The book addresses the topic of on-line implementation of structural and mechanical design criteria as an explicit part of optimal control schemes. The intention of the present research monograph is to reflect recent developments within this area. Examples of application of relevant control algorithms are included to illustrate their practical implementation. These examples are mainly taken from the area of marine technology with the multi-component external loading being represented as both varying in time and with magnitudes that are represented as statistical quantities. The relevant target group will be mechanical and structural engineers that are concerned with “smart components and structures” where optimal design principles and control actuators are combined. The book is also relevant for engineers e.g. involved in mechatronics and control applications.
The book exposes three alternative and competing approaches to uncertainty analysis in engineering. It is composed of some essays on various sub-topics like random vibrations, probabilistic reliability, fuzzy-sets-based analysis, unknown-but-bounded variables, stochastic linearization, possible difficulties with stochastic analysis of structures.
Increasing demand on improving the resiliency of modern structures and infrastructure requires ever more critical and complex designs. Therefore, the need for accurate and efficient approaches to assess uncertainties in loads, geometry, material properties, manufacturing processes, and operational environments has increased significantly. Reliability-based techniques help develop more accurate initial guidance for robust design and help to identify the sources of significant uncertainty in structural systems. Reliability-Based Analysis and Design of Structures and Infrastructure presents an overview of the methods of classical reliability analysis and design most associated with structural reliability. It also introduces more modern methods and advancements, and emphasizes the most useful methods and techniques used in reliability and risk studies, while elaborating their practical applications and limitations rather than detailed derivations. Features: Provides a practical and comprehensive overview of reliability and risk analysis and design techniques. Introduces resilient and smart structures/infrastructure that will lead to more reliable and sustainable societies. Considers loss elimination, risk management and life-cycle asset management as related to infrastructure projects. Introduces probability theory, statistical methods, and reliability analysis methods. Reliability-Based Analysis and Design of Structures and Infrastructure is suitable for researchers and practicing engineers, as well as upper-level students taking related courses in structural reliability analysis and design.
This text offers comprehensive and principled, yet practical, guidelines to critical infrastructures resilience. Extreme events and stresses, including those that may be unprecedented but are no longer surprising, have disproportionate effects on critical infrastructures and hence on communities, cities, and megaregions. Critical infrastructures include buildings and bridges, dams, levees, and sea walls, as well as power plants and chemical factories, besides lifeline networks such as multimodal transportation, power grids, communication, and water or wastewater. The growing interconnectedness of natural-built-human systems causes cascading infrastructure failures and necessitates simultaneous recovery. This text explores the new paradigm centered on the concept of resilience by approaching the challenges posed by globalization, climate change, and growing urbanization on critical infrastructures and key resources through the combination of policy and engineering perspectives. It identifies solutions that are scientifically credible, data driven, and sound in engineering principles while concurrently informed by and supportive of social and policy imperatives. Critical Infrastructures Resilience will be of interest to students of engineering and policy.
The Danubian provinces represent one of the largest macro-units within the Roman Empire, with a large and rich heritage of Roman material evidence. Although the notion itself is a modern 18th-century creation, this region represents a unique area, where the dominant, pre-Roman cultures (Celtic, Illyrian, Hellenistic, Thracian) are interconnected within the new administrative, economic and cultural units of Roman cities, provinces and extra-provincial networks. This book presents the material evidence of Roman religion in the Danubian provinces through a new, paradigmatic methodology, focusing not only on the traditional urban and provincial units of the Roman Empire, but on a new space taxonomy. Roman religion and its sacralized places are presented in macro-, meso- and micro-spaces of a dynamic empire, which shaped Roman religion in the 1st-3rd centuries AD and created a large number of religious glocalizations and appropriations in Raetia, Noricum, Pannonia Superior, Pannonia Inferior, Moesia Superior, Moesia Inferior and Dacia. Combining the methodological approaches of Roman provincial archaeology and religious studies, this work intends to provoke a dialogue between disciplines rarely used together in central-east Europe and beyond. The material evidence of Roman religion is interpreted here as a dynamic agent in religious communication, shaped by macro-spaces, extra-provincial routes, commercial networks, but also by the formation and constant dynamics of small group religions interconnected within this region through human and material mobilities. The book will also present for the first time a comprehensive list of sacralized spaces and divinities in the Danubian provinces.
This book proposes, for the first time, a basic formulation for structural control that takes into account the stochastic dynamics induced by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for stochastic optimal control of randomly-excited engineering structures in the context of probability density evolution methods, such as physically-based stochastic optimal (PSO) control. By logically integrating randomness into control gain, the book helps readers design elegant control systems, mitigate risks in civil engineering structures, and avoid the dilemmas posed by the methods predominantly applied in current practice, such as deterministic control and classical linear quadratic Gaussian (LQG) control associated with nominal white noises.
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
In Stochastic Dynamics of Structures, Li and Chen present a unified view of the theory and techniques for stochastic dynamics analysis, prediction of reliability, and system control of structures within the innovative theoretical framework of physical stochastic systems. The authors outline the fundamental concepts of random variables, stochastic process and random field, and orthogonal expansion of random functions. Readers will gain insight into core concepts such as stochastic process models for typical dynamic excitations of structures, stochastic finite element, and random vibration analysis. Li and Chen also cover advanced topics, including the theory of and elaborate numerical methods for probability density evolution analysis of stochastic dynamical systems, reliability-based design, and performance control of structures. Stochastic Dynamics of Structures presents techniques for researchers and graduate students in a wide variety of engineering fields: civil engineering, mechanical engineering, aerospace and aeronautics, marine and offshore engineering, ship engineering, and applied mechanics. Practicing engineers will benefit from the concise review of random vibration theory and the new methods introduced in the later chapters. "The book is a valuable contribution to the continuing development of the field of stochastic structural dynamics, including the recent discoveries and developments by the authors of the probability density evolution method (PDEM) and its applications to the assessment of the dynamic reliability and control of complex structures through the equivalent extreme-value distribution." —A. H-S. Ang, NAE, Hon. Mem. ASCE, Research Professor, University of California, Irvine, USA "The authors have made a concerted effort to present a responsible and even holistic account of modern stochastic dynamics. Beyond the traditional concepts, they also discuss theoretical tools of recent currency such as the Karhunen-Loeve expansion, evolutionary power spectra, etc. The theoretical developments are properly supplemented by examples from earthquake, wind, and ocean engineering. The book is integrated by also comprising several useful appendices, and an exhaustive list of references; it will be an indispensable tool for students, researchers, and practitioners endeavoring in its thematic field." —Pol Spanos, NAE, Ryon Chair in Engineering, Rice University, Houston, USA
Deterministic optimal control has contributed to the field of structural optimization through the introduction of new concepts, alternative ways of viewing design problems and a systematic methodology for solving design problems. It remains to show the applicability of stochastic control to the probabilistic structural optimization problem. Here the applicability is not so self evident but its usefulness can be demonstrated. The paper examines open-loop, closed-loop, open-loop feedback and m-measurement feedback control policies and gives the optimal result for the general nonlinear structural optimization problem. The special linear-quadratic problem is examined along with numerical approaches to solving the general nonlinear problem, including the method of stochastic approximation. The design of members in flexure and compression is used to illustrate the methodology. (Author).
