Seismic engineering data report: Romanian and Greek records, 1972-77

Brady, A. G.; Rojahn, Christopher; Pérez, Vicente Pérez; Carydis, PG; Shokos, J.G.

doi:10.3133/ofr781022

Cited by 2 publications

(2 citation statements)

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“…However, Soufleris (1980) showed tracings of records from both the June 20 and July 4 events, enabling our set of hand-digitized records to be correctly identified. Handdigitized records of four smaller earthquakes from the eastern Gulf of Corinth have been published by Brady et al (1978). We regenerated the digitized time series from these tabulated values.…”

Section: Accelerograph Station Coordinates and Instrumental Responsementioning

confidence: 99%

Strong ground motion in normal-faulting earthquakes

Westaway¹,

Smith²

1989

Geophysical Journal International

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We investigate the character of strong ground motion in normal-faulting earthquakes from the western USA, Italy, Greece, Turkey and New Zealand, with magnitudes from 4 to 7.5.In all cases where seismic phases can be identified, peak horizontal ground acceleration occurs in the direct S-phase. Crustal anelasticity for S-waves, which varies locally, is shown for events above magnitude 5 at distances of tens of km to be relatively unimportant in comparison with geometrical spreading in determining the variation of peak horizontal ground acceleration with source-station distance. This permits us to treat our set of records, regardless of source region, as a single worldwide data set. For events between magnitudes 4 and 5, peak horizontal ground acceleration appears not to be controlled by geometrical spreading. At source-station distances greater than about 10 km, it decreases approximately in inverse proportion to distance squared.Earthquakes with magnitudes greater than 5 cause ground acceleration equivalent to that in reverse-faulting and strike-slip events. This result contradicts the widely held view that normal-faulting earthquakes generate systematically smaller ground accelerations than other events. It implies that the orientation of the crustal stress-field, which will be very different in extensional regions from other regions, has little effect on the amplitude of high-frequency seismic waves generated by earthquakes.

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Section: Accelerograph Station Coordinates and Instrumental Responsementioning

confidence: 99%

Strong ground motion in normal-faulting earthquakes

Westaway¹,

Smith²

1989

Geophysical Journal International

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“…where u x ≡ u x (x, y, t) and u y ≡ u y (x, y, t) are the displacement fields, E is the modulus of elasticity, ν is the Poisson ratio, ρ is the material's mass density and p x and p y are the body forces. Specifically, the three-story reinforced concrete coupled shear walls of figure 19 were subjected to seismic loading, that of the accelerogram of 1972 Kefalonia earthquake [46] (figure 20) with a total duration of 6.00 sec. The Young moduli E 1 , E 2 and E 3 of each story are considered as uncorrelated random variables following the log-normal distribution with mean value µ = 30 GP a and standard deviation σ = 0.25µ = 7.5 GP a.…”

Section: Coupled Shear Walls Under Seismic Loadingmentioning

confidence: 99%

Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders

Nikolopoulos,

Kalogeris,

Papadopoulos

2021

Preprint

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This work presents a non-intrusive surrogate modeling scheme based on machine learning technology for predictive modeling of complex systems, described by parametrized time-dependent PDEs. For this type of problems, typical finite element solution approaches involve the spatiotemporal discretization of the PDE and the solution of the corresponding linear system of equations at each time step. Instead, the proposed method utilizes a convolutional autoencoder in conjunction with a feed forward neural network to establish a low-cost and accurate mapping from the problem's parametric space to its solution space. The aim is to evaluate directly the entire time history solution matrix through these interpolation schemes. For this purpose, time history response data are collected by solving the high-fidelity model via FEM for a reduced set of parameter values. Then, by applying the convolutional autoencoder to this data set, a low-dimensional representation of the high-dimensional solution matrices is provided by the encoder, while the reconstruction map is obtained by the decoder. Using the latent representation given by the encoder, a feed-forward neural network is efficiently trained to map points from the problem's parametric space to the compressed version of the respective solution matrices. This way, the encoded time-history response of the system at new parameter values is given by the neural network, while the entire high-dimensional system's response is delivered by the decoder. This approach effectively bypasses the need to serially formulate and solve the governing equations of the system at each time increment, thus resulting in a significant computational cost reduction and rendering the method ideal for problems requiring repeated model evaluations or 'real-time' computations. The elaborated methodology is demonstrated on the stochastic analysis of time-dependent PDEs solved with the Monte Carlo method, however, it can be straightforwardly applied to other similartype problems, such as sensitivity analysis, design optimization, etc.

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Seismic engineering data report: Romanian and Greek records, 1972-77

Cited by 2 publications

References 0 publications

Strong ground motion in normal-faulting earthquakes

Strong ground motion in normal-faulting earthquakes

Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders

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