Unreinforced masonry (URM) structures represent a significant portion of the residential building stock of the Central and Eastern United States (CEUSA), accounting for 15% of homes in the 8-state region impacted by the New-Madrid Seismic Zone and an even greater portion of the building stock in most other regions of the world. In addition to significant population, the brittle nature of URM buildings further supports a thorough consideration of seismic response given the susceptibility to severe failure modes. Currently, there is a pressing need for analytically based fragility curves for URM buildings. In order to improve the estimation of damage state probabilities through the development of simulation-based masonry fragilities, an extensive literature survey is conducted on pushover analysis of URM structures. Using this data, capacity diagrams are generated, from which damage exceedance limit states are defined.

Demand is simulated using synthetically derived accelerograms representative of the CEUSA. Structural response is evaluated using an advanced capacity spectrum method developed at the Mid-America Earthquake Center. Capacity, demand, and response are thus derived analytically and response data is used to generate an improved and uniform set of fragility curves for use in loss-assessment software via a framework amenable to rapid expansion of pushover database and variation of ground motion records. A set of best practices is hereby developed for selection and use of experimental and analytical data, input ground motions, analysis of structural capacity, limit state definitions, seismic design categories, and probabilistic analysis methods. Curves are expressed in multiple forms for wide range of use in loss-assessment applications. Results are discussed and compared with other relationships developed in the literature, along with those generated using HAZUS opinion-based capacity data. The parameters of the improved fragility relationships developed and presented in this thesis are provided and suggested for reliable use in seismic loss assessment software.

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. This book is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. This book serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester intro! duction to statistical methods and a thoeretical linear models course. This book emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts.

Excellent Applied Regression Text

This text covers all aspects of applied regression. In addition, it covers required background, theory, and several examples. It is written clearly and develops logically. I used this text for a course, and I highly recommend this text.