leStrade n. 1956 aprile 2024

English Version
tri modali di ogni singola campata del viadotto.
I valori di strain, ottenuti con le medesime fibre,
consentono la continua ricostruzione della deformata
e l’individuazione di eventuali meccanismi
anomali, che vengono prontamente segnalati
dal sistema.
Similmente, per gli altri manufatti della rete autostradale,
l’identificazione dei parametri modali
e la ricostruzione della deformata vengono effettuati
mediante dati accelerometrici ed inclinometrici,
correlati alle temperature misurate.
L’accesso ai gemelli digitali corrispondenti alle
singole opere è facilitato attraverso una mappa
interattiva, che mostra la posizione del manufatto
sul territorio nazionale, permettendo di accedervi
con un semplice click e di effettuare una
vera e propria ispezione virtuale tridimensionale.
La piattaforma combina tecniche per aggiornamento
e calibrazione in tempo reale del gemello
digitale con un evoluto sistema per analisi predittiva,
basato su intelligenza artificiale “physics-informed”,
che permette di prevedere il comportamento
futuro dell’opera, sulla base dei dati
di monitoraggio e delle simulazioni eseguite.
Il sistema costituisce una tecnologia “auto-apprendente”,
predisposta per continuare ad “allenarsi”
sulla base delle serie storiche multivariate
ottenute dalla strumentazione, e dei risultati
delle analisi numeriche.
Queste informazioni sono consultabili in ogni
momento attraverso dashboard dedicate all’analisi
dei dati misurati ed ottenuti mediante simulazione
FEM, che visualizzano specifici set di
indicatori di salute strutturale per ciascuna opera
al momento attuale, ed il loro andamento previsto
per il futuro. Questo approccio consente
manutenzione predittiva, con una pianificazione
degli interventi ottimizzata sulla base delle
informazioni ottenute dal gemello digitale. nn
Fig. 16: Mappa interattiva
per accesso ai gemelli digitali
delle singole opere della
concessionaria.
Fig. 16: Interactive map for
accessing the digital twins
of the individual works
of the motorway operator.
real operational conditions. Unlike traditional modal analysis,
which requires the use of excitation sources like hammers
or shakers, OMA utilizes the natural vibrations present
in the structure, for example, those caused by wind or
traffic.
By processing data detected by accelerometers placed on
viaducts, the system is capable of determining the frequencies
associated with the main modes of the structure. The
system constantly updates the model in order to minimize
discrepancies between the results of the modal analysis performed
on the FEM model and those obtained through OMA.
This allows for the identification of any changes in the characteristics
of the work, such as stiffness of structural elements,
joints, or supports.
Through the automatic analysis of rotations and deformations,
the system is capable of reconstructing the deformation
of the asset, and of distinguishing between its thermal,
transient, or permanent components. It is important
to emphasize that the calibrations performed do not aim to
achieve correspondence between measurement and simulation
by imposing the measured data in the latter, but
rather to characterize the behavior of the work over time,
throughout its entire useful life, correlating causes and effects.
To achieve this result, the calibration procedures used by
the software are of two main types, interrelated, that aim
to identify different parameters: long-term calibration and
short-term calibration.
Long-term calibration identifies nonlinear material parameters
or structural characteristics that cause the “slow” evo-
Riferimenti
[1] Linee Guida per la Classificazione del Rischio, la valutazione della sicurezza
ed il monitoraggio dei ponti esistenti, Allegate al parere del Consiglio Superiore
dei LL.PP. n. 54/2022
[2] M. Martinelli, M. Ferrario, SYNOPTIC FIBER OPTIC SENSOR”, Patent
[3] M.Martinelli, The dynamical behaviour of a single-mode optical fiber strain-gage
IEEE Journal of Quantum Electronics, QE 18, 666, (1982)
[4] C. Liguori, M. Martinelli, Integral phase modulation properties of a single mode
optical fiber subjected to controlled vibrations, Applied Optics, 20, 4319, (1981)
[5] NTC 2018 – Testo aggiornato delle norme tecniche per le costruzioni (NTC2018),
di cui alla legge 5 novembre 1971, n. 1086, alla legge 2 febbraio 1974, n. 64,
al decreto del Presidente della Repubblica 6 giugno 2001, n. 380, ed al decreto-legge
28 maggio 2004, n. 136, convertito, con modificazioni, dalla legge 27
luglio 2004, n. 186.
[6] Circolare NTC 2018 – CIRCOLARE 21 gennaio 2019, n. 7 C.S.LL.PP. Istruzioni
per l’applicazione dell’«Aggiornamento delle “Norme tecniche per le costruzioni”»
di cui al decreto ministeriale 17 gennaio 2018.
[7] ANSFISA Istruzioni Operative per l’applicazione delle Linee guida per la classificazione
e gestione del rischio, la valutazione della sicurezza ed il monitoraggio
dei ponti esistenti.
[8] WeStatiX SHM – westatix.com – Piattaforma per monitoraggio e manutenzione
predittiva delle infrastrutture.
lution of the behavior of the work, over long periods. Depending
on the type of artifact, this calibration may involve
identifying parameters that govern the evolution of stiffness,
creep and shrinkage evolution laws, damage, corrosion,
or fatigue.
The data is processed to eliminate from the measurements
the effects due to transient events, such as temperature variations
or acting loads. The calibration uses the complete series
of available measurements, in order to continue improving
the material laws governing their evolution over time.
Short-term calibration identifies parameters that govern
rapid changes in measured data. Examples can include the
thermomechanical characteristics of materials, which govern
the structure’s behavior as temperature changes (e.g.,
displacements due to temperature gradients), but also acting
loads (e.g., from traffic), which cause sudden and usually
reversible (elastic) deformations and displacements, as
well as scenarios of sudden damage, which the platform is
designed to detect and quantify accurately.
Some results
To validate the calculation models, simulation of the available
load tests was conducted. For the Parchi Viaduct, these
were carried out during May 2021. During these tests, various
spans of the viaduct were loaded with a maximum of 6
trucks weighing about 30-32 tons each, arranged as depicted
in the figure. The displacements of the deck were detected
by topographic survey.
The numerical simulations were performed using the model
previously described, integrated into the digital twin.
In particular, the aim was to accurately describe the position
of the trucks by applying the tire prints directly onto the
deck, also considering the distribution effects in
the asphalt layer as specified by the regulations.
The simulations accounted for the different
heights of the piers corresponding to the analyzed
spans, introducing two new heights, equal
to 15m and 9m. The results show an increase of
about 20% in terms of maximum deflection, for
the same load, in correspondence with the taller
piers.
The simulations demonstrated excellent adherence
to the real behavior of the structure, with
negligible discrepancies both in terms of maximum
deck deflection and in terms of the trend
of displacements.
The distributed fiber optic system installed on
the Parchi Viaduct enables the acquisition of
strain rate values (or the derivative of deformation
over time) and strain values (deformation
or elongation of the fiber).
The strain rate values are automatically processed
by the system, allowing for the dynamic
characterization and identification of modal parameters
for each individual span of the viaduct.
The strain values, obtained with the same fibers, allow for the
continuous reconstruction of the deformation and the detection
of any anomalous mechanisms, which are promptly reported
by the system.
Similarly, for other artifacts of the highway network, the identification
of modal parameters and the reconstruction of the
deformation are performed using accelerometric and inclinometric
data, correlated with measured temperatures.
Access to the digital twins corresponding to individual works
is facilitated through an interactive map, which shows the position
of the asset on the national territory, allowing access
with a simple click and enabling a true three-dimensional virtual
inspection.
The platform combines techniques for real-time updating
and calibration of the digital twin with an advanced system
for predictive analysis, based on “physics-informed” artificial
intelligence, which allows predicting the future behavior
of the work, based on monitoring data and performed simulations.
The system constitutes a “self-learning” technology, prepared
to continue “training” based on the multivariate historical
series obtained from the instrumentation, and the results
of numerical analyses.
This information can be consulted at any time through dashboards
dedicated to the analysis of measured data and obtained
through FEM simulation, displaying specific sets of
structural health indicators for each work at the current moment,
and their expected trend for the future. This approach
allows for predictive maintenance, with planning of interventions
optimized based on the information obtained from the
digital twin.
Infrastrutture&Mobilità
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