Fall Lectures 2016 in relation to risk by Risk Center Professors
Please see full list of lectures/courses here.
Security engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.
The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.
Topics covered include
* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems
Das Vorgehenskonzept wird Schritt für Schritt anhand eines Satzes von Fallstudienobjekten erklärt und von den Studierenden angewendet. Hierbei lernen Sie die Verknüpfung folgender Kompetenzen:
Risikoanalyse - Was kann passieren?
- Naturgefahren-Prozesse in ihren Grundzügen charakterisieren und Resultate aus Modellrechnungen integrieren.
- Einer bestimmten Gefahr exponierte Leben und Objekte identifizieren und ihre mögliche Beeinträchtigung oder Beschädigung abschätzen.
Risikobewertung - Was darf passieren?
- Ansätze zur Festlegung akzeptabler Risiken für Leben und Objekte anwenden, um Schutzdefizite im Raum zu bestimmen.
- Ursachen von Konflikten zwischen Risikowahrnehmung und Risikoanalyse erklären.
Risikomanagement - Was ist zu tun?
- Wirkungsprinzipien von Massnahmen zur Risikoreduktion erklären.
- Für die Bemessung von Massnahmen massgebende Gefährdungsbilder beschreiben.
- Anhand eines Zielkatalogs die beste Alternative aus einer Menge denkbarer Massnahmen bestimmen.
- Prinzipien der Risk-Governance erklären.
Finding solutions: what is complexity, problem solving cycle.
Implementing solutions: project management, critical path method, quality control feedback loop.
Controlling solutions: Vensim software, feedback cycles, control parameters, instabilities, chaos, oscillations and cycles, supply and demand, production functions, investment and consumption
A successful participant of the course is able to:
- understand why most real problems are not simple, but require solution methods that go beyond algorithmic and mathematical approaches
- apply the problem solving cycle as a systematic approach to identify problems and their solutions
- calculate project schedules according to the critical path method
- setup and run systems dynamics models by means of the Vensim software
- identify feedback cycles and reasons for unintended systems behavior
- analyse the stability of nonlinear dynamical systems and apply this to macroeconomic dynamics
Why are problems not simple? Why do some systems behave in an unintended way? How can we model and control their dynamics? The course provides answers to these questions by using a broad range of methods encompassing systems oriented management, classical systems dynamics, nonlinear dynamics and macroeconomic modeling.
The course is structured along three main tasks:
1. Finding solutions
2. Implementing solutions
3. Controlling solutions
PART 1 introduces complexity as a system immanent property that cannot be simplified. It introduces the problem solving cycle, used in systems oriented management, as an approach to structure problems and to find solutions.
PART 2 discusses selected problems of project management when implementing solutions. Methods for identifying the critical path of subtasks in a project and for calculating the allocation of resources are provided. The role of quality control as an additional feedback loop and the consequences of small changes are discussed.
PART 3, by far the largest part of the course, provides more insight into the dynamics of existing systems. Examples come from biology (population dynamics), management (inventory modeling, technology adoption, production systems) and economics (supply and demand, investment and consumption). For systems dynamics models, the software program VENSIM is used to evaluate the dynamics. For economic models analytical approaches, also used in nonlinear dynamics and control theory, are applied. These together provide a systematic understanding of the role of feedback loops and instabilities in the dynamics of systems. Emphasis is on oscillating phenomena, such as business cycles and other life cycles.
Weekly self-study tasks are used to apply the concepts introduced in the lectures and to come to grips with the software program VENSIM.
The course explains the key concepts and mechanisms of financial economics, their depth and then stresses how and why the theories and models fail and how this is impacting investment strategies and even a global view of citizenship, given the present developing crises in the US since 2007 and in Europe since 2010.
- Development of the concepts and tools to understand these risks and master them.
- Working knowledge of the main concepts and tools in finance (Portfolio theory, asset pricing, options, real options, bonds, interest rates, inflation, exchange rates)
- Strong emphasis on challenging assumptions and developing a systemic understanding of financial markets and their many dimensional risks
This course completes the series of two courses on seismic design of structures at ETHZ. Building on the material covered in Seismic Design of Structures I, the following advanced topics are covered in this course: 1) behavior and non-linear response of structural systems under earthquake excitation; 2) seismic behavior and design of moment frame, braced frame and shear wall structures; 3) fundamentals of seismic isolation; and 4) assessment and retrofit of existing buildings. These topics are discussed from the standpoint of performance-based design.
Advanced topics covered in this course are: 1) probabilistic seismic hazard analysis; 2) probabilistic seismic risk analysis; 3) seismic risk management using structural and financial engineering means; and, time permitting, 4) advanced topics in systemic probabilistic risk evaluation.
This course extends the series of two courses on seismic design of structures at ETHZ and introduces the topic of probabilistic seismic risk analysis and seismic risk management for the build environment and civil infrastructure systems. The following advanced topics will be covered in this course: 1) probabilistic seismic hazard analysis; 2) probabilistic seismic risk analysis; 3) seismic risk management using structural and financial engineering means; and, time permitting, 4) advanced topics in systemic probabilistic risk evaluation.
ETH Seismic Design of Structures I course (101-0188-00), or equivalent. Students are expected to understand the seismological nature of earthquakes, to characterize the ground motion excitation, to analyze the response of elastic single- and multiple-degree-of-freedom systems to earthquake excitation, to use the concept of response and design spectrum, to compute the equivalent seismic loads on simple structures, and to perform code-based seismic design of simple structures.
Structural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
The goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
Railway safety policies and safety concepts, command and control technologies for railways, optimization systems, European Train Control System, reliability availability maintainability safety (RAMS) of railway systems.
The students comprehend the main principles of safety, reliability and optimization for railway systems and understand the basic concepts of command and control technologies for railways.
Railway safety strategies
o Safety in public transport
o Safety relevant characteristic of railway transport
o Safety requirements for railway transport
o Safety concepts
Command and control technologies for railway systems
o protective functions
o ensure the sequence/spacing of trains
o ensure route protection
o ensure level crossing protection
o technical realization for protective functions
o European Train Control System
operational command/control systems
o operational control systems
o concepts of optimization
RAMS for railway systems
o accident investigation methods
o RAMS standards for railways
o risk analysis and hazard control
o RAMS methods
o design principles for availability and safety
o maintenance strategies
o Life Cycle Costs (LCC)
o Human Factor
o safety in long railway tunnels
tutorials in Railway Operation Laboratory
field trip to Siemens Wallisellen (command and control technologies)
We will cover a range of topics, including:
- Earthquake basics: definitions, faults, elastic rebound theory, and source parameters.
- Introduction to elastodynamics: strain, stress, equation of motion.
- Mathematical description of the source:
- Representation theorem, point and extended sources, source spectra.
- Energy partitioning
- Source dynamics: Linear Elastic Fracture Mechanics
- Fault mechanics and friction
- Seismic cycle: inter-, co-, and post-seismic processes
- Aseismic creep and slow slip transients
- Earthquake source inversion and data assimilation
- Recurrence models
- Modeling of dynamic ruptures and seismic cycles
After a theoretical understanding has been acquired, we invite students to apply this knowledge to their topic of preference by presenting a group of state-of-the-art and/or classical papers as a final project. This will require them to understand and evaluate current challenges and state-of-the-art practices in earthquake physics. Additionally, this stimulates participants to improve their skills to:
- critically analyze (to be) published papers
- disseminate knowledge within their own and neighboring research fields
- formulate their opinion, new ideas and broader implications
- present their findings to an audience
- ask questions and actively participate in discussions on new scientific ideas
Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models.
Objective: Enabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour.
Content: Behavioral model and measurement; travel diary, design process, hypothetical markets, discrete choice model, parameter estimation, pattern of travel behaviour, market segments, simulation, advanced discrete choice models.
This course provides an introduction to Information Security. The focus is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
Objective: Master fundamental concepts in Information Security and their application to system building. (See objectives listed below for more details).
Content: 1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design, Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobilityD-INFK: 252-0211-00L
This is a theoretical course on the economics of financial decision making, at the crossroads between Microeconomics and Finance. It discusses portfolio choice theory, risk sharing, market equilibrium and asset pricing.
Objective: The objective is to make students familiar with the economics of financial decision making and develop their intuition regarding the determination of asset prices, the notions of optimal risk sharing. However this is not a practical formation for traders. Moreover, the lecture doesn't cover topics such as market irrationality or systemic risk.
Content: The following topics will be discussed:
Introduction to finance and investment planning; Option valuation; Arbitrage; Choice under uncertainty; Portfolio Choice; Risk sharing and insurance; Market equilibrium under symmetric information.
1) "Investments", by Z. Bodie, A. Kane and A. Marcus, for the
introductory part of the course (see chapters 20 and 21 in
2) "Finance and the Economics of Uncertainty" by G. Demange and G. Laroque, Blackwell, 2006.
3) "The Economics of Risk and Time", by C. Gollier, and
- "Intermediate Financial Theory" by J.-P. Danthine and J.B. Donaldson.
- Ingersoll, J., E., Theory of Financial Decision Making, Rowman and Littlefield Publishers.
- Leroy S and J. Werner, Principles of Financial Economics, Cambridge University Press, 2001
Basic mathematical skills needed (calculus, linear algebra, convex analysis). Students must be able to solve simple optimization problems (e.g. Lagrangian methods). Some knowledge in microeconomics would help but is not compulsory. The bases will be covered in class.
The aim of this course is to present a concise overview of mathematical methods from the areas of probability and statistics that can be used by financial institutions to model market, credit and operational risk. Topics addressed include loss distributions, multivariate models, dependence and copulas, extreme value theory, risk measures, risk aggregation and risk allocation.
Objective: The aim of this course is to present a concise overview of mathematical methods from the areas of probability and statistics that can be used by financial institutions to model market, credit and operational risk.
1. Risk in Perspective
2. Basic Concepts
3. Multivariate Models
4. Copulas and Dependence
5. Aggregate Risk
6. Extreme Value Theory
7. Operational Risk and Insurance Analytics
The course material (pdf-slides and further reading material) are available at
in the section "Course material" (the username and password have been sent by email).
The textbook listed under "Literatur" below makes ideal background reading.
Quantitative Risk Management: Concepts, Techniques and Tools
AJ McNeil, R Frey and P Embrechts
Princeton University Press, Princeton, 2015 (Revised Edition)
(For this course the 2005 first edition also suffices)
The course corresponds to the Risk Management requirement for the SAA ("Aktuar SAV Ausbildung") as well as for the Master of Science UZH-ETH in Quantitative Finance.
This course yields an introduction into the one-dimensional theory of extremes, and this both from a probabilistic as well as statistical point of view. This course can be seen as a first course on extremes, a sequel concentrating more on multivariate extremes.
Objective: In this course, students learn to distinguish between so-called normal models, i.e. models based on the normal or Gaussian distribution, and so-called heavy-tailed or power-tail models.
They learn to do some standard modelling and data analysis for one-dimensional data. The probabilistic key theorems are the Fisher-Tippett Theorem and the Balkema-de Haan-Pickands Theorem. These lead to the statistical techniques for the analysis of extremes or rare events known as the Block Method, and Peaks Over Threshold method, respectively.
- Introduction to rare or extreme events
- Regular Variation
- The Convergence to Types Theorem
- The Fisher-Tippett Theorem
- The Method of Block Maxima
- The Maximal Domain of Attraction
- The Fre'chet, Gumbel and Weibull distributions
- The POT method
- The Point Process Method: a first introduction
- The Pickands-Balkema-de Haan Theorem and its applications
- Some extensions and outlook
Institutions and in particular political institutions are a central determinant of economic performance. In this course, we learn the characteristics of collective decision making and political processes as well as the theoretical tools in institutional design. At the end of the course we will discuss recent research in political economics.Design of Institutions and Policy
Objective: In this doctoral course, we learn the theoretical tools and major results in collective decision theory and political economics. We will use this knowledge to discuss recent research in political economics. The course enables the participants to do their own research in political economics or apply the frameworks to interesting institutional design problems in their own research area.
Part I: Theoretical Tools and Important Results (lectures)
1. Collective Decision Making and Impossibility Results
2. Voting Models
4. Creating Institutions: A Mechanism Design Perspective
5. Dynamic Political Economy
Part II: Recent Research in Political Economics (presentations)
In the first part, the theory is presented in lectures. In the second part, each participant will present a paper of her/his interest from the syllabus (provided in the first class meeting) and has to write a referee report (of max. 3 pages) on it.
The specialized PhD seminar aims at three-fold integration: 1)bringing modeling and computer simulation of techno-socio-economic processes and phenomena together with related empirical, experimental, and data-driven work, 2)combining perspectives of different disciplines (e.g. sociology, computer science, physics, complexity science, engineering), 3)bridging between fundamental and applied work.
Objective: Participants of the seminar should understand how tightly connected systems lead to networked risks, and why this can imply systems we do not understand and cannot control well, thereby causing systemic risks and extreme events.
They should also be able to explain how systemic instabilities can be understood by changing the perspective from a component-oriented to an interaction- and network-oriented view, and what fundamental implications this has for the proper design and management of complex dynamical systems.
Computational Social Science and Global Systems Science serve to better understand the emerging digital society with its close co-evolution of information and communication technology (ICT) and society. They make current theories of crises and disasters applicable to the solution of global-scale problems, taking a data-based approach that builds on a serious collaboration between the natural, engineering, and social sciences, i.e. an interdisciplinary integration of knowledge.
The course covers economics of risk management and insurance. Main topics are risk measures and risk management methods, supply and demand of insurance, asymmetric information in insurance markets and insurance regulation.
Objective: The goal is to introduce students to basic concepts of risk, risk management and economics of insurance.
- what is the rationale for risk management?
- measures of risk and methods of risk management
- demand and supply of insurance
- information problems in insurance markets: moral hazard, adverse selection, fraud
- insurance regulation
- Ray Rees and Achim Wambach (2008), The Microeconomics of Insurance, Foundations and Trends in Microeconomics: Vol. 4: No 1-2.
- Eeckhoudt/Gollier/Schlesinger (2007), Economic and Financial Decisions under Risk, Princeton University Press.
- introductory background reading: Harrington/Niehaus (2003), Risk Management and Insurance, McGraw Hill.
The course provides advanced tools for the risk/vulnerability analysis and engineering of complex technical systems and critical infrastructures. It covers application of modeling techniques and design management concepts for strengthening the performance and robustness of such systems, with reference to energy, communication and transportation systems.
Objective: Students will be able to model complex technical systems and critical infrastructures including their dependencies and interdependencies. They will learn how to select and apply appropriate numerical techniques to quantify the technical risk and vulnerability in different contexts (Monte Carlo simulation, Markov chains, complex network theory). Students will be able to evaluate which method for quantification and propagation of the uncertainty of the vulnerability is more appropriate for various complex technical systems. At the end of the course, they will be able to propose design improvements and protection/mitigation strategies to reduce risks and vulnerabilities of these systems.
Content: Modern technical systems and critical infrastructures are complex, highly integrated and interdependent. Examples of these are highly integrated energy supply, energy supply with high penetrations of renewable energy sources, communication, transport, and other physically networked critical infrastructures that provide vital social services. As a result, standard risk-assessment tools are insufficient in evaluating the levels of vulnerability, reliability, and risk.
This course offers suitable analytical models and computational methods to tackle this issue with scientific accuracy. Students will develop competencies which are typically requested for the formation of experts in reliability design, safety and protection of complex technical systems and critical infrastructures.
Specific topics include:
- Introduction to complex technical systems and critical infrastructures
- Basics of the Markov approach to system modeling for reliability and availability analysis
- Monte Carlo simulation for reliability and availability analysis
- Markov Chain Monte Carlo for applications to reliability and availability analysis
- Dependent, common cause and cascading failures
- Complex network theory for the vulnerability analysis of complex technical systems and critical infrastructures
- Basic concepts of uncertainty and sensitivity analysis in support to the analysis of the reliability and risk of complex systems under incomplete knowledge of their behavior
Practical exercitations and computational problems will be carried out and solved both during classroom tutorials and as homework.
The class will be largely based on the books:
- "Computational Methods For Reliability And Risk Analysis" by E. Zio, World Scientific Publishing Company
- "Vulnerable Systems" by W. Kröger and E. Zio, Springer
- additional recommendations for text books will be covered in the class
Agent-based modelling is introduced as a bottom-up approach to understand the dynamics of complex social systems. The course focuses on agents as the fundamental constituents of a system and their theoretical formalisation and on quantitative analysis of a wide range of social phenomena-cooperation and competition, opinion dynamics, spatial interactions and behaviour in online social networks.
Objective: A successful participant of this course is able to
- understand the rationale of agent-centered models of social systems
- understand the relation between rules implemented at the individual level and the emerging behaviour at the global level
- learn to choose appropriate model classes to characterise different social systems
- grasp the influence of agent heterogeneity on the model output
- efficiently implement agent-based models using Python and visualise the output
Content: Agent-based modelling (ABM) provides a bottom-up approach to understand the complex dynamics of social systems. In ABM, agents are the basic constituents of any social system. Depending on the granularity of the analysis, an agent could represent a single individual, a household, a firm, a country, etc. Agents have internal states or degrees of freedom opinions, strategies, etc.), the ability to perceive and change their environment, and the ability to interact with other agents. Their individual (microscopic) actions and interactions with other agents, result in macroscopic (collective, system) dynamics with emergent properties. As more and more accurate individual-level data about online and offline social systems become available, our formal, quantitative understanding of the collective dynamics of these systems needs to progress in the same manner.
We focus on a minimalistic description of the agents' behaviour which relates individual interaction rules to the dynamics on the collective level and complements engineering and machine learning approaches.
The course is structured in three main parts. The first two parts introduce two main agent concepts - Boolean agents and Brownian agents, which differ in how the internal dynamics of agents is represented. Boolean agents are characterized by binary internal states, e.g. yes/no opinion, while Brownian agents can have a continuous spectrum of internal states, e.g. preferences and attitudes. The last part introduces models in which agents interact in physical space, e.g. migrate or move collectively.
Throughout the course, we will discuss a wide variety of application areas, such as:
- opinion dynamics and social influence,
- cooperation and competition,
- online social networks,
- systemic risk
- emotional influence and communication
- swarming behavior
- spatial competition
While the lectures focus on the theoretical foundations of agent-based modelling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agent-based models in guided and autonomous projects, which they present and jointly discuss.
Participants of the course should have some background in mathematics and an interest in formal modelling and computer simulations, and should be motivated to learn about social systems from a quantitative perspective.
Prior knowledge of Python is not necessary.
Self-study tasks are provided as home work for small teams (2-4 members).
Weekly exercises (45 min) are used to discuss the solutions and guide the student.
During the second half of the semester, teams need to complete a course project in which they will implement and discuss an agent-based model to characterise a system chosen jointly with the course organisers. This project will be evaluated, and its grade will count as 25% of the final grade.
The course provides an overview of the methods and abstractions used in (i) the quantitative study of complex networks, (ii) empirical network analysis, (iii) the study of dynamical processes in networked systems, (iv) the analysis of robustness of networked systems, (v) the study of network evolution, and (vi) data mining techniques for networked data sets.
* the network approach to complex systems, where actors are represented as nodes and interactions are represented as links
* learn about structural properties of classes of networks
* learn about feedback mechanism in the formation of networks
* learn about statistical inference and data mining techniques for data on networked systems
* learn methods and abstractions used in the growing literature on complex networks
Content: Networks matter! This holds for social and economic systems, for technical infrastructures as well as for information systems. Increasingly, these networked systems are outside the control of a centralized authority but rather evolve in a distributed and self-organized way. How can we understand their evolution and what are the local processes that shape their global features? How does their topology influence dynamical processes like diffusion? And how can we characterize the importance and/or role of specific nodes?
This course provides a systematic answer to such questions, by developing methods and tools which can be applied to networks in diverse areas like infrastructure, communication, information systems, biology or (online) social networks. In a network approach, agents in such systems (like e.g. humans, computers, documents, power plants, biological or financial entities) are represented as nodes, whereas their interactions are represented as links.
The first part of the course, "Introduction to networks: basic and advanced metrics", describes how networks can be represented mathematically and how the properties of their link structures can be quantified empirically.
In a second part "Stochastic Models of Complex Networks" we address how analytical statements about crucial properties like connectedness or robustness can be made based on simple macroscopic stochastic models without knowing the details of a topology.
In the third part we address "Dynamical processes on complex networks". We show how a simple model for a random walk in networks can give insights into the authority of nodes, the efficiency of diffusion processes as well as the existence of community structures.
A fourth part "Statistical Physics of Networks: Optimisation and Inference" introduces models for the emergence of complex topological features which are due to stochastic optimization processes, as well as algorithmic approaches to automatically infer knowledge about structures and patterns from network data sets.
In a fifth part, we address "Network Dynamics", introducing models for the emergence of complex features that are due to (i) feedback phenomena in simple network growth processes or (iii) order correlations in systems with highly dynamic links.
A final part "Research Trends" introduces recent research on the application of data mining and machine learning techniques to relational data, as well as current trends in the study of multi-layer complex networks.
There are no pre-requisites for this course. Self-study tasks (to be solved analytically and by means of computer simulations) are provided as home work. Weekly exercises (45 min) are used to discuss selected solutions. Active participation in the exercises is strongly suggested for a successful completion of the final exam.
The following topics are covered: 1) origin and quantification of earthquake hazard; 2) seismic response of elastic and inelastic structures; 3) response history and response spectrum evaluation methods; 4) basis for seismic design codes; and 5) fundamentals of seismic design of structures. These topics are discussed in framework of performance-based seismic design.
Objective: After successfully completing this course the students will be able to:
1. Explain the nature of earthquake hazard and risk.
2. Explain the seismic response of simple linear and nonlinear single- and multi-degree-of-freedom structural systems and quantify it using response time history and response spectrum approaches.
3. Apply design code provisions to size the structural elements in a lateral force resisting system of a typical frame building.
Content: This course initiates the series of two courses on seismic design of structures at ETHZ. Building on the material covered in the course on Structural Dynamics and Vibration Problems, the following fundamental topics are covered in this course: 1) origin and quantification of earthquake hazard; 2) seismic response of elastic and inelastic single- and multiple-degree-of-freedom structures; 3) response history and response spectrum seismic response evaluation methods; 4) basis for seismic design codes; and 5) fundamentals of seismic design of structures. These topics are discussed in framework of performance-based seismic design.
1. Dynamics of Structures: Theory and Applications to Earthquake Engineering, 4th edition, Anil Chopra, Prentice Hall, 2012
2. Earthquake Engineering: From Engineering Seismology to Performance-Based Engineering, Yousef Borzorgnia and Vitelmo Bertero, Eds., CRC Press, 2004
3. Erdbebensicherung von Bauwerken, 2nd edition, Hugo Bachmann, Birkhäuser, Basel, 2002
ETH Structural Dynamics and Vibration Problems course, or equivalent. Students are expected to be able to compute the response of elastic single- and multiple-degree-of-freedom structural systems in free vibration, as well as in forced vibration under harmonic and pulse excitation, to use the response spectrum method and to understand and be able to apply the modal response analysis method for multiple-degree-of-freedom structures. Knowledge of structural analysis and design of reinforced concrete or steel structures under static loads is expected. Familiarity with general-purpose numerical analysis software, such as Matlab, and structural analysis software, such as SAP2000, is desirable.
Uncertainty quantification aims at studying the impact of aleatory - (e.g. natural variability) or epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data, uncertainty propagation techniques (polynomial chaos expansions), and sensitivity analysis.
Objective: After this course students will be able to properly define an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. Although the course is primarily intended to civil, mechanical and electrical engineers, it is suitable to any master student with a basic knowledge in probability theory.
Content: The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab.
A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course.
A good knowledge of Matlab is required to participate in the tutorials and work out assignments.
Fundamentals of railroad technology and interactions between track and vehicles, network development and infrastructure planning, planning of rail infrastructure, planning and design of railway stations, construction and dimensioning of tracks, approval and beginning service on complex infrastructure facilities, special issues of maintenance.
Objective: Teaches the basic principles of public transport network and topology design, geometrical design, dimensioning and construction as well as the maintenance of rail infrastructures. Teaches students to recognize the interactions between the infrastructure design and the production processes. Provides the background for Masters degree study.
Content: (1) Fundamentals: Infrastructures of public transport systems; interaction between track and vehicles; passengers and goods as infrastructure users; management and financing of networks; railway standards and normes. (2) Infrastructure planning: Planning processes and decision levels in network development and infrastructure planning, planning of railway tracks and rail topologies; planning of the passenger parts of stations. (3) Infrastructure design: Fundamentals of the layout of a line; track geometry; switchs and crossings; design of station platforms. (4) Construction of railway infrastructures: Assembly and evolution of the railway track; elements of the railway track; dimensioning of the track; track stability. (5) Approval and beginning service on complex infrastructure facilities: Definitions and limitations; fundamentals of the legal situation; test and approval processes; processes of putting railway systems into operation. (6) Maintenance of railway infrastructures: Fundamentals of infrastructure maintenance; kinds of depreviations; supervision methods; steps of infrastructure maintenance; estimation of maintenance need; methods to minimize maintenance costs.
Course notes will be provided in German. Slides are made available some days before each lecture. The relevant literature for self-studies are announced