Earthquake ground-motion relationships for soil and rock sites in Iran have been developed based on the specific barrier model (SBM) used within the context of the stochastic modeling and calibrated against up-to-date Iranian strong-motion data. A total of 171 strong-motion accelerograms recorded at distances of up to 200 km from 24 earthquakes with moment magnitudes ranging from Mw 5.2 to 7.4 are used to determine the region-specific source parameters of this model. Regression analysis was conducted using the “random effects” methodology that considers both earthquake-to-earthquake (inter-event) variability and within-earthquake (intra-event) variability to effectively handle the problem of weighting observations from different earthquakes. The minimization of the error function in each iteration of the “random effects” procedure was performed using the genetic algorithm method. The residuals are examined against available Iranian strong-motion data to confirm that the model predictions are unbiased and that there are no significant residual trends with distance and magnitude. No evidence of self-similarity breakdown is observed between the source radius and its seismic moment. To verify the robustness of the results, tests were performed to confirm that the results are unchanged if the number of observations is changed by removing different randomly selected datasets from the original database. Stochastic simulations, using the derived SBM, are then performed to predict peak ground-motion and response spectra parameters for a wide range of magnitudes and distances. The stochastic SBM predictions agree well with the new empirical regression equations proposed for Iran, Europe and Middle East in the magnitude–distance ranges well represented by the data. It has been shown that the SBM of this study provides unbiased ground-motion estimates over the entire frequency range of most engineering interests (1–10 Hz) for the Iranian earthquakes. Our results are also important for the assessment of hazards in other seismically active environments in the Middle East and Mediterranean regions. 相似文献
Produced water discharge accounts for the greater portion of wastes arising from offshore oil and gas production operations. Development and expansion of Canada’s offshore oil and gas reserves has led to concerns over the potential long-term impacts of produced water discharges to the ocean. To examine this emerging environmental issue at a regional scale, an integrated risk assessment approach was developed in this study based on the princeton ocean model (POM), a random walk (RW) and Monte Carlo simulation. The use of water quality standards arrayed in a Monte Carlo design in the developed approach has served to reflect uncertainties and quantify environmental risks associated with produced water discharge. The model was validated against field data from a platform operating off Canada’s east coast, demonstrating its usefulness in supporting effective management of future produced water discharge. 相似文献
High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management
of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies
for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming.
One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through
a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data
at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with
the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification
of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar
scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function,
and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is
tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four
different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only
between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall
data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very
well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not
necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme
caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such
methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic
modeling. 相似文献
On the basis of the pseudo-excitation method (PEM), a random vibration methodology is formulated for the seismic analysis of multi-supported structures subjected to spatially varying ground motions. The ground motion spatial variability consists of the wave passage, incoherence and site–response effects. Advantages of this method are that less computation effort is required and that the cross-correlations both between normal modes and between excitations are automatically included. Random seismic responses of a realistic long-span bridge due to the wave passage, incoherence and site–response effects are extensively investigated. It is shown that all these effects have significant influence on the seismic response of the structure. 相似文献