Structural Health Monitoring of Architectural Heritage: From the past to the Future Advances

Clementi, Francesco; Formisano, Antonio; Ubertini, Filippo

doi:10.1080/15583058.2021.1879499

Cited by 20 publications

(8 citation statements)

References 13 publications

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“…Finally, the dynamic identification was performed employing the Stochastic Subspace Identification (SSI) technique. To briefly introduce the SSI methods, it is convenient to consider a time-invariant n-dof structural system, whose state-space equations of motion can be written as (1) where k denotes the time step; is the state vector; ℝ is the vector of the output measurements;…”

Section: Dynamic Identification From Ambient Vibration Tests (Avts)mentioning

confidence: 99%

“…If the main trends of the research [1] follow the employment of data-driven statistical methodologies aimed at unsupervised evaluations, not less important is the complementary employment of engineering-based model-driven approaches, relying on accurate mechanical models for the simulation of the structural behaviour. In the case of historical masonry structures, among the possible modelling strategies (reviewed in [2] [3] and discussed in [4] from a seismic engineering perspective), the common approach relies on refined Finite Element (FE) models, allowing for a detailed idealization of the structure.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Numerical Models to Support Shm: The Consoli Palace of Gubbio, Italy

Cattari¹,

Sivori²,

Alfano³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Historical masonry structures constitute a fundamental part of the built cultural heritage but are characterized by an intrinsic vulnerability to ageing and natural hazards, in particular to earthquakes. The related need to assess their current health condition and to ensure their future conservation is giving rise to increasing efforts in scientific research. The combined employment of health monitoring systems and structural modelling is widely adopted in this field, either to better interpret the effects of age-related degradation or to reliably predict the structural response to earthquakes. Both scenarios can leverage experimental measurements for the model calibration, thus reducing epistemic and aleatory uncertainties in the assessment phase. Among the available modelling strategies, refined Finite Element (FE) models represent the most common choice in the SHM perspective for monumental URM structures. Nonetheless, the computational effort required by the assessments in the nonlinear fieldunavoidable in seismic evaluationsis often unfeasible, especially in practice engineering. In the case of palaces, an alternative is the employment of more computationally efficient formulations such as Equivalent Frame (EF) models. Within this framework, the paper firstly deals with the equivalent-frame modelling and model updating of the Consoli Palace, a historic masonry building in Gubbio (Italy) investigated through ambient vibration tests. The peculiar aspects of the buildinge.g. the unusually high inter-storey height, the presence of vaulted floors, the irregular distribution of the openingsmake the equivalent-frame idealization a challenging task. The comparison with a detailed finite element model developed in previous research points out the differences and limits of the two approaches, providing some suggestions to benefit from their integrated use.

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Section: Dynamic Identification From Ambient Vibration Tests (Avts)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration of Numerical Models to Support Shm: The Consoli Palace of Gubbio, Italy

Cattari¹,

Sivori²,

Alfano³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

Self Cite

View full text Add to dashboard Cite

show abstract

“…The need to preserve and maintain historical buildings, structural heritage and civil infrastructures combined with improved safety standards has led to the increasing use of structural health monitoring (SHM) techniques [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The adoption of reliable and rigorous monitoring systems is fundamental to reducing maintenance costs and at the same time extending the service life of the existing structures.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Melchiorre

Bertetto

Rosso

et al. 2023

Sensors

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The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

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“…Moreover, researchers have been implementing digital technologies in various aspects of the safeguarding and preservation of CH. For instance, numerous research studies have been undertaken to develop accurate, complete, and multisource digital documentation of cultural heritage objects and sites for in-depth Structural Health Monitoring (SHM) analysis [4,5,6]. This is particularly important in the context of the adequate registration and documentation of objects as well as for object degradation analysis.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive System for In-Wall Moisture Assessment of Cultural Heritage Buildings

Muradov,

Kot,

Markiewicz

et al. 2022

SSRN Journal

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Structural Health Monitoring of Architectural Heritage: From the past to the Future Advances

Cited by 20 publications

References 13 publications

Calibration of Numerical Models to Support Shm: The Consoli Palace of Gubbio, Italy

Calibration of Numerical Models to Support Shm: The Consoli Palace of Gubbio, Italy

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Non-Destructive System for In-Wall Moisture Assessment of Cultural Heritage Buildings

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