Global Sensitivity‐Based Model Updating for Heritage Structures

Boscato, Giosuè; Russo, Salvatore; Ceravolo, Rosario; Fragonara, Luca Zanotti

doi:10.1111/mice.12138

Cited by 76 publications

(33 citation statements)

References 48 publications

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“…In a first step, the elastic parameters of the building and of the soil (Figure 13b) have been calibrated In a second updating phase, the identification results for the SR building have been used to perform an automatized procedure of model updating for each different value of experimental frequencies. The FE updating procedure minimized a penalty function in terms of the first three modal frequencies (two flexural and a torsional mode) and MAC values [15,21] as proposed in equation (2). Because most FE software do not allow for a continuous variation of the elastic modulus, the law of variation for E(z) has been mapped as discrete regions of 5 m (see Figure 13).…”

Section: Finite Element Model Updating For the San Romano In Garfagnamentioning

confidence: 99%

Amplitude dependence of equivalent modal parameters in monitored buildings during earthquake swarms

Ceravolo

Matta

Quattrone

et al. 2017

Earthq Engng Struct Dyn

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Summary This paper investigates the dynamic response of three sample buildings belonging to the Seismic Observatory for Structures, the Italian network for the permanent seismic monitoring of strategic structures, managed by the Italian Department of Civil Protection. The case studies cover different building types that could loosely represent the Italian building stock, with a special emphasis on cultural heritage and masonry structures. Observed under a low‐intensity seismic swarm comprising about 30 aftershocks after a main event, the three buildings are analysed through an input–output, model‐driven linear dynamic identification procedure, depicting the relation between the shaking level at the site and the variation of the equivalent structural modal parameters, while keeping into account the effects of soil–structure interaction. Finite element models will be used to investigate one of the case studies and to compare the law of variation of the structural modal parameters with respect to simplified models proposed by technical standards. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Finite Element Model Updating For the San Romano In Garfagnamentioning

confidence: 99%

Amplitude dependence of equivalent modal parameters in monitored buildings during earthquake swarms

Ceravolo

Matta

Quattrone

et al. 2017

Earthq Engng Struct Dyn

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“…Recently, structural health monitoring (SHM) of structural systems has received considerable attention in civil engineering community and some useful results for evaluating structural integrity, durability, and reliability throughout the lifecycle of structures have been obtained (Park et al., ; Qarib and Adeli, ; Park et al., ; Amezquita‐Sanchez and Adeli, , ). In practical applications of SHM, system identification exhibits a critical component and extensive research has been conducted in this field (Jiang and Adeli, ; Adeli and Jiang, ; Amezquita‐Sanchez and Adeli, ; Qarib and Adeli, ; Sun et al., ; An et al., ; Yuen and Mu, ; Zhang et al., ; Oh et al., ; Sun and Betti, ; Boscato et al., ; Bolourchi et al., ; Cha and Buyukozturk, ; Shabbir and Omenzetter, ; Vincenzi and Savoia, ). To evaluate the performance of Taipei 101 Tower under typhoon and earthquake actions, a monitoring system was installed in the super‐tall building.…”

Section: Wind‐induced Response and Wind Loading Estimation Based On Tmentioning

confidence: 99%

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Zhi

Ming-xin

2016

Computer aided Civil Eng

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This article presents a Kalman‐filter‐based estimation algorithm for identification of wind loads on a super‐tall building using limited structural responses. In practice, acceleration responses are most convenient to be measured among wind‐induced dynamic responses of structures. The proposed inverse method allows estimating the unknown wind loads and structural responses of a super‐tall building using limited acceleration measurements. Taipei 101 Tower is a super‐tall building with 101 stories and a height of 508 m. Field measurements and numerical simulations of the wind effects on Taipei 101 Tower are conducted. The wind loads acting on the super‐tall building are estimated based on the wind‐induced responses determined from the numerical simulations and the refined finite‐element model of the structure, which are in good agreement with the exact results. The stability performance of the proposed algorithm is evaluated. The influence of noise levels in the measurements and covariance matrix of noise on the identification accuracy are investigated and discussed based on the L‐curve method. Finally, the wind loads and structural responses are reconstructed based on the field‐measured accelerations during Typhoon Matsa. The accuracy of the identified results is verified by comparing the reconstructed acceleration responses with the field measurements. The results of this study show that the proposed inverse approach can provide accurate predictions of the wind loads and wind‐induced responses of super‐tall buildings based on limited measured responses.

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“…Identification of modal parameters is important in the theoretical research and engineering applications of health monitoring, damage assessment, and the active control of bridge structures [17,18,19]. Given the increasing span and complexity of modern bridge structures, gaining effective excitation signals through human motivation (such as shakers and drop weights) is difficult, and may cause unnecessary damage to bridge structures [20].…”

Section: Introductionmentioning

confidence: 99%

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

Xiong

Zhu

2017

Sensors

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Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

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Global Sensitivity‐Based Model Updating for Heritage Structures

Cited by 76 publications

References 48 publications

Amplitude dependence of equivalent modal parameters in monitored buildings during earthquake swarms

Amplitude dependence of equivalent modal parameters in monitored buildings during earthquake swarms

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

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