Model Updating to Forecast the Dynamic Behavior of the Ghirlandina Tower in Modena, Italy

Sabia, Donato; Aoki, Takayoshi; Cosentini, Renato Maria; Lancellotta, Renato

doi:10.1080/13632469.2014.962668

Cited by 21 publications

(10 citation statements)

References 23 publications

(20 reference statements)

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“…In this case, the tower is responding and adapting to the behavior of the soil, which was strongly consolidated during the big earthquake that occurred in Modena in May 2012. The earthquake, indeed, was characterized by a main vertical component , and the soil has gradually released the compression, accumulated in the form of interstitial pressure . As a consequence, the tower has suffered with a certain time latency the permanent residual component of vertical displacement, which was highlighted only by this analysis.…”

Section: Discussionmentioning

confidence: 82%

“…As a consequence, there was no way with the existing sensors to determine whether the tower experienced a permanent deformation or not until the present methodology put the residual vertical failure suffered by the structure into evidence. After the confirmation given by the following survey campaigns, where no raising was detected, the municipality, in accordance with the Scientific Committee, installed a number of accelerometers for a dynamic monitoring . In some way, it can be noticed that the proposed methodology highlighted the potential need of a dynamic monitoring system and supported the decisional process to implement it.…”

Section: Discussionmentioning

confidence: 92%

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Monitoring leaning towers by geodetic approaches: effects of subsidence and earthquake to the Ghirlandina Tower

Castagnetti

Bertacchini

Capra

2015

Struct. Control Health Monit.

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The research focuses on structural monitoring and movements identification applied to cultural heritage protection. The final purpose is the integration among different and independent technologies for analyzing and investigating the geometry changing over time of ancient leaning towers. The paper deals with a novel strategy implemented to compute differential vertical displacements starting from results obtained by repeated high-precision leveling network adjustments. These results usually aim at monitoring the subsidence phenomenon, while their use in engineering applications is more or less absent in literature. Moreover, the multidisciplinary approach is also able to analyze subsidence gradients in order to extrapolate the trend of the vertical axis and compute structure's rotations. The approach is applied to the Ghirlandina Tower, Modena (Italy), in order to identify the leaning and the subsidence trend. About 30 years of leveling campaigns provide a useful dataset to test the methodology, which is finally validated by the independent observations collected by a pendulum. The approach allows to compute the mean total displacement since 1984 of about 4.7 cm with 30% occurring over the last 6 years. In the same period, the total overhang of the tower (1.30 m in 2007) increased by about 19.1 and 10.4 mm towards southwest. The approach is also able to identify anomalous behavior of the tower such as the reversal tilting trend due to the scaffolding in the years of restoration and the permanent deformation suffered after the 2012 Emilia Romagna earthquake (failure of 4 mm in 6 months).

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 92%

Monitoring leaning towers by geodetic approaches: effects of subsidence and earthquake to the Ghirlandina Tower

Castagnetti

Bertacchini

Capra

2015

Struct. Control Health Monit.

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“…The results obtained showed the interaction with the cathedral and the important contribution of the soil-structure interaction on the tower's modes of vibration. [28][29][30][31] During the same year, a sensor network made of 12 capacitive accelerometers and 3 thermocouples was permanently installed on the tower, in order to implement a monitoring system acquiring data continuously over a long period of time. The sensors were put at different heights, from the basement up to the cusp (Figure 2).…”

Section: The Long-term Monitoring Of the Ghirlandina Towermentioning

confidence: 99%

A machine learning approach for the automatic long-term structural health monitoring

Demarie

Sabia

2018

Structural Health Monitoring

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Measuring the response of a structure to the ambient and service loads is a source of information that can be used to estimate some important engineering parameters or, to a certain extent, to characterize the structural behavior as a whole. By repeating the data acquisition over a period of time, it is possible to check for variations in the structure’s response, which may be correlated to the appearance or growth of a damage (e.g. following some exceptional event as the earthquake, or as a consequence of materials and components aging). The complexity of some existing structures and their environment very often requires the execution of a monitoring plan in order to support analyses and decisions through the evidence of measured data. If the monitoring is implemented through a sensor network continuously acquiring over time, then the evolution of the structural behavior may be tracked continuously as well. Such approach has become a viable option for practical applications since the last decade, as a consequence of the progress in the data acquisition and storage systems. However, proper methods and algorithms are needed for managing the large amount of data and the extraction of valuable knowledge from it. This article presents a methodology aimed at making automatic the process of structural monitoring in case it is carried continuously over time. It relies on some existing methods from the machine learning and data mining fields, which are casted into a process targeted to delimit the need of the human being intervention to the training phase and the engineering judgment of the results. The methodology has been successfully applied to the real-world case of an ancient masonry bell tower, the Ghirlandina Tower (Modena, Italy), where a network made of 12 accelerometers and 3 thermocouples has been acquiring continuously since August 2012. The structural characterization is performed by identifying the first modes of vibration, whose evolution over time has been tracked.

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“…Being non-destructive, such techniques perfectly meet the well-known minimum intervention and reversibility criteria for CH assets [5]. Through dynamic identification, vibrational properties of the structure (natural frequencies, modal shapes and damping coefficients) can be deduced and compared to the ones obtained from numerical models [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Modal Identification of the San Francesco Church in Pisa, Italy

Lorenzo¹,

Canessa²,

Chellini³

et al. 2020

XI International Conference on Structural Dynamics

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Model Updating to Forecast the Dynamic Behavior of the Ghirlandina Tower in Modena, Italy

Cited by 21 publications

References 23 publications

Monitoring leaning towers by geodetic approaches: effects of subsidence and earthquake to the Ghirlandina Tower

Monitoring leaning towers by geodetic approaches: effects of subsidence and earthquake to the Ghirlandina Tower

A machine learning approach for the automatic long-term structural health monitoring

Modal Identification of the San Francesco Church in Pisa, Italy

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