Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Women and human development: the capabilities approach

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

Book Information Women and Human Development: The Capabilities Approach By Martha C Nussbaum Cambridge University Press Cambridge/New York 2000 Pp xxi + 312

Women and Human Development: The Capabilities Approach

Community resilience has been addressed across multiple disciplines including environmental sciences, engineering, sociology, psychology, and economics. Interest in community resilience gained momentum following several key natural and human-caused hazards in the United States and worldwide. To date, a comprehensive community resilience model that encompasses the performance of all the physical and socio-economic components from immediate impact through the recovery phase of a natural disaster has not been available. This paper summarizes a literature review of previous community resilience studies with a focus on natural hazards, which includes primarily models of individual infrastructure systems, their interdependencies, and community economic and social systems. A series of national and international initiatives aimed at community resilience are also summarized in this study. This paper suggests extensions of existing modeling methodologies aimed at developing an improved, integrated understanding of resilience that can be used by policy-makers in preparation for future events.

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State of the research in community resilience: progress and challenges.

The resilience of a system is related to its ability to withstand stressors, adapt, and rapidly recover from disruptions. Two significant challenges of resilience analysis are to (1) quantify the resilience associated with a given recovery curve; and (2) develop a rigorous mathematical model of the recovery process. To quantify resilience, a mathematical approach is proposed that systematically describes the recovery curve in terms of partial descriptors, called resilience metrics. The proposed resilience metrics have simple and clear interpretations, and their definitions are general so that they can characterize the resilience associated with any recovery curve. This paper also introduces a reliability-based definition of damage levels which is well-suited for probabilistic resilience analysis. For the recovery modeling, a stochastic formulation is proposed that models the impact of recovery activities and potential disrupting shocks, which could happen during the recovery, on the system state. ...

/pdf/resilience-analysis-a-mathematical-formulation-to-model-4o73ufv2p5.pdf

Resilience analysis: a mathematical formulation to model resilience of engineering systems

Reducing hazard‐induced disruptions to infrastructure functionality is cardinal to regional resilience. Specifically, effective strategies to enhance regional resilience require: (a) devel...

Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure

Probabilistic capacity models and fragility estimates for RC columns retrofitted with FRP composites

A review and assessment of importance sampling methods for reliability analysis

This chapter proposes a novel general stochastic formulation for the Life-Cycle Analysis (LCA) of deteriorating engineering systems. The formulation rigorously formalizes the different aspects of the life-cycle of engineering systems. To capture the probabilistic nature of the proposed formulation, it is named Stochastic Life-Cycle Analysis (SLCA). The life-cycle of an engineering system is shaped by deterioration processes and repair/recovery processes, both characterized by several sources of uncertainty. The deterioration might be due to exposure to environmental conditions and to both routine and extreme loading. The repair and recovery strategies are typically implemented to restore or enhance the safety and functionality of the engineering system. In the SLCA, state-dependent stochastic models are proposed to capture the impact of deterioration processes and repair/recovery strategies on the engineering systems in terms of performance measures like instantaneous reliability and resilience. The formulation integrates the state-dependent stochastic models with the previously developed Renewal Theory-based Life-Cycle Analysis (RTLCA) to efficiently evaluate additional system performance measures such as availability, operation cost, and benefits. The proposed SLCA can be used for the optimization of the initial design and mitigation strategies of engineering systems accounting for their life-cycle performance. As an illustration, the proposed SLCA is used to model the life-cycle of a reinforced concrete bridge, subject to deteriorations caused by corrosion and earthquake excitations. The deteriorated bridge column is repaired using Fiber Reinforced Polymer (FRP) composites. The results show that the deterioration processes significantly affect the performance measures of the example bridge.

Armin Tabandeh

Papers

Resilience analysis: a mathematical formulation to model resilience of engineering systems

Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure

Probabilistic capacity models and fragility estimates for RC columns retrofitted with FRP composites

A review and assessment of importance sampling methods for reliability analysis

Life-Cycle Analysis of Engineering Systems: Modeling Deterioration, Instantaneous Reliability, and Resilience