ALBERT

Description: The Yuan-Agrawal (YA) memory-free approach is employed to study fractional dynamical systems with freeplay nonlinearities subjected to a harmonic excitation, by combining it with the precise integration method (PIM). By the YA method, the original equations are transformed into a set of first-order piecewise-linear ordinary differential equations (ODEs). These ODEs are further separated as three linear inhomogeneous subsystems, which are solved by PIM together with a predictor-corrector process. Numerical examples show that the results by the presented method agree well with the solutions obtained by the Runge-Kutta method and a modified fractional predictor-corrector algorithm. More importantly, the presented method has higher computational efficiency.

Description: 〈span〉〈div〉ABSTRACT〈/div〉The maximum observed peak ground acceleration (PGA) and peak ground velocity (PGV) at various stations during the 2018 Hualien, Taiwan earthquake were 594 Gal and 146  cm/s, respectively. Pulse‐like velocities were observed at all stations within a distance of 4 km from the Milun fault. The horizontal spectral accelerations of the pulse‐like records indicated two obvious amplifications at periods of roughly 1 and 2 s. Natural frequencies of 0.8–1.5 Hz were observed in the region near the Milun fault using microtremor measurements. The spectral acceleration peak at periods of roughly 2 s is mostly seen in the east–west direction, indicating a typical fault‐normal seismic radiation from the fault rupture. Consequently, we contend that the amplifications of spectral acceleration at approximately 1 and 2 s were caused by site amplification and the rupture front, respectively. The site amplification at approximately 1 s may have been one reason for the collapse of medium‐rise buildings during this earthquake. Evident soil nonlinearity resulted in smaller horizontal than vertical PGA at many stations in the near‐fault region.〈/span〉

Description: The strong-motion downhole array (SMDA) in Taipei basin is examined, and the data quality, glitches, and systemic errors in its data are discussed. This seismic network is an array of arrays: the SMDA comprises a total of 32 triggered strong-motion acceleration seismometers spanning eight sites. Each site has one seismometer at the surface and an additional two to four seismometers each collocated at the individual boreholes. Polarity reversals, swapped components, clock desynchronization, and bad components (flat-line signal or aseismic noise) have all been observed and are shown to be occasional issues. Signal-to-noise ratios are generally excellent. The lack of known orientations at depth is the primary issue regarding data quality of the SMDA. Orientations of each borehole seismometer were recorded at the time of installation, but those initial values are not reliable for subsequent events. Furthermore, redetermined azimuthal orientations are inconsistent from event to event, suggesting that the borehole seismometer orientations change over time. Orientation wander is not associated with instrument maintenance. An iterative method to reliably determine borehole seismometer orientations is introduced. Data from each borehole seismometer are rotated and compared with that from a collocated reference station on the surface with known orientation until a maximum correlation is reached. This method is reliable for most events, but may become unreliable for local events in which the incidence of incoming seismic waves is near vertical and shorter wavelengths introduce complicated wave propagation within Taipei basin. Temporal analysis of borehole seismometer orientations shows not only drifting of azimuthal orientations over time, but also erratic changes in orientation in multiples of 90°. This behavior is suggestive of frequent polarity reversals and/or swapped components on one or both of the two horizontal components of each borehole seismometer.

Description: We propose an accurate approach, based on the precise integration method, to solve the aeroelastic system of an airfoil with a pitch hysteresis. A major procedure for achieving high precision is to design a predictor-corrector algorithm. This algorithm enables accurate determination of switching points resulting from the hysteresis. Numerical examples show that the results obtained by the presented method are in excellent agreement with exact solutions. In addition, the high accuracy can be maintained as the time step increases in a reasonable range. It is also found that the Runge-Kutta method may sometimes provide quite different and even fallacious results, though the step length is much less than that adopted in the presented method. With such high computational accuracy, the presented method could be applicable in dynamical systems with hysteresis nonlinearities.

Description: 〈span〉〈div〉Abstract〈/div〉Ground‐motion prediction equations (GMPEs), also called ground‐motion models and attenuation relationships, are empirical models widely used in probabilistic seismic hazard analysis (PSHA). They estimate the conditional distribution of ground shaking at a site given an earthquake of a certain magnitude occurring at a nearby location. In the past decade, the increasing interest in assessing earthquake risk and resilience of spatially distributed portfolios of buildings and infrastructure has motivated the modeling of ground‐motion spatial correlation. This introduces further challenges for researchers to develop statistically rigorous and computationally efficient algorithms to perform ground‐motion model estimation with spatial correlation. To this goal, we introduce a one‐stage ground‐motion estimation algorithm, called the scoring estimation approach, to fit ground‐motion models with spatial correlation. The scoring estimation approach is introduced theoretically and numerically, and it is proven to have desirable properties on convergence and computation. It is a statistically robust method, producing consistent and statistically efficient estimators of inter‐ and intraevent variances and parameters in spatial correlation functions. The performance of the scoring estimation approach is assessed through a comparison with the multistage algorithm proposed by 〈a href="https://pubs.geoscienceworld.org/bssa#rf28"〉Jayaram and Baker (2010)〈/a〉 in a simulation‐based application. The results of the simulation study show that the proposed scoring estimation approach presents comparable or higher accuracy in estimating ground‐motion model parameters, especially when the spatial correlation becomes smoother. The simulation study also shows that ground‐motion models with spatial correlation built via the scoring estimation approach can be used for reliable ground‐shaking intensity predictions. The performance of the scoring estimation approach is further discussed under the ignorance of spatial correlation, and we find that neglecting spatial correlation in ground‐motion models may result in overestimation of interevent variance and underestimation of intraevent variance and thus inaccurate predictions.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉Ground‐motion prediction equations (GMPEs), also called ground‐motion models and attenuation relationships, are empirical models widely used in probabilistic seismic hazard analysis (PSHA). They estimate the conditional distribution of ground shaking at a site given an earthquake of a certain magnitude occurring at a nearby location. In the past decade, the increasing interest in assessing earthquake risk and resilience of spatially distributed portfolios of buildings and infrastructure has motivated the modeling of ground‐motion spatial correlation. This introduces further challenges for researchers to develop statistically rigorous and computationally efficient algorithms to perform ground‐motion model estimation with spatial correlation. To this goal, we introduce a one‐stage ground‐motion estimation algorithm, called the scoring estimation approach, to fit ground‐motion models with spatial correlation. The scoring estimation approach is introduced theoretically and numerically, and it is proven to have desirable properties on convergence and computation. It is a statistically robust method, producing consistent and statistically efficient estimators of inter‐ and intraevent variances and parameters in spatial correlation functions. The performance of the scoring estimation approach is assessed through a comparison with the multistage algorithm proposed by 〈a href="https://pubs.geoscienceworld.org/bssa#rf28"〉Jayaram and Baker (2010)〈/a〉 in a simulation‐based application. The results of the simulation study show that the proposed scoring estimation approach presents comparable or higher accuracy in estimating ground‐motion model parameters, especially when the spatial correlation becomes smoother. The simulation study also shows that ground‐motion models with spatial correlation built via the scoring estimation approach can be used for reliable ground‐shaking intensity predictions. The performance of the scoring estimation approach is further discussed under the ignorance of spatial correlation, and we find that neglecting spatial correlation in ground‐motion models may result in overestimation of interevent variance and underestimation of intraevent variance and thus inaccurate predictions.〈/span〉