate and robust numerical model. To verify the effects introduced by the number of identified modal parameters on the model updating procedure several analyses are in addition performed. This paper, providing an illustrative case study in the field of health monitoring of monumental structures, confirms that the OMA technique is able to derive effective information on the dynamic behavior of historical buildings, which in turn is useful to tune reliable and robust numerical models to be employed for structural analysis.

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Discrete and Continuous Finite Element Models and Their Calibration via Vibration and Material Tests for the Seismic Assessment of Masonry Structures

Cited by 9 publications

References 33 publications

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Finite element model updating for structural applications

Dynamic Identification as a Tool to Constrain Numerical Models for Structural Analysis of Historical Buildings

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