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Regional seismic loss and engineering applications generally require the simulation of spatially distributed ground motions using multiple intensity measures (IMs), which can be described by spatial correlations. In this study, a geostatistical analysis is conducted to obtain spatial correlations for peak ground acceleration and spectral acceleration using more than 2000 measured recordings from eight earthquake events that occurred in California recently. In general, the spatial correlations of IMs are higher under uniform site conditions than in areas with varying site conditions. Considering the effects of site conditions on spatial correlations, we establish a predictive equation linking spatial correlation range and $V_{s}30$ values. This facilitates the estimation of spatial correlation under varying site conditions in regions with limited observational records. Subsequently, we directly obtain the spatial correlations based on geological information. In addition, considering the high similarity of IMs within a small scale of less than 1km, the spatial correlations are replaced by lagged coherence at a small separation distance instead of IMs to describe the variation in the Fourier phase. The spatial variation in any region can be described by combining coherence and spatial correlations.

When a CDMA signal is passed through an RF transmitter, nonlinear elements cause spectral regrowth which result in reduction of spectral efficiency. CDMA signal has a pseudo noise nature; hence its mathematical treatment is too complex to analysis. In this paper, first it will be shown how to simplify the complex mathematics of CDMA signal. Then, a deterministic signal replaces the CDMA signal and the system response to both of them is calculated. In this paper, for the first time, ACPR is explicitly calculated for both the CDMA and deterministic signals. ACPR is calculated in terms of the nonlinear system coefficients and input power, and therefore, can be used in design objectives. In addition, it will be shown that if input power of the deterministic signal is multiplied by (i.e., correction factor), ACPR error of these kinds of signals in -55 dBc is less than 2.2% for the system nonlinearity orders up to 13. This correction factor is obtained by both theoretical and simulation methods.

As the phenomenon of fatigue damage is a common failure mode of equipment, the reliability evaluation and life prediction have become a hot-spot. The precise prediction of fatigue life in the initiation stage has become necessary. The common prediction study for structural fatigue life takes less influence factors into consideration. The common prediction results of fatigue life cannot be quantitatively corrected by the influence factors at the same time. This paper presents a research on the prediction approach for structural fatigue life based on the multi-factor correction. The influence of some factors on the fatigue life was analyzed and the prediction approach for structural fatigue life based on multi-factor correction was raised. Then the fatigue life of axle housing was predicted by using the corrected approach, as the case study. Moreover, the result predicted by the local stress–strain approach and the experimental data were used to verify the accuracy of the corrected approach. It can be clearly demonstrated by the results that the corrected prediction approach can be used to achieve the precision fatigue life for engineering structures. Further, it is also a prediction approach endowed with engineering application prospects.

A new method for obtaining equivalent stress–strain curves of bilinear and Swift law material models from the numerical results of shear–compression test (SCT) is developed. This task is conducted on the basis of the approximate analytical relations for the compression and shear conditions at the gauge section and by the application of correction factors. Logarithmic correction models are developed and used to generalize the geometry of an optimum shear–compression specimen (SCS) that is calibrated by one-factor-at-time method for a reference material. Furthermore, a dataset created from 125 finite element simulations is analyzed by data analysis techniques, and a universal nonlinear strain hardening relationship is determined. This model is used to predict the stress–strain curves directly from the force–displacement curve of the SCS. The input and output stress–strain curves are in good agreement with an average error of approximately 3%. The numerical findings of this study provide a foundation to develop a general quantitative relationship to study the behavior of materials by SCT.