Top PDF An analytical approach to probabilistic modeling of liquefaction based on shear wave velocity

An analytical approach to probabilistic modeling of liquefaction based on shear wave velocity

An analytical approach to probabilistic modeling of liquefaction based on shear wave velocity

probability density function curve for the liquefaction safety factor and reliability assessment, an. 12[r]

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An analytical approach to reliability assessment of shear wave velocity relationship

An analytical approach to reliability assessment of shear wave velocity relationship

Abstract. During an earthquake, seismic waves propagate vibrations that carry energy from the source of the shaking outwards. Seismic waves can be distinguished by the velocity and shape of propagation. The velocity of waves depends on the elastic properties and density of the soil layers through which the waves pass. Probabilistic analysis of earthquake waves can be used as an eective tool to evaluate inherent uncertainty in the soil properties and the resulting uncertainty in site classication. In this research, the jointly distributed random variables method is used for probabilistic analysis and reliability assessment of the shear wave velocity relationship. The selected stochastic parameters are density, elastic modulus and Poisson's ratio which are modeled using truncated normal probability distribution functions. The results are compared with the Monte Carlo simulation, point estimated method and rst order second moment method. Comparison of the results indicates very good performance of the proposed approach for assessment of reliability. It is shown that this method can correctly predict the inuence of stochastic input parameters and capture the expected probability distribution of shear wave velocity correctly. It is also shown that the modulus of elasticity is the most eective parameter in shear wave velocity.
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Characterization of a Carbonate Sand based on Shear Wave Velocity Measurement

Characterization of a Carbonate Sand based on Shear Wave Velocity Measurement

In the simplified stress-based approach for liquefaction analysis, seismic demand is calculated as the cyclic shear stress ratio applied by an earthquake (CSR) and the cyclic resistance ratio (CRR) of the soil (capacity) is estimated from a correlation with an in-situ test. Earthquake-induced CSR can be estimated using the Seed and Idriss (1971) simplified procedure or numerical methods such as finite element method based seismic response analysis. The simplified procedure provides CRR of a level-ground (no shear stress bias) for an effective overburden pressure of 100 kPa. Cyclic liquefaction is deemed to occur when CSR exceeds CRR. Semi-empirical relationships between CRR and SPT or CPT penetration resistance have been extensively studied by many researchers. Determining CRR from in-situ shear wave velocity measurement can be a particularly useful alternative for sites underlain by soils that are difficult to penetrate or extract undisturbed samples. Robertson, et al. (1992) present one of the earliest boundary curves between liquefaction and non- liquefaction cases using a limited field database. Based on cases of liquefaction and non-liquefaction for 26 earthquakes and more than 70 different sites, Andrus and Stokoe (2000) developed relationships between CRR and VS1 which are the current state of practice for evaluating liquefaction potential using VS1.
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Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

The probabilistic relationship proposed by Liao et al. employs a larger number of case history data points than were used by Seed et al. (1984), but this larger number of data points is the result of less severe screening of points for data quality. This relationship was developed using the maximum likelihood esti- mation method for probabilistic regression (binary regression of logistic models). A largely judgmental correction was made for sampling bias, and this significantly affected the final relation- ships. Liao et al. sought, but failed to find, a significant impact of fines content on the regressed relationship between SPT penetra- tion resistance and liquefaction resistance, and so developed reli- able curves [Fig. 2(a)] only for “clean” sandy soils (soils with less than 12% fines). This was a landmark effort in its time, and it set high standards for those that followed.
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Three dimensional shear wave velocity structure in the upper mantle beneath the Philippine Sea region

Three dimensional shear wave velocity structure in the upper mantle beneath the Philippine Sea region

Group velocity dispersion data of surface waves travelling through the central portion of the Philippine Sea were used to construct a one-dimensional (1-D) upper mantle shear wave model, ARC-1. Several studies have been done to reveal the lateral variations of the upper mantle structure in the Philip- pine Sea region since Kanamori and Abe (1968). Seekins and Teng (1977) studied on the laterally heterogeneous structure in the upper mantle beneath the Philippine Sea with using seismograms recorded at Guam which is the WWSSN sta- tion located at the eastern edge of the Philippine Sea. They regionalized the Philippine Sea plate into three sub regions according to its topographic features. Shiono et al. (1980) also made a surface wave analysis using group velocity dis- persion data. They investigated waveforms which traversed western (West) and eastern (East) Philippine Sea. Senna et al. (1990) and Oda and Senna (1994) divided the Philippine Sea region and its marginal region into seven sub regions ac- cording to the topographic features and obtained a 1-D shear wave velocity structure for each sub region using group ve- locity dispersion data. As to body wave analysis, Iidaka et al. (1989) is the only study to investigate the lateral hetero- geneity of the upper mantle in the Philippine Sea.
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A New Method for Shear Wave Speed
Estimation in Shear Wave Elastography

A New Method for Shear Wave Speed Estimation in Shear Wave Elastography

Shear waves were generated by a mechanical excitation from a single location; thus, shear waves were assumed to resemble plane waves propagating away from the source with little to no reflection from boundaries or inclusions. In this experiment, the shear wave is seen to propagate both laterally and axially. Because the direction of propa- gation may not be known entirely beforehand, to capture most of the energy in a single direction and maintain the form of the wave, wideband directional filters were imple- mented using a Tukey window with a tapered-to-constant ratio of 0.75, where the window was ±π/3 about the de- sired direction of wave propagation. The truncated direc- tional bounds allowed the Tukey window to have smooth/ sharp drop off while maintaining directionality. If shear waves were assumed to be propagating in unknown direc- tions, or generated at several angles by acoustic radiation force, several narrower directional filters would need to be implemented. This would increase SNR and provide a smoother final image, but may filter out sharp boundaries between tissues of significantly different shear wave speed. Regardless of the number of directional filters, the method proposed here assumes the shear wave is approximated by a plane wave in the estimation kernel. A minimum number of directional filters should be used to meet this assump- tion. A study with different directional windowing func- tions might better describe reconstruction image quality for final c s estimates.
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Temporal change of near surface shear wave velocity associated with rainfall in Northeast Honshu, Japan

Temporal change of near surface shear wave velocity associated with rainfall in Northeast Honshu, Japan

And the shear wave velocity of rock can be influenced by porosity, fluid saturation, fractures, pressure and tem- perature (Toksoz et  al. 1976). Hence the near-surface shear wave velocity is subjected to a variety of environ- ment conditions, such as crustal stress, ground surface perturbation and atmospheric forcing. Many researchers have observed change of near-surface velocity caused by strong motion (Hobiger et al. 2012; Nakata and Snieder 2011; Niu et al. 2008), plate motion (Nakata and Snieder 2012a; Takagi and Okada 2012), volcanic eruption (Bren- guier et al. 2008, 2011), earth tide (Hillers et al. 2015a, b), seasonal variations, including temperature, precipitation, groundwater level; sea level, wind and so on (Clements and Denolle 2018; Meier et al. 2010; Richter et al. 2014; Sen-Schönfelder and Wegler 2006; Tsai 2011; Wang et al. 2017).
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Probabilistic Liquefaction  Hazard Evaluation: Method and Application (M164)

Probabilistic Liquefaction Hazard Evaluation: Method and Application (M164)

rock peak ground acceleration (PGA) hazard curves for one of the locations. This figure also shows the corresponding soil PGA hazard curve, transformed from the rock curves using the soil/rock amplification relation presented in [15]. These soil PGA hazard curves correspond to Eq. 5. Each of the circle symbols on the hazard curve in Figure 4 indicates where a magnitude-distance deaggregation matrix value [Eq. 6] is available to use in ASHLES. Figure 5 is an example ground motion deaggregation matrix for one point along the hazard curve. As an observation for this particular site at this example ground motion and hazard level, the highest peaks are coming from the dominating contribution to ground motion hazard of the Hayward-Rodgers Creek (63%) and San Andreas (17%) faults, identifiable from their distinguishing magnitude and distance distributions. This deaggregation matrix indicates that much of the remaining PGA hazard contribution is coming from nearby (0 to 10km) smaller magnitude events not associated with any given fault. Deaggregation matrices for different points along a hazard curve will indicate differing relative contributions by the various seismic sources. As discussed above, and indicated by the summation over a i in Eq. 17, the evaluation of annual probability of liquefaction
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Spatial Variability of Shear Wave Velocity Using Geostatistical Analysis in  Mashhad City, NE Iran

Spatial Variability of Shear Wave Velocity Using Geostatistical Analysis in Mashhad City, NE Iran

We utilized the shear wave velocity information of 243 boreholes. These boreholes were drilled during geotech- nical studies for different projects such as hotels, residential and commercial buildings. Downhole shear wave profiling has been done by Zamin Physics Consulting Engineering Company. Mashhad is spiritual capital of Iran and more than 20 million tourists and pilgrims visit the city annually. Hence tendency increases to construct high- raised commercial buildings, malls, and hotels. The main aim of downhole Vs measuring are determining seis- mic soil classification based on 2800 Iranian Seismic Code. Most of the boreholes were drilled to depth of 30 m although some continue more than 30 meters. The Vs profiles scattered throughout the city are shown in Figure 1. 2.3. Method
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Fundamental modeling of wave propagation in temporally relaxing media with applications to cardiac shear wave elastography

Fundamental modeling of wave propagation in temporally relaxing media with applications to cardiac shear wave elastography

During the experiments, the nylon wires were first pre- stretched to the maximum tension of 57 N. A single unipolar wave pulse was then manually generated by perturbing the first metal rod, and the tension was subsequently dropped to 12.4 N by releasing the lever during propagation. In order to avoid the superposition of boundary reflections with the waves we wanted to study, the release of the lever was timed so that the waveform would always be in the center of the system when the tension dropped. The tension drop happens in a fraction of a period. Figure 4 shows the propagation measured during an experiment: the axes represent the time elapsed and the spatial coordinates of each rod expressed by rod number; due to the limited field of view of the camera, the extremal rods were not imaged and therefore do not appear in the plot. The color scheme represents the ampli- tude of rotation of each bar, so that the bright yellow “bands” essentially correspond to the positive forward trav- eling wave. At time t  1:5 s the effects of the tension drop can be seen. A reflected wave appears as the broad band that moves backwards from right to left, with its negative ampli- tude represented in dark blue. In addition, broadening of the
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A probabilistic approach to modeling collisions with thin barriers

A probabilistic approach to modeling collisions with thin barriers

When considering this model, there may be a question of the validity of binding structure defects to the grid and assessing the impact of cell size on the resulting fragmentation spectrum because when changing the cell size, the spatial distribution of the initial inhomogeneities will change fundamentally. This point was analyzed in detail and the following conclusions were made. The selected grid step, in fact, determines the level of the structure modeling, at the same time at any level there are distributed real inhomogeneities, which act as triggers and initiate the formation of microcracks. At the atomic level, the inhomogeneities are dislocations and other defects of the crystal lattice. In larger scale analysis, grains and grain boundaries act as defects. In macro analysis, the inhomogeneities are already microcracks and other relatively large stress concentrators. Thus, the approach with the distribution of strength characteristics is applicable for any cell size, however, as in any other algorithm using grid approximation, the cell size should be sufficient to ensure the necessary accuracy (in this case – the accuracy of the definition of the fragmentation spectrum).
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An Hybrid Method of Modeling Maintenance Cost Based on Analytical and UVA Approach

An Hybrid Method of Modeling Maintenance Cost Based on Analytical and UVA Approach

Abstract-- In This paper we will present a new methodology for modeling and optimization of maintenance costs, in a different and innovate way, and highlights, management methods based on the process approach, as recommended by the ISO 9000, mainly, this new method get their strategy from the Unity of Value Added (UVA). An application of this hybrid method to calculate maintenance costs is detailed.

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Bayesian Approach for Indoor Wave Propagation Modeling

Bayesian Approach for Indoor Wave Propagation Modeling

Abstract—This paper presents a parsimonious Bayesian indoor wave propagation model for predicting signal power in multi-wall multi-floor complex indoor environments. The received power is modeled as a Bayesian multiple regression model. The parameters of the model are assessed and validated using a two-tier validation strategy in which Bayes factor and posterior probability are used in the first tier and second tier, respectively. The performance of the two-tier strategy is then assessed using Bayesian information criterion. The proposed indoor propagation model is tested in a two-storey building with access points operating at 2.4 GHz.
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Crustal Shear Wave Velocities Inferred From Love Wave Group Velocity Dispersion Between Mt. Merapi and Mt. Lawu, Central Java

Crustal Shear Wave Velocities Inferred From Love Wave Group Velocity Dispersion Between Mt. Merapi and Mt. Lawu, Central Java

middle part of central Java, north to Merapi and Lawu volcanoes, there is a large and very intense anomaly with a velocity decrease of up to 30% and 35% for P and S models, respectively. Inside this anomaly E-W orientation of fast velocity takes place, probably caused by regional extension stress regime. They observed that in a vertical section there is a faster horizontal velocities inside this anomaly that might be explained by layering of sediments and/or penetration of quasi-horizontal lenses with molten magma. Luehr et al [4] identified a large low velocity body in the crust which extends down to the upper mantle beneath Central Java. The shear wave signals recorded above this anomaly are strongly attenuated compared to the neighboring areas. Active volcanoes like Merapi, Sumbing, and Lawu are located at the edge of this anomaly between high and low velocity regions. In this paper we try to model the shear wave velocities in the crust between the Merapi and Lawu volcanoes anomaly (MLA) inferred from Love wave dispersion.
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An analytical approach to modelling shear panels in steel beams at elevated temperatures

An analytical approach to modelling shear panels in steel beams at elevated temperatures

Shear buckling of beam webs in the vicinity of beam-to-column connections has been observed in many full-scale fire tests. This phenomenon can lead to force redistribution within the adjacent connections, and even influence the performance-based analysis of full- scale structures in fire. However, beam-web shear buckling for Class 1 beams at either ambient or elevated temperatures has not been well studied previously. In this work an analytical model has been created to predict the shear buckling behaviour of Class 1 beams in the vicinity of beam-to-column connections at ambient and elevated temperatures. The model considers the reduction of resistance of the beam after web shear buckling has occurred. It is capable of predicting the shear resistance and transverse drift of the shear panel from its initial loading to final failure. Several 3D finite element models have been created using the ABAQUS software, in order to validate the analytical model over a range of geometries. Comparisons between the theoretical and FE models have shown that the proposed method provides sufficient accuracy to be implemented and used in performance- based global modelling.
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A Predictive Modeling Approach for Assessing Seismic Soil Liquefaction Potential Using CPT Data

A Predictive Modeling Approach for Assessing Seismic Soil Liquefaction Potential Using CPT Data

Another significant limitation of existing ELM’s is a lack of openness regarding the model building process. In this process, a modeler must make choices regarding which predictor variables to include, how to pre-process these predictors, what model forms to use, and many more. When a modeler makes these decisions, either manually or as part of pre-programmed algorithm, they will naturally have some idea of what “good model performance” is and make their choices to achieve it. This notion of good performance can be concrete such as some statistical metric, based on engineering judgement and experience, or most often some combination of the two. While necessary to the modeling process, this introduces bias. Because these training methods and metrics are often not reported along with the finished product, practitioners and code writers cannot currently evaluate these model biases when selecting which relationships to use or recommend in guidance documents. Within this context of our own definition of “good model performance” we discuss the impacts of modeling choices and how they can be used in future model development.
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Detecting and modeling subsurface fracture systems in geothermal fields using shear-wave splitting

Detecting and modeling subsurface fracture systems in geothermal fields using shear-wave splitting

Inversion results show that most cracks have a relatively steep dip; however, the results for stations P06 and P11 indicate that the shallow dip of the cracks (<40°) may indeed be related to fractures associated with the overall shape of the Krafla-Leirhnúkur geothermal field, which stretches between the two regions of shear wave attenuation imaged by Einarsson (1978). As the source volume of most of our events lies between 1- 2 km depth, fractures at this depth are likely to be formed by deformation within the near- surface, extrusive part of the crust, dike injections, or the strike-slip across the divergent plate boundary. Figure 3.7 shows some of the major geological lineament structures to the north of Iceland. It seems that the NW-SE oriented fracture systems detected by this study could also be interpreted as the subsurface continuation of Dalvik Lineament. The magnitude of splitting delays is similar to what has been observed elsewhere within the Neovolcanic Zone of Iceland (Menke et al., 1994). The high scatter of time delays are also observed by Volti and Crampin (2003a, 2003b) and should be accounted for by similar explanations thereof.
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An analytical approach to modelling shear panels in steel beams at elevated temperatures

An analytical approach to modelling shear panels in steel beams at elevated temperatures

Classical tension field theory can represent the post-buckling behaviour of plate girders very well [9-11, 14-17]. In these models, shear resistance involves three stages: pre-buckling, post-buckling and collapse. In the pre-buckling stage, no buckling appears in the panel, and the principal tensile and compressive stresses are identical until elastic buckling happens. The elastic buckling strengths of plates under various conditions are given by Timoshenko [24]. In the post-buckling stage, stress redistribution occurs, with increase occurring especially in the directions of the tensile principal stresses. Any additional compressive stress after web shear buckling can effectively be neglected. In the collapse stage, four plastic hinges appear on the flanges, and finally the plate girder fails in a “sway” mechanism. In the proposed analytical model, for Class 1 beams, the shear response once again consists of three stages, which differ from those of tension field theory. These are the elastic, plastic and plastic post-buckling stages. The behaviour of the web panel of a Class 1 beam subject to shear and bending moment is compared with that of a plate girder in Fig. 3.
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A New Shear Wave Speed Estimation Method for Shear Wave Elasticity Imaging

A New Shear Wave Speed Estimation Method for Shear Wave Elasticity Imaging

standard deviation of the second Gaussian mixture respectively, Y is displacement measurements at X instances. No modifications are happened to linear regression implementation performed in the Lateral TTP algorithm. Regressions are not applied to lateral locations within ROE i.e. one excitation beam width defined by (F/#)* from excitation center and extended over lateral range where the peak displacements remained above 1 m. The inverse slopes of these regression lines, with goodness-of-fit metrics exceeding a threshold (R2 >0.8, 95% CI <0.2), represent the material’s local shear wave speeds. These specific goodness- of-fit metrics is applied to all of the datasets presented throughout this manuscript. The material’s shear modulus is then estimated using Eq. 3 [31].
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The effect of stretching on transmural shear wave anisotropy in cardiac shear wave elastography

The effect of stretching on transmural shear wave anisotropy in cardiac shear wave elastography

As expected, group and phase speed values for a certain heart sample at a given stretch state were depth- and probe orientation-dependent, due to the varying myocardial fiber orientation across tissue thickness. The range of the resulting group speed values of all hearts corresponded to values previously reported in literature: 2-6 m/s for in vitro porcine hearts at 0% stretch [6]. Uniaxial stretching the myocardial slab increased the group and maximally excited phase speed, as could be seen from the 75 th percentile increase, but also from the increasing predicted marginal mean of each variable during stretching. This increase was mainly caused by an increase in wave speed for probe orientation angles oriented along the fiber direction for both cases. This trend was more convincing for group speed than for phase speed, as the GLM statistical model fit showed an adjusted R-squared value of 0.804 for group speed vs. a value of 0.415 for maximally excited phase speed (also related to the fewer data points for phase speed analysis). Furthermore, for maximally excited phase speed, tissue depth was the second most relevant factor in determining marginal mean phase speed for a mean heart at a mean depth, whereas this was the fourth relevant factor for the group speed. This is also noticed from the larger 95% confidence interval in Fig. 5 vs. Fig. 2.
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