Lecture

Nonlinear Analysis of Structures

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In course

CIVIL-449: Nonlinear analysis of structures

This course deals with nonlinear modelling of structures under mechanical loading. ...

Description

This lecture covers the nonlinear analysis of structures, focusing on time-stepping methods like the Newmark Method. It discusses selecting time steps, stability conditions, and the use of damping matrices. The instructor explains modeling strategies, benefits of nonlinear analysis, and presents a case study of a 4-story steel building. The lecture concludes with personal remarks on model complexity and performance objectives.

Instructors (3)

Katrin Beyer

Since 2017 Associate Professor, School of Architecture, Civil and Environmental Engineering (ENAC), EPFL. Head of the Earthquake Engineering and Structural Dynamics (EESD) Laboratory 2010-2017 Assistant Professor, EPFL. 2008-2010 Post-doctoral researcher, ETH Zürich. 2003-2007 Ph.D., Roseschool / Università di Pavia, Italy. 2001-2003 Ove Arup & Partners, Advanced Technology and Research Group, London. 2001 Diploma, Civil engineering, ETH Zürich.

Dimitrios Lignos

Prof. Lignos joined the École Polytechnique Féderale de Lausanne (EPFL) in 2016 from McGill University in Canada where he was a tenured Associate Professor and a William Dawson Scholar for Infrastructure Resilience. He holds a diploma (National Technical University of Athens, NTUA, 2003), M.S. (Stanford University, 2004) and Ph.D. (Stanford University, 2008). In addition, he was a post-doctoral scientist at Stanford University (2009) and in Kyoto University (2010). Prof. Lignos teaches graduate and undergraduate courses in seismic design, nonlinear behaviour of steel and composite structures as well as supplemental damping systems, Structural Stability, Nonlinear Analysis and Performance-based Earthquake Engineering. His awards for teaching, research and service in Civil Engineering include the 2011 Outstanding Teaching Award (Faculty of Engineering, McGill University), as well as the Outstanding reviewer (2012, 2013) award from ASCE, the 2013 State-of-the-Art in Civil Engineering Award by ASCE and the 2014 Christophe Pierre Award for Research Excellence - Early Career. Just recently, he received the 2019 Walter L. Huber Civil Engineering Research Prize from ASCE for significant contributions in developing state of the art methods to simulate extreme limit states in steel structures.Prof. Lignos is a member of ASCE and the Earthquake Engineering Research Institute. He acts as an Associate Editor for Metal Structures and Seismic Effects of the ASCE Journal of Structural Engineering. He joined the Editorial Board of Earthquake Spectra and Earthquake Engineering and Structural Dynamics International journals. He serves as an acting member of the CEN/TC 250/SC 8/WG 2 and has been selected as a member of the Project Team (PT2) for the Eurocode 8-Part 1 Current Revisions for Steel and Composite Structures. He is also a member of the Canadian Standards Association (CSA) S16 technical committee for Steel Structures. Prof. Lignos is involved as a NEHRP consultant in numerous research-to-practice projects related to the behaviour and nonlinear modelling and analysis of structures applicable to the engineering practice through the Applied Technology Council (ATC). Detailed Curriculum Vitae (last update September 2018)

Official source

Related concepts (32)

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

Euler method

In mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who first proposed it in his book Institutionum calculi integralis (published 1768–1870).

Explicit and implicit methods

Explicit and implicit methods are approaches used in numerical analysis for obtaining numerical approximations to the solutions of time-dependent ordinary and partial differential equations, as is required in computer simulations of physical processes. Explicit methods calculate the state of a system at a later time from the state of the system at the current time, while implicit methods find a solution by solving an equation involving both the current state of the system and the later one.

Complexity class

In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.

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