For performance-based design, nonlinear dynamic structural analysis for various types of input ground motions is required. Stochastic (simulated) ground motions are sometimes useful as input motions, because unlike recorded motions they are not limited in number and because their properties can be varied systematically to study the impact of ground motion properties on structural response. This dissertation describes an approach by which the wavelet packet transform can be used to characterize complex time-varying earthquake ground motions, and it illustrates the potential benefits of such an approach in a variety of earthquake engineering applications. The proposed model is based on Thr´ainsson and Kiremidjian (2002), which use Fourier amplitudes and phase differences to simulate ground motions and attenuation models to their model parameters. We extend their model using wavelet packet transform since it can control the time and frequency characteristic of time series. The time- and frequency-varying properties of real ground motions can be captured using wavelet packets, so a model is developed that requires only 13 parameters to describe a given ground motion. These 13 parameters are then related to seismological variables such as earthquake magnitude, distance, and site condition, through regression analysis that captures trends in mean values, standard deviations and correlations of these parameters observed in a large database of recorded strong ground motions. The resulting regression equations then form a model that can be used to predict ground motions for a future earthquake scenario; this model is analogous to widely used empirical ground motion prediction models (formerly called "attenuation models") except that this model predicts entire time series rather than only response spectra. The ground motions produced using this predictive model are explored in detail, and are shown to have elastic response spectra, inelastic response spectra, durations, mean periods, etc., that are consistent in both mean and variability to existing published predictive models for those properties. That consistency allows the proposed model to be used in place of existing models for probabilistic seismic hazard analysis (PSHA) calculations. This new way to calculate PSHA is termed "simulation-based probabilistic seismic hazard analysis" and it allows a deeper understanding of ground motion hazard and hazard deaggregation than is possible with traditional PSHA because it produces a suite of potential ground motion time histories rather than simply a distribution of response spectra. The potential benefits of this approach are demonstrated and explored in detail. Taking this analysis even further, this suite of time histories can be used as input for nonlinear dynamic analysis of structures, to perform a risk analysis (i.e., "probabilistic seismic demand analysis") that allows computation of the probability of the structure exceeding some level of response in a future earthquake. These risk calculations are often performed today using small sets of scaled recorded ground motions, but that approach requires a variety of assumptions regarding important properties of ground motions, the impacts of ground motion scaling, etc. The approach proposed here facilitates examination of those assumptions, and provides a variety of other relevant information not obtainable by that traditional approach.

For performance-based design, nonlinear dynamic structural analysis for various types of input ground motions is required. Stochastic (simulated) ground motions are sometimes useful as input motions, because unlike recorded motions they are not limited in number and because their properties can be varied systematically to study the impact of ground motion properties on structural response. This dissertation describes an approach by which the wavelet packet transform can be used to characterize complex time-varying earthquake ground motions, and it illustrates the potential benefits of such an approach in a variety of earthquake engineering applications. The proposed model is based on Thráinsson and Kiremidjian (2002), which use Fourier amplitudes and phase differences to simulate ground motions and attenuation models to their model parameters. We extend their model using wavelet packet transform since it can control the time and frequency characteristic of time series. The time- and frequency-varying properties of real ground motions can be captured using wavelet packets, so a model is developed that requires only 13 parameters to describe a given ground motion. These 13 parameters are then related to seismological variables such as earthquake magnitude, distance, and site condition, through regression analysis that captures trends in mean values, standard deviations and correlations of these parameters observed in a large database of recorded strong ground motions. The resulting regression equations then form a model that can be used to predict ground motions for a future earthquake scenario; this model is analogous to widely used empirical ground motion prediction models (formerly called "attenuation models") except that this model predicts entire time series rather than only response spectra. The ground motions produced using this predictive model are explored in detail, and are shown to have elastic response spectra, inelastic response spectra, durations, mean periods, etc., that are consistent in both mean and variability to existing published predictive models for those properties. That consistency allows the proposed model to be used in place of existing models for probabilistic seismic hazard analysis (PSHA) calculations. This new way to calculate PSHA is termed "simulation-based probabilistic seismic hazard analysis" and it allows a deeper understanding of ground motion hazard and hazard deaggregation than is possible with traditional PSHA because it produces a suite of potential ground motion time histories rather than simply a distribution of response spectra. The potential benefits of this approach are demonstrated and explored in detail. Taking this analysis even further, this suite of time histories can be used as input for nonlinear dynamic analysis of structures, to perform a risk analysis (i.e., "probabilistic seismic demand analysis") that allows computation of the probability of the structure exceeding some level of response in a future earthquake. These risk calculations are often performed today using small sets of scaled recorded ground motions, but that approach requires a variety of assumptions regarding important properties of ground motions, the impacts of ground motion scaling, etc. The approach proposed here facilitates examination of those assumptions, and provides a variety of other relevant information not obtainable by that traditional approach.

This self-contained book focuses on the safety assessment of existing structures subjected to multi-hazard scenarios through advanced numerical methods. Whereas the focus is on concrete dams and nuclear containment structures, the presented methodologies can also be applied to other large-scale ones. The authors explains how aging and shaking ultimately lead to cracking, and how these complexities are compounded by their random nature. Nonlinear (static and transient) finite element analysis is hence integrated with both earthquake engineering and probabilistic methods to ultimately derive capacity or fragility curves through a rigorous safety assessment. Expanding its focus beyond design aspects or the state of the practice (i.e., codes), this book is composed of seven sections: Fundamentals: theoretical coverage of solid mechnics, plasticity, fracture mechanics, creep, seismology, dynamic analysis, probability and statistics Damage: that can affect concrete structures, such as cracking of concrete, AAR, chloride ingress, and rebar corrosion, Finite Element: formulation for both linear and nonlinear analysis including stress, heat and fracture mechanics, Engineering Models: for soil/fluid-structure interaction, uncertainty quantification, probablilistic and random finite element analysis, machine learning, performance based earthquake engineering, ground motion intensity measures, seismic hazard analysis, capacity/fragility functions and damage indeces, Applications to dams through potential failure mode analyses, risk-informed decision making, deterministic and probabilistic examples, Applications to nuclear structures through modeling issues, aging management programs, critical review of some analyses, Other applications and case studies: massive RC structures and bridges, detailed assessment of a nuclear containment structure evaluation for license renewal. This book should inspire students, professionals and most importantly regulators to rigorously apply the most up to date scientific methods in the safety assessment of large concrete structures.

While numerous advanced statistical approaches have recently been developed for quantitative trait loci (QTL) mapping, the methods are scattered throughout the literature. Statistical Methods for QTL Mapping brings together many recent statistical techniques that address the data complexity of QTL mapping. After introducing basic genetics topics and statistical principles, the author discusses the principles of quantitative genetics, general statistical issues of QTL mapping, commonly used one-dimensional QTL mapping approaches, and multiple interval mapping methods. He then explains how to use a feature selection approach to tackle a QTL mapping problem with dense markers. The book also provides comprehensive coverage of Bayesian models and MCMC algorithms and describes methods for multi-trait QTL mapping and eQTL mapping, including meta-trait methods and multivariate sequential procedures. This book emphasizes the modern statistical methodology for QTL mapping as well as the statistical issues that arise during this process. It gives the necessary biological background for statisticians without training in genetics and, likewise, covers statistical thinking and principles for geneticists. Written primarily for geneticists and statisticians specializing in QTL mapping, the book can also be used as a supplement in graduate courses or for self-study by PhD students working on QTL mapping projects.

Under the pressure of harsh environmental conditions and natural hazards, large parts of the world population are struggling to maintain their livelihoods. Population growth, increasing land utilization and shrinking natural resources have led to an increasing demand of improved efficiency of existing technologies and the development of new ones. A