102,147 research outputs found
Bayesian analysis of the local intensity attenuation
No full textWe present a method that allows us to incorporate additional information from
the historical earthquake felt reports in the probability estimation of local intensity attenuation.
The approach is based on two ideas: a) standard intensity versus epicentral distance
relationships constitute an unnecessary lter between observations and estimates; and b) the
intensity decay process is a ected by many, scarcely known elements; hence intensity decay
should be treated as a random variable as is the macroseismic intensity. The observations
related to earthquakes with their epicenter outside the area concerned, but belonging to homogeneous
zones, are used as prior knowledge of the phenomenon, while the data points of
events inside the area are used to update the estimates through the posterior means of the
quantities involved.Publishedope
Bayesian analysis of the local intensity attenuation
No full textWe present a method that allows us to incorporate additional information from
the historical earthquake felt reports in the probability estimation of local intensity attenuation.
The approach is based on two ideas: a) standard intensity versus epicentral distance
relationships constitute an unnecessary lter between observations and estimates; and b) the
intensity decay process is a ected by many, scarcely known elements; hence intensity decay
should be treated as a random variable as is the macroseismic intensity. The observations
related to earthquakes with their epicenter outside the area concerned, but belonging to homogeneous
zones, are used as prior knowledge of the phenomenon, while the data points of
events inside the area are used to update the estimates through the posterior means of the
quantities involved.Publishedope
SHM of Vibrating Stay-Cables by Microwave Remote Sensing
No full textA radar equipment was used to measure the deflection response of bridge stay-cables induced by ambient and traffic excitation. After a concise description of the radar equipment and a summary of advantages and potential issues of the microwave technology, the paper focuses on the experimental tests performed on all stay-cables of the curved cable-stayed bridge erected in the commercial harbor of Porto Marghera, Venice, Italy. The bridge consists of an inclined concrete tower, single-plane cables and a composite deck; the curved deck has a centerline length of 231 m, with two different side spans and 9 cables supporting each side span. Three series of ambient vibration tests were performed (on July 2010, April 2011 and October 2019) on the two arrays of cables of the bridge by using conventional accelerometers and microwave interferometer. The availability of simultaneously collected radar and accelerometer data (which are usually regarded as reference data in dynamic tests) allowed to investigate the accuracy of the radar technique (in terms of natural frequencies and tensile force estimated from natural frequencies) and the errors/uncertainties in radar results. Furthermore, the tests allowed to verify the repeatability of radar survey, with SHM purposes.Accepted author manuscriptMechanics and Physics of Structure
Probability distribution of the macroseismic intensity attenuation in the Italian volcanic districts
No full textWe present the probabilistic version of the analysis performed in Azzaro et al. (2006a) on the attenuation of the seismic intensity in Italian volcanic districts. The main results are the estimate of the probability distribution of the intensity at site IS, conditioned on the site-epicenter distance d and on I0, and then, assuming the mode of this distribution as estimator of IS, the forecasting of future macroseismic fields given I0. To this end we have modified the method presented in Rotondi and Zonno (2004) by inserting the following innovative elements: identification of possible different trends and exploitation of knowledge from prior experience or data.
Data set. The intensity dataset considered in the present analysis is the same used in the study by Azzaro et al. (2006a), based on a deterministic approach. We consider a total of 38 earthquakes located in the Italian volcanic areas, so distributed: Etna region (24 events), Aeolian Islands (6 events), Vesuvius-Ischia (3 events) and Albani Hills (5 events). The CMTE local earthquake catalogue (Azzaro et al., 2000, 2002, 2006b) has been used for the Etna region while for the other Italian volcanic districts (Aeolian Islands, Ischia, Vesuvius and Albani Hills) the CPTI04 Italian seismic catalogue (Gruppo di lavoro CPTI, 2004) and the DBMI04 associated database (Stucchi et al., 2007) have been considered (Tab. 1). For the analysis, subsets of earthquakes with epicentral intensity I0 ≥ VII MCS and I0 ≥ VI MCS were used for the Etna region and for the other Italian volcanic districts, respectively.
Probability model. We cite here the key-elements of the probabilistic method, referring to Rotondi and Zonno (2004) for a detailed description. Instead of adding a gaussian error to deterministic relationships which express the intensity decay as a function of some factors (epicentral intensity, site-epicenter distance, depth, site types, and styles of faulting), we treat the decay as an aleatory variable defined on the domain {0, I0}. Consequently, we assume that the intensity IS is a discrete binomial distributed variable Bin(I0 , p) where pI0 means the probability of null decay, and p belongs to [0,1]. According to the Bayesian approach, p is considered as a random variable following the beta distribution Beta(α, β). Since mean and variance of p are functions of the α, β hyperparameters, we can express our initial knowledge on the decay process through these parameters. To do this, we have divided each macroseismic field in bins of fixed width and the intensity data points in subsets according to this spatial subdivision. For each bin we have repeated the following procedure: a) assessing the prior values to α, β, that is a prior distribution for p; b) updating, through Bayes’ theorem, the hyperparameters on the basis of the current observations; c) estimating the p parameter through the mean of its posterior distribution. By substituting this estimate in the distribution Bin(I0 , p), we obtain an updated binomial distribution indicated as plug-in distribution. Its mode has been assumed as the expected value of the intensity at the sites within the corresponding bin. To predict the intensity at any distance we have smoothed the p’s estimated in the different bins through a monotonically decreasing function; the lowest mean squared error was given by the inverse power function .
Hence, the mode of the plug-in distribution obtained by setting p=g(d) provides an expected value for IS at any distance. If, on the contrary, we assume that, from the attenuation viewpoint, the sites inside any bin behave in the same way, we can average over the domain [0,1] of p by integrating the product of the likelihood with respect to the posterior Beta distribution of p. In this way we have obtained the so-called predictive distribution for every bin and its mode is taken as expected value for IS at any site inside that bin.
Trends in the intensity decay. We have analysed the macroseismic field of the 38 earthquakes constituting our dataset (Tab. 1) by drawing the decay versus the site-epicenter distance of each data point. A quick look at these graphical representations suggests that these earthquakes do not show an homogeneous decay. To identify different trends in the decay, we have synthetized the information contained in each field by collecting, in a matrix, median, mean, and quartile of each set of distances from the epicenter of the points with the same ΔI. Then we have applied to this matrix a clustering algorithm based on the evaluation of the distance between each pair of rows of the matrix. The dataset has been thus partitioned into two groups of events according to their attenuation trend: the first set mainly formed by the earthquakes of Mt. Etna and Vesuvius-Ischia areas, the second one including the events of the Aeolian Islands and Albani Hills.
The set 1 shows an higher decay than the set 2, so two different spatial scales are required: bins of width 1 km for the set 1 and of width 25 km for the set 2. A similar classification analysis was performed in Zonno et al. (2008) on 55 earthquakes representative of the Italian territory; in that case three classes were identified.
The probabilistic analysis above described has been separately applied to the two sets, discriminating the events of from those of , and using as a priori distributions for the parameters p’s those indicated in Zonno et al. (2008) for the class of earthquakes with the highest attenuation. The hyperparameters α’s and β’s have been then updated through the observed intensity data points according to the expressions α=α0 + ΣNn=1 IS (n) and β= β0 + ΣNn=1 (I0 - IS (n)).
Some results. For each bin the values of the predictive probability function of for the Etna area and Aeolian Islands, are shown in Fig. 1; the squares indicate the values of the intensity decay computed through the logarithmic regressions (Tab. 2) obtained by Azzaro et al. (2006) with the same dataset. These values can be compared with the mode of the predictive function in each bin.
The fit between the two methods is good but much more information is provided by the probabilistic approach. In addition to the estimate of the intensity at any site, the probability distribution of IS provides a measure of the uncertainty and its values can be directly used in the software “SASHA” (D’Amico and Albarello, 2007) to calculate the probabilistic seismic hazard at the site.
Conclusions. The identification of different decay trends produced by the clustering algorithm matches well with that already presented in the literature (Azzaro et al. 2006), and this suggests that the method could be successfully applied to other cases. Only two earthquakes in Albani Hills - 1876/10/26, I0 VI-VII, 1927/12/26, I0 VII-VIII - are unexpectedly included in the set 1 together with the events of Mt. Etna and Vesuvio-Ischia areas; further, detailed analyses are required to explain such an anomaly.
Some problems are still open: a) most of the earthquakes here considered have epicentral intensity I0 VII or VIII, so that we have evaluated the probability functions of IS conditioned on these two values of I0. Also other values of I0 must be used in the analysis; b) the method should be also validated on other earthquakes not included in the dataset of Tab. 1, on the basis of probabilistic measures of the degree to which the model predicts the decay in the data points of a macroseismic field (Rotondi and Zonno, 2004).PublishedTrieste4.2. TTC - Scenari e mappe di pericolosità sismicaope
Probability distribution of the macroseismic intensity attenuation in the Italian volcanic districts
No full textWe present the probabilistic version of the analysis performed in Azzaro et al. (2006a) on the attenuation of the seismic intensity in Italian volcanic districts. The main results are the estimate of the probability distribution of the intensity at site IS, conditioned on the site-epicenter distance d and on I0, and then, assuming the mode of this distribution as estimator of IS, the forecasting of future macroseismic fields given I0. To this end we have modified the method presented in Rotondi and Zonno (2004) by inserting the following innovative elements: identification of possible different trends and exploitation of knowledge from prior experience or data.
Data set. The intensity dataset considered in the present analysis is the same used in the study by Azzaro et al. (2006a), based on a deterministic approach. We consider a total of 38 earthquakes located in the Italian volcanic areas, so distributed: Etna region (24 events), Aeolian Islands (6 events), Vesuvius-Ischia (3 events) and Albani Hills (5 events). The CMTE local earthquake catalogue (Azzaro et al., 2000, 2002, 2006b) has been used for the Etna region while for the other Italian volcanic districts (Aeolian Islands, Ischia, Vesuvius and Albani Hills) the CPTI04 Italian seismic catalogue (Gruppo di lavoro CPTI, 2004) and the DBMI04 associated database (Stucchi et al., 2007) have been considered (Tab. 1). For the analysis, subsets of earthquakes with epicentral intensity I0 ≥ VII MCS and I0 ≥ VI MCS were used for the Etna region and for the other Italian volcanic districts, respectively.
Probability model. We cite here the key-elements of the probabilistic method, referring to Rotondi and Zonno (2004) for a detailed description. Instead of adding a gaussian error to deterministic relationships which express the intensity decay as a function of some factors (epicentral intensity, site-epicenter distance, depth, site types, and styles of faulting), we treat the decay as an aleatory variable defined on the domain {0, I0}. Consequently, we assume that the intensity IS is a discrete binomial distributed variable Bin(I0 , p) where pI0 means the probability of null decay, and p belongs to [0,1]. According to the Bayesian approach, p is considered as a random variable following the beta distribution Beta(α, β). Since mean and variance of p are functions of the α, β hyperparameters, we can express our initial knowledge on the decay process through these parameters. To do this, we have divided each macroseismic field in bins of fixed width and the intensity data points in subsets according to this spatial subdivision. For each bin we have repeated the following procedure: a) assessing the prior values to α, β, that is a prior distribution for p; b) updating, through Bayes’ theorem, the hyperparameters on the basis of the current observations; c) estimating the p parameter through the mean of its posterior distribution. By substituting this estimate in the distribution Bin(I0 , p), we obtain an updated binomial distribution indicated as plug-in distribution. Its mode has been assumed as the expected value of the intensity at the sites within the corresponding bin. To predict the intensity at any distance we have smoothed the p’s estimated in the different bins through a monotonically decreasing function; the lowest mean squared error was given by the inverse power function .
Hence, the mode of the plug-in distribution obtained by setting p=g(d) provides an expected value for IS at any distance. If, on the contrary, we assume that, from the attenuation viewpoint, the sites inside any bin behave in the same way, we can average over the domain [0,1] of p by integrating the product of the likelihood with respect to the posterior Beta distribution of p. In this way we have obtained the so-called predictive distribution for every bin and its mode is taken as expected value for IS at any site inside that bin.
Trends in the intensity decay. We have analysed the macroseismic field of the 38 earthquakes constituting our dataset (Tab. 1) by drawing the decay versus the site-epicenter distance of each data point. A quick look at these graphical representations suggests that these earthquakes do not show an homogeneous decay. To identify different trends in the decay, we have synthetized the information contained in each field by collecting, in a matrix, median, mean, and quartile of each set of distances from the epicenter of the points with the same ΔI. Then we have applied to this matrix a clustering algorithm based on the evaluation of the distance between each pair of rows of the matrix. The dataset has been thus partitioned into two groups of events according to their attenuation trend: the first set mainly formed by the earthquakes of Mt. Etna and Vesuvius-Ischia areas, the second one including the events of the Aeolian Islands and Albani Hills.
The set 1 shows an higher decay than the set 2, so two different spatial scales are required: bins of width 1 km for the set 1 and of width 25 km for the set 2. A similar classification analysis was performed in Zonno et al. (2008) on 55 earthquakes representative of the Italian territory; in that case three classes were identified.
The probabilistic analysis above described has been separately applied to the two sets, discriminating the events of from those of , and using as a priori distributions for the parameters p’s those indicated in Zonno et al. (2008) for the class of earthquakes with the highest attenuation. The hyperparameters α’s and β’s have been then updated through the observed intensity data points according to the expressions α=α0 + ΣNn=1 IS (n) and β= β0 + ΣNn=1 (I0 - IS (n)).
Some results. For each bin the values of the predictive probability function of for the Etna area and Aeolian Islands, are shown in Fig. 1; the squares indicate the values of the intensity decay computed through the logarithmic regressions (Tab. 2) obtained by Azzaro et al. (2006) with the same dataset. These values can be compared with the mode of the predictive function in each bin.
The fit between the two methods is good but much more information is provided by the probabilistic approach. In addition to the estimate of the intensity at any site, the probability distribution of IS provides a measure of the uncertainty and its values can be directly used in the software “SASHA” (D’Amico and Albarello, 2007) to calculate the probabilistic seismic hazard at the site.
Conclusions. The identification of different decay trends produced by the clustering algorithm matches well with that already presented in the literature (Azzaro et al. 2006), and this suggests that the method could be successfully applied to other cases. Only two earthquakes in Albani Hills - 1876/10/26, I0 VI-VII, 1927/12/26, I0 VII-VIII - are unexpectedly included in the set 1 together with the events of Mt. Etna and Vesuvio-Ischia areas; further, detailed analyses are required to explain such an anomaly.
Some problems are still open: a) most of the earthquakes here considered have epicentral intensity I0 VII or VIII, so that we have evaluated the probability functions of IS conditioned on these two values of I0. Also other values of I0 must be used in the analysis; b) the method should be also validated on other earthquakes not included in the dataset of Tab. 1, on the basis of probabilistic measures of the degree to which the model predicts the decay in the data points of a macroseismic field (Rotondi and Zonno, 2004).PublishedTrieste4.2. TTC - Scenari e mappe di pericolosità sismicaope
Assessment of Similar Reinforced Concrete Arch Bridges by Operational Modal Analysis and Model Updating
No full textResults of the multidisciplinary activities carried out on the different spans of a historic RC arch bridge are presented in the paper. The bridge, completed in May 1917, crosses the Adda river between the small towns of Brivio and Cisano Bergamasco (about 50 km North-East of Milan) and represents a crucial node for the regional vehicular traffic. The investigation program included: (i) documentary research in the archives of Brivio town hall and National Roadway Authority (ANAS), (ii) visual inspection and geometric survey of the arches intrados, (iii) ambient vibration tests, (iv) mechanical characterization of the concrete materials, (v) finite element modeling of each span and vibration-based model tuning. The present paper, after a concise summary of the multidisciplinary investigation carried out on the different spans of the bridge, is mainly focused on structural modeling and vibration-based model updating. In order to take advantage of the similarity of the different tied arches, a simple system identification technique was used to update the models. The application of this technique provided optimal models that accurately fit the identified modal parameters of each span; in addition, the identified structural parameters are in good agreement with the available characterization of the materials
Monitoring Reinforced Concrete Arch Bridges with Operational Modal Analysis
No full textThe paper presents the first results of the vibration-based Structural Health Monitoring strategy implemented on the historic Brivio bridge, within a joint research between Lombardy Region and Politecnico di Milano aimed at defining guidelines for the monitoring of key infrastructures. The bridge at study, dating back to 1917, crosses the Adda river and consists of three reinforced concrete tied arches. Due to its position, the Brivio bridge represents a crucial node for the vehicular traffic of the local road network. Firstly, documentary research, visual inspections, geometric survey, mechanical characterization of materials, and multiple dynamic tests are performed. The information collected on-site is then used to develop and calibrate a FE model of each arch. Once those preliminary activities are completed, a monitoring system is installed including 8 seismometers per span and 1 temperature sensor. The collected data are transferred in real time to a dedicated workstation and stored in separate files of 1 h, that are automatically processed using a multi-level procedure developed in the Matlab environment
Urban Disaster Prevention Strategies Using MAcroseismic Fields and FAult Sources (UPStrat-MAFA)
No full textThis contribution gives information on the European project UPStrat-MAFA “Urban disaster Prevention Strategies using MAcroseismic fields and FAult sources” as presented during the “Joint kick-off meeting for the representatives of all the projects selected in 2011 call for proposals C49”, in Brussels on 6 February 2012, at the European Commission - DG ECHO Unit A5 - 5, avenue de Beaulieu (Room C), 1060 Brussels – Belgium.UnpublishedEUROPEAN COMMISSION DIRECTORATE - GENERAL HUMANITARIAN AID AND CIVIL PROTECTION
Directorate A - Strategy, Policy and International Co-operation - Unit A.5 – Civil Protection Policy, Prevention, Preparedness and Disaster Risk Reduction
Venue: Brussels, Avenue Beaulieu 5, Room C at BU-54.1. Metodologie sismologiche per l'ingegneria sismicaope
Urban Disaster Prevention Strategies Using MAcroseismic Fields and FAult Sources (UPStrat-MAFA)
No full textThis contribution gives information on the European project UPStrat-MAFA “Urban disaster Prevention Strategies using MAcroseismic fields and FAult sources” as presented during the “Joint kick-off meeting for the representatives of all the projects selected in 2011 call for proposals C49”, in Brussels on 6 February 2012, at the European Commission - DG ECHO Unit A5 - 5, avenue de Beaulieu (Room C), 1060 Brussels – Belgium.UnpublishedEUROPEAN COMMISSION DIRECTORATE - GENERAL HUMANITARIAN AID AND CIVIL PROTECTION
Directorate A - Strategy, Policy and International Co-operation - Unit A.5 – Civil Protection Policy, Prevention, Preparedness and Disaster Risk Reduction
Venue: Brussels, Avenue Beaulieu 5, Room C at BU-54.1. Metodologie sismologiche per l'ingegneria sismicaope
Relationship between molt cycle and hepatopancreatic ephitelial cell composition in Penaeus (Marsupenaeus) japonicus
No full text- …
