1,720,969 research outputs found
Mellin transform for the probabilistic characterization of random variables and stochastic processes
Full text linkThe probabilistic characterization of random variables and stochastic processes involves the evaluation of the probability density function or characteristic function. The latter is typically obtained by using integer-order statistical moments, that could lead to divergence problem for high-order moments especially in case of heavy-tailed distributions, such as the distribution of the α-stable random variables. On the other hand, recent approaches that use complex fractional moments, offer a more robust probabilistic description, but for particular cases. In this paper, a novel approach based on Mellin transform for the probabilistic characterization of random variables is proposed. Starting from numerical data, this approach is effective for the evaluation of both the probability density function and the characteristic function, and then is valid for a wide class of random variables. Further, an extension of the approach from random variables to stochastic processes is proposed. The reliability of the proposed approach is assessed through several numerical simulations involving α-stable distributions, Gaussian distributions and α-stable stochastic processes
An innovative only-output method to identify a structural system
Full text linkStructural Health Monitoring (SHM) is nowadays common in many branches of engineering since it allows to have a continuous or periodic report of the structural conditions and therefore to promptly intervene if there are incipient damages. The first step to perform a SHM is the identification of the dynamic parameters, i.e. natural frequencies, damping ratios and modal shapes, and it is a crucial step since a modification of the structural parameters can be a direct consequence of structural damages. Among the structural identification methods, Operational Modal Analysis (OMA) methods have received increasing attention from the researchers since they do not require the knowledge of the structural excitation that is due to ambient vibrations and that is usually modeled as a white noise. This aspect makes this kind of methods cheaper and simpler than the classical Experimental Modal Analysis (EMA) methods.
In this paper an innovative OMA method is proposed. It is a semi - automated method that allows to identify natural frequencies, damping ratios and modal shapes of a structural system and that can be used also from users that have not knowledge in stochastic dynamics and signal analysis. First of all, the modal shapes are estimated through the use of signal filtering techniques applied on the stochastic properties of the output process and then natural frequencies and damping ratios can be estimated from the mono - component analytical signals obtained by performing a decomposition of the analytical signals matrix. The proposed method has been used to perform the dynamic identification of a real historic building situated in Palermo, i.e. Chiaramonte palace, and the results obtained have been compared with those obtained by using other OMA methods
Digital simulation of multi-variate stochastic processes
Full text linkStochastic dynamic analysis of linear or nonlinear multi-degree-of-freedom systems excited by multi-variated processes is usually conducted by using digital Monte Carlo (MC) simulation. Since in structural systems few modal shapes contribute to the response in the nodal space, the computational burden of MC simulation is mainly related to the digital simulation of the input process. Usually, the generation of multi-variated samples of Gaussian input process is
performed with the aid of the Shinozuka formula. However, since in this procedure the stochastic process is given as a summation of waves with random amplitude amplified by the square root of the power spectral density, the randomness is due to a random phase angle of each wave, therefore a very large number of waves is required to reach the Gaussianity, i.e. the process is only asymptotically stable. Moreover, the computational burden increases in case of multi-variated processes. The paper aims to drastically reduce the generation time of the input process through the use of a two-step procedure. In the first step, by using the Priestley formula, each wave is normally distributed. This first aspect allows to drastically reduce the computational effort for the mono-variate process since few waves are sufficient to reach the Gaussianity. In the second step, the multi-variate process is reduced as a summation of independent fully coherent vectors if the quadrature spectrum (q-spectrum) can be neglected. An application of digital simulation of the wind velocity field is discussed to prove the efficiency of the proposed approach
Vibration Based Structural Health Monitoring: A Real Case Study Framed into Cultural Heritage
No full textVibration based Structural Health Monitoring (SHM) is increasingly
used to monitor modal parameters of structures, i.e. natural frequencies, damping
coefficients and mode shapes. Operational Modal Analysis (OMA) methods are
very attractive since they are cheap and totally non-destructive. Furthermore, they
allow to perform SHM when the structure is under operative conditions, i.e. when
the structural input, due to ambient vibrations, is not known.
In this paper anOMAmethod based on the Hilbert transform is used to perform
a real world application of vibration based SHM. Particularly, the case study of a
cultural heritage structure located in Palermo is analysed in detail also considering
comparisons with other OMA methods for SHM
A new OMA method to perform structural dynamic identification: numerical and experimental investigation
Full text linkOperational modal analysis (OMA) methods are nowadays common in civil, mechanical and aerospace engineering to identify and monitor structural systems without any knowledge on the structural excitation provided that the latter is due to ambient vibrations. For this reason, OMA methods are embedded with stochastic concepts and then it is difficult for users that have no-knowledge in signal analysis and stochastic dynamics. In this paper an innovative method useful for structural health monitoring (SHM) is proposed. It is based on the signal filtering and on the Hilbert transform of the correlation function matrix. Specifically, the modal shapes are estimated from the correlation functions matrix of the filtered output process and then the frequencies and the damping ratios are estimated from the analytical signals of the mono-component correlation functions: a complex signals in which the real part represents the correlation function and the imaginary part is its Hilbert transform. This method is very simple to use since requires only few interactions with the users and thus it can be used also from users that are not experts in the aforementioned areas. In order to prove the reliability of the proposed method, numerical simulations and experimental tests are reported also considering comparisons with the most popular OMA methods
OMA: From Research to Engineering Applications
No full textAmbient vibration modal identification, also known as Operational Modal Analysis (OMA), aims to identify the modal properties of a structure based on vibration data collected when the structure is under its operating conditions, i.e., when there is no initial excitation or known artificial excitation. This method for testing and/or monitoring historical buildings and civil structures, is particularly attractive for civil engineers concerned with the safety of complex historical structures. However, in practice, not only records of external force are missing, but uncertainties are involved to a significant extent. Hence, stochastic mechanics approaches are needed in combination with the identification methods to solve the problem. In this context, this paper’s contribution is to introduce an innovative ambient identification method based on the Hilbert Transform to obtain the analytical representation of the system response in terms of the correlation function. This approach opens the pathway for a monitoring system that is user friendly and can be used by people who have little to no knowledge of signal processing and stochastic analysis such as those who are responsible for the maintenance of a city’s historical buildings. In particular, this method operates in time domain only. Specifically, firstly the correlation functions matrix RX(τ) is determined based on the recorded time domain data. Next, performing a Singular Value Decomposition (SVD) on RX(τ) for τ= 0 leads to an estimate of the modal matrix Φ containing all the modal shapes. In this manner, once Φ is known, the entire correlation functions matrix in modal space RY(τ) is recovered. Further, the analytical signals of the auto-correlation functions in modal space are determined performing the sum of each auto-correlation function with its Hilbert transform. Moreover, since the analytical signal can be expressed in terms of amplitude and phase, then frequencies and damping ratios estimation is possible. Finally, in order to prove the reliability of the method several numerical examples and an experimental test are reported
On the occurrence of the invasive Atlantic blue crab Callinectes sapidus Rathbun 1896 (Decapoda: Brachyura: Portunidae) in Sicilian inland waters
Full text link- The Atlantic blue crab Callinectes sapidus Rathbun 1896 is included among the worst invasive alien species in the Mediterranean Sea. Here we report the finding of the species in two Sicilian rivers, the Irminio and the Imera Meridionale, where it was collected up to 6 km distant from the river mouths. Although several records of the species are already available from Italy, this is the first evidence of the occurrence of this invasive crab this far from the coastline throughout the Country. In the light of the well-known impact of the Atlantic blue crab on the invaded water bodies, the monitoring of the species and appropriate mitigation strategies should be implemented in order to protect the threatened native biota of the Sicilian inland waters
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Food Habits of the Javelin Sand Boa Eryx jaculus (Linnaeus 1758; Serpentes, Erycidae) in Sicily, Italy
No full textThe Javelin Sand Boa, Eryx jaculus, is reported to be a predator of mammals, lizards and their eggs, and occasionally of birds and invertebrates, but data on its diet are scarce and fragmentary. Here we describe some aspects of the feeding behavior of E. jaculus on the Mediterranean island of Sicily. A total of 132 individual snakes were examined. Prey remains were found in 43% of them, both in their feces (82.5%) and gut contents (17.5%). The number of snakes observed and their feeding rate decreased in August, probably as a result of the relatively higher temperatures. Feeding rate increases were observed in adult females in September, perhaps to enhance body reserves before hibernation. The overall prey spectrum is dominated by small mammals, with a frequency of occurrence of 71.4%, but also consisted of lizard eggs (30.2%) and lizards (7.9%). Lizards seem to be occasional prey, and our frequent detection of ingested autotomized tails suggests E. jaculus has low efficiency as a saurian predator. We observed a relationship between prey type and snout-vent length of the snakes. Lizard eggs are most frequently eaten by smaller snakes, which could be linked to gape size ontogenetic variation. We found differences in the prey spectrum between sexes and age classes. Our results indicate that juveniles, adult males, and females seem to adopt different foraging strategies. Females probably adopt ambush predation on small mammals, while juveniles are active foragers of lizard eggs. Adult males appear to be slightly more versatile predators, consuming both types of prey, probably because of their high mobility rates during the mating period
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