http://www.ukm.my/kamal3/psm/pfpstochastics.pdf

The stochastic finite element method: Past, present and future

PART 1 DEALS WITH THE DYNAMIC ASPECTS OF THE SUBJECT: MATHEMATICAL ANALYSIS OF SYSTEMS SUBJECTED TO INDEPENDENT VIBRATIONS BY MEANS OF MATHEMATICAL MODELS. THE ANALYTICAL SYSTEMS USED ARE NON-LINEAR SYSTEMS, HYDRODYNAMICS AND NUMERICAL METHODS. PART 2 EXAMINES SEISMIC MOVEMENTS, THE DYNAMIC BEHAVIOUR OF STRUCTURES AND THE BASIC CONCEPTS OF THE SEISMIC DESIGN OF STRUCTURES.

Fundamentals of earthquake engineering

Methods to calculate extreme wind speeds are described and reviewed, including ‘classical’ methods based on the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD), and approaches designed specifically to deal with short data sets. The emphasis is very much on the needs of users who seek an accurate method to derive extreme wind speeds but are not fully conversant with up-to-date developments in this complex subject area. First, ‘standard’ methods are reviewed: annual maxima, independent storms, r-largest extremes with the GEV distribution, and peak-over-threshold extremes with the GPD. Techniques for calculating the distribution parameters and quantiles are described. There follows a discussion of the factors which must be considered in order to fulfil the criterion that the data should be independent and identically distributed, and in order to minimize standard errors. It is commonplace in studies of extreme wind speeds that the time series available for analysis are very short. Finally, therefore, the paper deals with techniques applicable to data sets as short as two years, including simulation modelling and methods based on the parameters of the parent distribution.

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1017/S1350482799001103

A review of methods to calculate extreme wind speeds

The dynamic behavior of rigid-block structures resting on a rigid foundation subjected to horizontal harmonic excitation is examined. For slender structures, the nonlinear equation of motion is approximated by a piecewise linear equation. Using this approximation for an initially quiescent structure, safe or no-toppling and unsafe regions are identified in an excitation amplitude versus excitation frequency plane. Furthermore, several possible modes of steady-state response are detected, and analytical procedures are developed for determining the amplitudes of the predominant modes and for performing stability analyses. It is shown that the produced stability diagrams can be beneficial to assessing the toppling potential of a rigid-block structure under a given amplitude-frequency combination of harmonic excitation; in this manner the integration of the equation of motion is circumvented.

Closure of "Rocking of Rigid Blocks Due to Harmonic Shaking"

Masonry structures, although classically suitable to withstand gravitational loads, are sensibly vulnerable if subjected to extraordinary actions such as earthquakes, exhibiting cracks even for events of moderate intensity compared to other structural typologies like as reinforced concrete or steel buildings. In the last half-century, the scientific community devoted a consistent effort to the computational analysis of masonry structures in order to develop tools for the prediction (and the assessment) of their structural behavior. Given the complexity of the mechanics of masonry, different approaches and scales of representation of the mechanical behavior of masonry, as well as different strategies of analysis, have been proposed. In this paper, a comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented. Although a fully coherent collocation of all the modeling approaches is substantially impossible due to the peculiar features of each solution proposed, this classification attempts to make some order on the wide scientific production on this field. The modeling strategies are herein classified into four main categories: block-based models, continuum models, geometry-based models, and macroelement models. Each category is comprehensively reviewed. The future challenges of computational analysis of masonry structures are also discussed.

/pdf/modeling-strategies-for-the-computational-analysis-of-4lh0cu9a67.pdf

Modeling Strategies for the Computational Analysis of Unreinforced Masonry Structures: Review and Classification

Homogenization of non-periodic masonry structures

Simulation of non-Gaussian field applied to wind pressure fluctuations

A parametric investigation of wind-induced cable fatigue

Lateral loads carrying capacity and minimum thickness of circular and pointed masonry arches

This paper and a companion paper present a complete study (analysis, modeling, and numerical simulation) of the wind pressure field on prismatic tall buildings, taking into account its non-Gaussian nature. In this paper, the analysis of the data obtained by wind tunnel experimental tests is used to provide a systematic characterization of the stochastic pressure field. First, the experimental work is briefly described; then the wind pressure statistical moments up to the second order (mean, standard deviation, and spectral densities) are obtained. Comparisons with results reported in the literature are used to validate the reliability of the experimental measurements. Histograms of the experimental data in the separated flow regions (vortex shedding and wake) are used to identify the non-Gaussian nature of the pressure fluctuations. Maps of higher-order statistical moments (skewness and kurtosis coefficients) at the four faces of the model are proposed to obtain an easy tool to localize regions with non-G...

Vittorio Gusella

Papers

Homogenization of non-periodic masonry structures

Simulation of non-Gaussian field applied to wind pressure fluctuations

A parametric investigation of wind-induced cable fatigue

Lateral loads carrying capacity and minimum thickness of circular and pointed masonry arches

Non-Gaussian Wind Pressure on Prismatic Buildings. I: Stochastic Field