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A Fourier series non-stationary coherency model with respect to the site conditions for the horizontal component was developed. The evolution of the model was achieved using parameters related to the site, inter-station distance, and time. First, based on the simulation of a non-stationary ground-motion field considering the wave-passage effect and site-response effect, the approach used for the estimation of coherency using wavelet transform is presented. Subsequently, the Fourier series non-stationary coherency model is formulated. The parameters are considered piecewise constant variables to represent the non-stationarity of the estimated coherency. The effects of the site and inter-station distance on the proposed model are presented. Parameters related to the site and inter-station distance were obtained. Finally, the proposed model is compared with the ground motion records at the SMART-1 array during Event 45, the stationary model, and the data from the Argostoli rock-site dense array. This indicates that the Fourier series non-stationary coherency model with respect to site conditions for the horizontal component can well match the correlation of the realistic spatially variable seismic ground motion, which is related to the site, time, and inter-station distance.

Based on the Direct Probability Integral Method (DPIM), this study investigates the global seismic reliability of reinforced concrete (RC) frame structures considering the randomness of material parameters and the non-stationarity of ground motions. A doubly non-stationary ground motion model is established using evolutionary power spectrum theory combined with the spectral representation–stochastic function method. A dimensionality reduction technique is adopted to generate ground motion samples compatible with the design response spectrum. A finite element model of the RC frame is developed in Abaqus. Modal analysis and deterministic time history analysis are conducted to obtain the dynamic characteristics and seismic responses of the structure. Based on 600 representative ground motion time histories generated using the maximum frontier (MF) discrepancy sampling method, nonlinear time history analyses are performed. The DPIM is then employed to calculate the statistical characteristics of structural responses and quantify response variability, enabling a rational evaluation of the structural safety margin. Finally, based on the equivalent extreme value event theory and DPIM, the reliability of the structure under a single failure mode and the global reliability under multiple failure modes are computed. The results show that the global reliability of the structure is 82.088%, which is significantly lower than that of any single failure mode. This study provides a quantitative reference for evaluating the global seismic reliability of RC frame structures subjected to nonstationary seismic excitation.

In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectral are proposed to characterize the data derived from the multivariate non-stationary process. The scalogram and instantaneous frequency spectral capture spectral evolution of each component while the coscalogram reveals embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectral provides more detailed portrayal for multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is validated by numerical examples of uniformly and generally modulated ground motions.

This paper presents a spectral representation method for simulation of non-stationary ground motion processes on the basis of Priestley's evolutionary spectral theory. Following this method, sample processes can be generated using a cosine series formula. It is shown that, these sample processes accurately reflect the prescribed characteristics of the evolutionary power spectral density function when the number of the terms in the cosine series is large enough; and the ensemble expected value and the ensemble autocorrelation function approach the corresponding target functions, respectively, as the sample size increases; and these sample processes are asymptotically normal as the number of the terms in the series tends to infinity. Finally, a few special cases of the formula are discussed, one of which is non-stationary white noise process, and other one is reduced to the formula for simulation of stationary stochastic processes.[PUBLICATION ABSTRACT]

This paper proposes a method for simulation of non-stationary ground motion processes having the identical statistical feature, time-dependent power spectrum, with a given ground motion record, on the basis of review of simulation of non-stationary ground motion processes. The method has the following advantages: the sample processes are non-stationary both in amplitude and frequency, and both the amplitude and frequency non-stationarity depend on the target power spectrum; the power spectrum of any sample process does not necessarily accord with the target power spectrum, but statistically, it strictly accords with the target power spectrum. Finally, the method is verified by simulation of one acceleration record in Landers earthquake.[PUBLICATION ABSTRACT]

The current state of the practice in probabilistic seismic hazard analysis (PSHA) employs ergodic ground motion models (GMMs), which assume that the ground motion variability observed in a global database is the same as the variability in ground motion at a single site-source combination. However, the fast-growing empirical ground motion databases indicate significant regional differences in ground motions due to repeatable and systematic source, path, and site effects. These systematic effects, which are spatially correlated, are not consistent with the ergodic assumption, promoting the transition to non-ergodic GMMs for PSHA. In this study, we use Gaussian processes with different covariance functions to model the spatial correlation structures of systematic source, path, and site effects for the Ridgecrest area. We compare the proposed correlation models for Ridgecrest with those previously developed for the ANZA array in terms of predictive performance. We also evaluate the effects of the cell-specific attenuation approach on the spatial correlation structures of path effects. We find that the spatial correlation of systematic source and path effects is best characterized by anisotropic non-stationary covariance functions in Gaussian processes. We also find that the cell-specific attenuation approach with squared grids has limitations in predicting path effects and does not affect the correlation structures significantly for the Ridgecrest database. Finally, comparisons with the ANZA array suggest that the spatial correlation structures of path effects derived from the ANZA array may be transferable to the Ridgecrest area, potentially due to their similarity in crustal heterogeneity.

This work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

Topology optimization has been mainly addressed for structures under static loads using a deterministic setting. Nonetheless, many structural systems are subjected to uncertain dynamic loads, and thus efficient approaches are required to evaluate the optimal topology in such kind of applications. Within this framework, the present paper deals with the topology optimization of multi-story buildings subjected to seismic ground motion. Because of the inherent randomness of the earthquakes, the uncertain system response is determined through a random vibration-based approach in which the seismic ground motion is described as filtered white Gaussian noise with time-varying amplitude and frequency content (i.e., fully non-stationary seismic ground motion). The paper is especially concerned with the assessment of the dynamic response sensitivity for the gradient-based numerical solution of the optimization problem. To this end, an approximated construction of the gradient is proposed in which explicit, exact derivatives with respect to the design variables are computed analytically through direct differentiation for a sub-assembly of elements (up to a single element) resulting from the discretization of the optimizable domain. The proposed strategy is first validated for the simpler case of stationary base excitation by comparing the results with those obtained using an exact approach based on the adjoint method, and its correctness is ultimately verified for the more general case of non-stationary seismic ground motion. Overall, this validation demonstrates that the proposed approach leads to accurate results at low computational effort. Further numerical investigations are finally presented to highlight to what extent the features of the non-stationary seismic ground motion influence the optimal topology.

The field and laboratory evidence of nonlinear soil behaviour, even at small strains, emphasizes the importance of employing nonlinear methods in seismic ground response analysis. Additionally, determination of dynamic characteristics of soil layers always includes some degree of uncertainty. Most of previous stochastic studies of ground response analysis have focused only on variability of soil parameters, and the effect of soil sample location has been mostly ignored. This study attempts to couple nonlinear time-domain ground response analysis with variability of soil parameters considering existing boreholes’ location through a geostatistical method using a program written in MATLAB. To evaluate the efficiency of the proposed method, stochastic seismic ground responses at construction location were compared with those of the non-stationary random field method through real site data. The results demonstrate that applying the boreholes’ location significantly affects not only the ground responses but also their coefficient of variation (COV). Furthermore, the mean value of the seismic responses is affected more considerably by the values of soil parameters at the vicinity of the construction location. It is also inferred that considering boreholes’ location may reduce the COV of the seismic responses. Among the surface responses in the studied site, the values of peak ground displacement (PGD) and peak ground acceleration (PGA) reflect the highest and lowest dispersion due to variability of soil properties through both non-stationary random field and geostatistical methods.

Non-stationary spatially variable ground motions (SVGMs) are commonly modelled as multivariate oscillatory processes based on evolutionary power spectral density (EPSD) functions. The existing conditional simulation algorithms require the known EPSD functions. The EPSD functions are usually assumed to be identical for all locations, which is unreasonable for long-span bridges because variable soil conditions are practically observed at different bridge piers. This paper proposes a conditional simulation algorithm for non-stationary SVGMs in consideration of non-uniform site conditions. The spatial interpolation tool, termed inverse-distance-weighted (IDW) interpolation, is introduced to estimate the EPSD functions at sites without ground motion measurement. Subsequently, the covariance matrix of the random Fourier coefficients of the multivariate oscillatory processes can be calculated. The Kriging estimation is adopted to obtain the unknown random Fourier coefficients, from which the time histories of the non-stationary SVGMs can be conditionally simulated. The proposed conditional simulation algorithm is first validated through a numerical example, in which the EPSD functions of non-uniform sites are represented by a non-stationary Kanai-Tajimi spectrum with different soil parameters. Then, the algorithm is applied to the Jiuzhou Channel Bridge, a navigation channel bridge of the Hong Kong-Zhuhai-Macau Bridge (HZMB), with complex soil and water conditions. Based on the limited in-situ seismic measurement data, the site characteristics in the bridge area are analysed, and the ground motion time histories at all piers can be generated.